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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "3"
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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 cmapy as cm
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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, UV2MeshLabel, maskFacesBatch, _maskFaces
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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 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 models.cvae_curv import CVAE
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name='cvae_curv_2_2_2'
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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. new implementation for kl loss. \
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no lr schedule. 25 jumpsuits, average reconst_loss before calculation, offsets over average unposed garment. gamma balancing. remove global \
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orientation (in posed body input and in pose_shape parameters.. retrained body encoder with deoriented bodies. lower kl coeff to 0.1. fixed plotting colors\
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fixed deoriented bodies and need retraining (ae_body_6 with 50k iteration), addent MLP for lowering conditional Z diemnsion\
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higher starting kl coeff (0.1)')
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artifact = wandb.Artifact('code', type='code')
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artifact.add_file('cvae_curv.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 = UV2MeshLabel(res=256)
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def plot_3d(curv_gt, curv_pred, faces, mesh):
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mesh_2 = mesh.copy()
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mesh_2[:,1] += 1
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colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, 6+1)])
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v_color_1 = np.zeros((mesh.shape[0], 3))
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v_color_2 = np.zeros((mesh.shape[0], 3))
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for i in range(6+1):
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v_color_1[curv_gt == i, :] = colors[i]
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v_color_2[curv_pred == i, :] = colors[i]
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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[:,0],
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y=mesh[:,1],
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z=mesh[:,2],
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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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vertexcolor=v_color_1,
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text='gt'
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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_2[:,0],
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y=mesh_2[:,1],
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z=mesh_2[:,2],
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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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vertexcolor=v_color_2,
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text='pred'
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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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def log_normal_pdf(sample, mean, logvar, raxis=1):
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log2pi = tf.math.log(2. * np.pi)
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res = tf.reduce_sum(-.5 * ((sample - mean) ** 2. * tf.exp(-logvar) + logvar + log2pi), axis=raxis)
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return res
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def kl_loss(mean, logvar):
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kl_loss = 1 + logvar - tf.square(mean) - tf.exp(logvar)
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kl_loss = tf.reduce_mean(kl_loss)
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kl_loss *= -0.5
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return kl_loss
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g_mean_iou = tf.keras.metrics.MeanIoU(num_classes=7)
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def get_mean_iou(y_true, y_pred):
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g_mean_iou.reset_state()
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g_mean_iou.update_state(y_true, y_pred)
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return g_mean_iou.result()
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# https://github.com/NifTK/NiftyNet/blob/dev/niftynet/layer/loss_segmentation.py
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def dice_dense(prediction, ground_truth, weight_map=None):
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"""
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Computing mean-class Dice similarity.
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:param prediction: last dimension should have ``num_classes``
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:param ground_truth: segmentation ground truth (encoded as a binary matrix)
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last dimension should be ``num_classes``
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:param weight_map:
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:return: ``1.0 - mean(Dice similarity per class)``
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"""
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if weight_map is not None:
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raise NotImplementedError
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prediction = tf.cast(prediction, dtype=tf.float32)
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ground_truth = tf.cast(ground_truth, dtype=tf.float32)
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ground_truth = tf.reshape(ground_truth, prediction.shape)
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# computing Dice over the spatial dimensions
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reduce_axes = list(range(len(prediction.shape) - 1))
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dice_numerator = 2.0 * tf.reduce_sum(
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prediction * ground_truth, axis=reduce_axes)
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dice_denominator = \
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tf.reduce_sum(tf.square(prediction), axis=reduce_axes) + \
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tf.reduce_sum(tf.square(ground_truth), axis=reduce_axes)
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epsilon = 0.00001
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dice_score = (dice_numerator + epsilon) / (dice_denominator + epsilon)
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return tf.reduce_mean(dice_score)
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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_static'], batch['uv_body_posed'], uv_curvature, batch['uv_mask'], batch['uv_shaped_body']])
