import os os.environ["CUDA_VISIBLE_DEVICES"] = "0" import tensorflow as tf gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: try: # Currently, memory growth needs to be the same across GPUs for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) logical_gpus = tf.config.experimental.list_logical_devices('GPU') print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs") except RuntimeError as e: # Memory growth must be set before GPUs have been initialized print(e) import time import argparse import wandb import random import numpy as np import scipy as sp from custom_utils.custom_utils import UV2Mesh, maskFacesBatch, _maskFaces from tensorboardX import SummaryWriter import plotly.graph_objects as go import plotly.express as ex from PIL import Image 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 losses import d_logistic, d_logistic_r1_reg, g_logistic_non_saturating, g_logistic_ns_pathreg, edge_loss, uv_mask_loss, bend_loss # from losses import collision_penalty as collision_penalty from losses import collision_loss as collision_penalty from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block from stylegan2_ae.generator import Generator from custom_utils.graph_utils import Reconstructor_V4_Decoder as GNNDecoder, sparse_to_tensor from custom_utils.graph_utils import Reconstructor_v2 as SimpleReconstructor from dataset import DatasetFactory, StaticDataset from custom_utils.custom_utils import batch_to_dict from custom_utils.smpl import SMPL from auto_encoder_body import AutoEncoder as BodyAE from auto_encoder_curv import AutoEncoder as CurvatureAE from auto_encoder_static import AutoEncoder as StaticAE from cvae_curv import CVAE as CVAECurv from models.cvae_model import CVAE as CVAEMain name='cvae_12_6' def init_wandb(): run = wandb.init(project='vae', name=name, notes='learn offsets. smaller flattened latent vectors. with 25-85k pretraining. smaller batchsize. fixed balancing of reconst and kl loss for baseline. \ new implementation for kl loss. no lr schedule. 25 jumpsuits, average reconst_loss before calculation, linearly increasing balancing, up to 1 from 0.1. \ offsets over average unposed garment. remove global orientation (in posed body input and in pose_shape parameters. \ retrained body encoder with deoriented bodies \ use mean l2 loss. small batch. l2 * 1000, balancing to 1 from 0.1') artifact = wandb.Artifact('code', type='code') artifact.add_file('cvae.py') artifact.add_file('dataset.py') artifact.add_file('losses.py') artifact.add_file('convnext/models/convnext_tf.py') run.log_artifact(artifact) uv2mesh = UV2Mesh(res=256) def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, collision_color): mesh_pred_unposed[:,2] += 1 collision[:,2] += 2 fig = go.Figure(data=[ go.Mesh3d( # 8 vertices of a cube x=mesh_gt[:,0], y=mesh_gt[:,2], z=mesh_gt[:,1], # i, j and k give the vertices of triangles i = faces[:,0], j = faces[:,1], k = faces[:,2] ), go.Mesh3d( # 8 vertices of a cube x=mesh_pred_unposed[:,0], y=mesh_pred_unposed[:,2], z=mesh_pred_unposed[:,1], # i, j and k give the vertices of triangles i = faces[:,0], j = faces[:,1], k = faces[:,2], ), go.Mesh3d( # 8 vertices of a cube x=collision[:,0], y=collision[:,2], z=collision[:,1], # i, j and k give the vertices of triangles i = collision_faces[:,0], j = collision_faces[:,1], k = collision_faces[:,2], vertexcolor=collision_color ) ]) fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=2, z=1)) fig.update_layout( scene = dict( xaxis = dict(nticks=4, range=[-1,1],), yaxis = dict(nticks=4, range=[-1,3],), zaxis = dict(nticks=4, range=[-1,1],),) ) return fig @tf.function def train_step(batch): with tf.GradientTape() as tape: uv_curvature = tf.one_hot(batch['uv_curvature'], 7) pred, mean, logvar, z = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], uv_curvature, batch['uv_mask'], batch['uv_shaped_body']]) def plot_everything(batch: dict, type: str): uv_curvature = tf.one_hot(batch['uv_curvature'], 7) pred, mean, logvar, z = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], uv_curvature, batch['uv_mask'], batch['uv_shaped_body']]) mesh_pred = uv2mesh(tf.transpose(pred, [0,3,1,2])) mesh_orig = batch['mesh_unposed'] orig_img = tf.transpose(batch['uv_unposed'], [0,2,3,1]) uv_static_img = tf.transpose(batch['uv_static'], [0,2,3,1]) uv_body_posed_img = tf.transpose(batch['uv_body_posed'], [0,2,3,1]) pred_img = pred img = tf.concat((uv_static_img, uv_body_posed_img, orig_img, pred_img), axis=2) imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)] F = np.load("custom_utils/smpl_faces.npy") mask = batch['mesh_mask'].numpy().astype(bool) garment_unposed, faces = maskFacesBatch(mesh_pred.numpy(), F, mask) gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred.shape[0])] body = batch['mesh_body_posed'].numpy() mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred).numpy() garment_posed = [mesh_posed[i, mask[i], :] for i in range(body.shape[0])] collision = [np.concatenate((body[i], garment_posed[i]), axis=0) for i in range(len(garment_posed))] collision_faces = [np.concatenate((F, faces[i] + 14475), axis=0) for i in range(len(garment_posed))] collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(garment_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(garment_posed))] figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) } mean_hist = wandb.Histogram(sequence=mean.numpy().mean(axis=0), num_bins=model.latent_dim) logvar_hist = wandb.Histogram(sequence=logvar.numpy().mean(axis=0), num_bins=model.latent_dim) return { **figs, **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } } def load_model(model, checkpoint): ckpt = tf.train.Checkpoint(model=model) manager = tf.train.CheckpointManager(ckpt, checkpoint, max_to_keep=1) ckpt.restore(manager.latest_checkpoint) def sample(folder_main, folder_curv): # run = wandb.init(project='vae_plots', name=folder.split('/')[-1]) load_model(model, folder_main) load_model(curv_model, folder_curv) nr_of_samples = 2 count = 0 for batch in dataset_train: batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic)) pred = curv_model.sample_single(batch['uv_static'][0], batch['uv_body_posed'][0], batch['uv_mask'][0], nr_of_samples=nr_of_samples) uv_curvature = tf.math.argmax(pred, axis=-1) uv_curvature = tf.one_hot(uv_curvature, 7) pred = model.sample_single(batch['uv_static'][0], batch['uv_body_posed'][0], uv_curvature, batch['uv_mask'][0], nr_of_samples=nr_of_samples, fix_noise=True) pose, shape = batch['pose_shape'][0, :72], batch['pose_shape'][0, 72:] pose, shape = tf.tile(pose[tf.newaxis,:], [nr_of_samples,1]), tf.tile(shape[tf.newaxis,:], [nr_of_samples,1]) mesh_pred = uv2mesh(tf.transpose(pred, [0,3,1,2])) mesh_posed = smpl.run_garment(pose, shape, mesh_pred).numpy() mesh_orig = batch['mesh_posed'][0].numpy() mesh_orig_reposed = smpl.run_garment(pose[:1], shape[:1], batch['mesh_unposed'][:1]).numpy()[0] body = batch['mesh_body_posed'][0].numpy() body_color = np.ones_like(body, dtype=np.int32)*100 F = np.load("custom_utils/smpl_faces.npy") mask = batch['mesh_mask'].numpy().astype(bool)[0] _, faces = _maskFaces(np.ones((14475, 3)), F, mask) meshes = [] meshes.append(go.Mesh3d( # 8 vertices of a cube x=mesh_orig_reposed[mask,0], y=mesh_orig_reposed[mask,2], z=mesh_orig_reposed[mask,1], # i, j and k give the vertices of triangles i = faces[:,0], j = faces[:,1], k = faces[:,2] )) for i in range(nr_of_samples): x = mesh_posed[i][mask, :].copy() # x[:, 2] += 1 * (i + 1) x[:, 2] += 0.5 meshes.append(go.Mesh3d( # 8 vertices of a cube x=x[:,0], y=x[:,2], z=x[:,1], # i, j and k give the vertices of triangles i = faces[:,0], j = faces[:,1], k = faces[:,2] )) # for i in range(5): # x = body.copy() # x[:, 2] += 1 * i # meshes.append(go.Mesh3d( # # 8 vertices of a cube # x=x[:,0], # y=x[:,2], # z=x[:,1], # # i, j and k give the vertices of triangles # i = F[:,0], # j = F[:,1], # k = F[:,2], # vertexcolor=body_color # )) fig = go.Figure(data=meshes) # fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=nr_of_samples-1, z=1)) fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1)) fig.update_layout( scene = dict( xaxis = dict(nticks=4, range=[-1,1],), # yaxis = dict(nticks=4, range=[-1,nr_of_samples+1],), yaxis = dict(nticks=4, range=[-1,1],), zaxis = dict(nticks=4, range=[-1,1],),) ) # wandb.log({'plot_' + str(count): fig}) fig.show() uv_curvature_all = np.concatenate(uv_curvature.numpy().argmax(-1), axis=1) ex.imshow(uv_curvature_all).show() # frames = [Image.fromarray(pred[i].numpy()/6*255) for i in range(pred.shape[0])] # frames[0].save("temp/curv_4.gif", format="GIF", append_images=frames[1:], # save_all=True, duration=500, loop=0, optimize=False) if count > 10: break print(count) count += 1 print() if __name__ == '__main__': # init_wandb() # allow_memory_growth() parser = argparse.ArgumentParser(description='') parser.add_argument('--name', type=str) args = vars(parser.parse_args()) batch_size = 4 smpl = SMPL() factory = DatasetFactory() signature_dynamic = { 'id': (), 'pose_shape': (72 + 11,), # 'uv_posed': (3, 512, 256,), 'uv_body_posed': (3, 512, 256,), 'mesh_body_posed': (14475, 3,), 'mesh_posed': (14475, 3,), 'mesh_unposed': (14475, 3,), 'uv_unposed': (3, 512, 256,), 'uv_curvature': (512, 256), } signature_static = { 'uv_static': (3, 512, 256,), # 'mesh_static': (14475, 3,), 'mesh_mask': (14475,), 'uv_mask': (512, 256,), 'uv_shaped_body': (3, 512, 256,), } dataset_train = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/train', batch_size=batch_size, signature=signature_dynamic) static_dataset_train = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/train', signature=signature_static) dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/valid', batch_size=batch_size, signature=signature_dynamic) static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid', signature=signature_static) model = CVAEMain(avg_garment_path='custom_data/jumpsuit_20_avg_uv.npy', latent_dim=128, static_model='checkpoints/ae_static_3', body_model='checpoints/ae_body_6', curvature_model='checpoints/ae_curvature_6') curv_model = CVAECurv() # lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.0001, decay_steps=30000, decay_rate=0.1, staircase=False) optimizer = tf.optimizers.Adam(learning_rate=0.0001) balancing_coeff = 0.1 reg_coeff = 1 folder = 'models/ae_logs/' valid_iterator = iter(dataset_valid) gamma = 1 balancing_activated_step = 0 avg_jumpsuit = np.load('custom_data/jumpsuit_20_avg_uv.npy') sample('checkpoints/cvae_13', 'checkpoints/cvae_curv_2') # ckpt = tf.train.Checkpoint(model=model) # manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + name, max_to_keep=3) # step = 0 # for batch in dataset_train: # batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic))