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