276 lines
10 KiB
Python
276 lines
10 KiB
Python
from lib2to3.refactor import get_fixers_from_package
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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_model 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='plots', name=name,
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notes='')
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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 get_frame(folder, nr):
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pose_folder = os.path.join(folder, 'pose_shape')
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frames = sorted(os.listdir(pose_folder))
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frames = [frame.split('.')[0] for frame in frames] # .split('_')[0]
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random.shuffle(frames)
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return frames[0], frames[1:nr]
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def get_bodies(folder, frame_main, frame_rand, nr=4, random=True):
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bodies = []
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uv_posed_body_no_orient = np.load(os.path.join(folder, 'uv_posed_body_no_orient', frame_main + '.npz')); uv_posed_body_no_orient = uv_posed_body_no_orient['uv_posed_body_no_orient']; bodies.append(uv_posed_body_no_orient)
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pass
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def get_curvs(folder, body, static, mask, frame=None, nr=4, random=True, model=None):
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assert (random and model is not None) or (not random and model is None)
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if random:
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curv = model([body, static, mask])
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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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pass
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else:
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curv = np.load(os.path.join(folder, 'uv_curvature')) #...
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pass
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def get_statics(folder, nr=4, random=True):
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pass
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def get_unposed(folder, nr=4, random=True):
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pass
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def plot_inputs(unposed, static, body, curv):
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pass
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def plot_variable_condition(condition, is_curv=False):
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pass
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def plot_results(unposed, posed, body, mesh_mask):
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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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_, faces = _maskFaces(np.ones((14475, 3)), F, mesh_mask)
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pass
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def sample(folder, variable_condition, fix_noise, nr=4):
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# run = wandb.init(project='vae_plots', name=)
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random_static, random_body, random_curv, random_unposed = variable_condition == 'static', variable_condition == 'body', variable_condition == 'curv', variable_condition == 'unposed'
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frame_main, frame_rand = get_frame(folder, nr)
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unposed, uv_mask, mesh_mask = get_unposed(folder, frame, nr, random_unposed)
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static = get_statics(folder, nr, random_static)
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body, pose, shape = get_bodies(folder, nr, random_body)
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curv = get_curvs(folder, body, static, uv_mask, frame, nr, random_curv, curv_model)
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pred = model([unposed, static, body, curv, uv_mask], fix_noise=fix_noise)
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mesh_pred = uv2mesh(pred)
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gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
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shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
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mesh_pred_shaped = mesh_pred + shape_offsets
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mesh_posed = smpl.run_garment(pose, shape, mesh_pred_shaped).numpy()
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plot_inputs(unposed[0], static[0], body[0], curv[0])
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variable_cond = None
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if random_static:
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variable_cond = static
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elif random_body:
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variable_cond = body
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elif random_curv:
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variable_cond = curv
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elif random_unposed:
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variable_cond = unposed
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else:
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assert False
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plot_variable_condition(variable_cond, is_curv=random_curv)
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plot_results(mesh_pred, mesh_posed, body, mesh_mask)
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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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folder = '/home/workspace/local-data/train_100'
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folder_main = 'checkpoints/cvae_13'
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folder_curv = 'checkpoints/cvae_curv_2'
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variable_condition = 'curv'
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batch_size = 4
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smpl = SMPL()
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model = CVAEMain(avg_garment_path='custom_data/train_100_no_shape_avg_uv.npy',
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latent_dim=128, static_model='checkpoints/ae_static_4',
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body_model='checpoints/ae_body_8',
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curvature_model='checpoints/ae_curvature_7')
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curv_model = CVAECurv()
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load_model(model, folder_main)
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load_model(curv_model, folder_curv)
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sample(folder, variable_condition, fix_noise=True)
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