From 2572ae7788b131ca179fb37a344c14211fbacecc Mon Sep 17 00:00:00 2001 From: Hunor Laczko Date: Tue, 20 Sep 2022 23:00:43 +0000 Subject: [PATCH] base of pro sampling --- sample_pro.py | 275 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 275 insertions(+) create mode 100644 sample_pro.py diff --git a/sample_pro.py b/sample_pro.py new file mode 100644 index 0000000..b91f9ce --- /dev/null +++ b/sample_pro.py @@ -0,0 +1,275 @@ +from lib2to3.refactor import get_fixers_from_package +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='plots', name=name, + notes='') + 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 get_frame(folder, nr): + pose_folder = os.path.join(folder, 'pose_shape') + frames = sorted(os.listdir(pose_folder)) + frames = [frame.split('.')[0] for frame in frames] # .split('_')[0] + random.shuffle(frames) + return frames[0], frames[1:nr] + +def get_bodies(folder, frame_main, frame_rand, nr=4, random=True): + bodies = [] + 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) + pass + +def get_curvs(folder, body, static, mask, frame=None, nr=4, random=True, model=None): + assert (random and model is not None) or (not random and model is None) + if random: + curv = model([body, static, mask]) + + + uv_curvature = tf.math.argmax(pred, axis=-1) + uv_curvature = tf.one_hot(uv_curvature, 7) + pass + else: + curv = np.load(os.path.join(folder, 'uv_curvature')) #... + pass + +def get_statics(folder, nr=4, random=True): + pass + +def get_unposed(folder, nr=4, random=True): + pass + +def plot_inputs(unposed, static, body, curv): + pass + +def plot_variable_condition(condition, is_curv=False): + pass + +def plot_results(unposed, posed, body, mesh_mask): + body_color = np.ones_like(body, dtype=np.int32)*100 + + F = np.load("custom_utils/smpl_faces.npy") + _, faces = _maskFaces(np.ones((14475, 3)), F, mesh_mask) + pass + + +def sample(folder, variable_condition, fix_noise, nr=4): + # run = wandb.init(project='vae_plots', name=) + + random_static, random_body, random_curv, random_unposed = variable_condition == 'static', variable_condition == 'body', variable_condition == 'curv', variable_condition == 'unposed' + + frame_main, frame_rand = get_frame(folder, nr) + unposed, uv_mask, mesh_mask = get_unposed(folder, frame, nr, random_unposed) + static = get_statics(folder, nr, random_static) + body, pose, shape = get_bodies(folder, nr, random_body) + curv = get_curvs(folder, body, static, uv_mask, frame, nr, random_curv, curv_model) + + pred = model([unposed, static, body, curv, uv_mask], fix_noise=fix_noise) + mesh_pred = uv2mesh(pred) + + gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32) + 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])]) + + mesh_pred_shaped = mesh_pred + shape_offsets + + mesh_posed = smpl.run_garment(pose, shape, mesh_pred_shaped).numpy() + + plot_inputs(unposed[0], static[0], body[0], curv[0]) + + variable_cond = None + if random_static: + variable_cond = static + elif random_body: + variable_cond = body + elif random_curv: + variable_cond = curv + elif random_unposed: + variable_cond = unposed + else: + assert False + + plot_variable_condition(variable_cond, is_curv=random_curv) + + plot_results(mesh_pred, mesh_posed, body, mesh_mask) + + + print() + + + +if __name__ == '__main__': + # init_wandb() + # allow_memory_growth() + parser = argparse.ArgumentParser(description='') + parser.add_argument('--name', type=str) + args = vars(parser.parse_args()) + + folder = '/home/workspace/local-data/train_100' + folder_main = 'checkpoints/cvae_13' + folder_curv = 'checkpoints/cvae_curv_2' + variable_condition = 'curv' + batch_size = 4 + + smpl = SMPL() + + model = CVAEMain(avg_garment_path='custom_data/train_100_no_shape_avg_uv.npy', + latent_dim=128, static_model='checkpoints/ae_static_4', + body_model='checpoints/ae_body_8', + curvature_model='checpoints/ae_curvature_7') + curv_model = CVAECurv() + + + load_model(model, folder_main) + load_model(curv_model, folder_curv) + + sample(folder, variable_condition, fix_noise=True)