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ce_uv_loss = scce(batch['uv_curvature'], pred)
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ms_ssim = tf.reduce_mean(tf.image.ssim(pred, uv_curvature, max_val=1))
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reconstr_loss = ce_uv_loss
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reconstr_loss = tf.reduce_mean(reconstr_loss)
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kl = kl_loss(mean, logvar)
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loss = reconstr_loss + balancing_coeff * kl
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ms_ssim = tf.reduce_mean(ms_ssim)
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mean = tf.reduce_mean(mean)
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logvar = tf.reduce_mean(logvar)
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# mesh_pred = uv2mesh(tf.tile(tf.math.argmax(pred, axis=-1, output_type=tf.dtypes.int32)[:,:,:,tf.newaxis], [1,1,1,3]))[:,:,0]
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# mesh_orig = tf.one_hot(batch['mesh_curvature'] + tf.cast(batch['mesh_mask'], tf.int32), 7)
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# ce_mesh_loss = scce(mesh_orig, mesh_pred)
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mean_iou = get_mean_iou(batch['uv_curvature'], tf.math.argmax(pred, axis=-1))
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dice = dice_dense(pred, uv_curvature)
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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 { 'train/loss': loss, 'train/ce_uv_loss': ce_uv_loss, 'train/ssim': ms_ssim, 'train/mean_iou': mean_iou, 'train/dice': dice, #'ce_mesh_loss': ce_mesh_loss
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'train/reconstr_loss': reconstr_loss,
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'train/mean': mean, 'train/logvar': logvar, 'train/kl_loss': kl, 'train/reconst_loss_norm': reconstr_loss / (2 * (gamma ** 2)), 'train/var': np.e ** logvar,
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'train/reg_coeff': reg_coeff}
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@tf.function
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def valid_step(batch):
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uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
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pred, mean, logvar, z = model([batch['uv_static'], batch['uv_body_posed'], uv_curvature, batch['uv_mask'], batch['uv_shaped_body']])
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ce_uv_loss = scce(batch['uv_curvature'], pred)
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ms_ssim = tf.reduce_mean(tf.image.ssim(pred, uv_curvature, max_val=1))
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loss = ce_uv_loss
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mean_iou = get_mean_iou(batch['uv_curvature'], tf.math.argmax(pred, axis=-1))
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dice = dice_dense(pred, uv_curvature)
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reconstr_loss = tf.reduce_mean(ce_uv_loss)
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kl = kl_loss(mean, logvar)
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loss = reconstr_loss + balancing_coeff * kl
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ms_ssim = tf.reduce_mean(ms_ssim)
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mean = tf.reduce_mean(mean)
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logvar = tf.reduce_mean(logvar)
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return { 'valid/loss': loss, 'valid/ce_uv_loss': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mean_iou': mean_iou, 'valid/dice': dice,
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'valid/ms_ssim': ms_ssim, 'valid/reconstr_loss': reconstr_loss,
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'valid/mean': mean, 'valid/logvar': logvar, 'valid/kl_loss': kl, 'valid/var': np.e ** logvar }
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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_static'], batch['uv_body_posed'], uv_curvature, batch['uv_mask'], batch['uv_shaped_body']])
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pred = np.argmax(pred.numpy(), axis=-1)
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_curvature']
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mesh_base = batch['mesh_posed']
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orig_img = batch['uv_curvature']
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pred_img = pred
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img = tf.concat((orig_img, pred_img), axis=2)
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imgs = [wandb.Image(img[i].numpy().astype(np.float32) / 6.0) 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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_, faces = maskFacesBatch(np.zeros((4, 14475, 3)), F, mask)
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gt_curv = [mesh_orig[i].numpy()[mask[i]] + 1 for i in range(pred.shape[0])]
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pred_curv = [mesh_pred[i].numpy()[mask[i]] for i in range(pred.shape[0])]
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mesh = [mesh_base[i].numpy()[mask[i]] for i in range(pred.shape[0])]
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figs = { type + "/garment_" + str(i): plot_3d(gt_curv[i], pred_curv[i], faces[i], mesh[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):
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load_model(model,folder)
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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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uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
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nr_of_samples = 16
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# pred = model.sample(batch['uv_static'], batch['uv_body_posed'], batch['uv_mask'])
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pred = 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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ce_uv_loss = scce(tf.tile(batch['uv_curvature'][0][tf.newaxis,:,:], [nr_of_samples,1,1]), pred)
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mesh_pred = uv2mesh(pred).numpy().argmax(axis=-1)
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mesh_orig = batch['mesh_curvature'][0].numpy()
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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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mesh_pred = [mesh_pred[i, mask] for i in range(mesh_pred.shape[0])]
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curv_color = [np.zeros((mesh_pred[i].shape[0], 3)) for i in range(len(mesh_pred))]
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mesh_orig = mesh_orig[mask] + 1
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colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, 6+1)])
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curv_color_gt = np.zeros((mesh_pred[0].shape[0], 3))
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for i in range(6+1):
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for j in range(len(mesh_pred)):
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curv_color[j][mesh_pred[j] == i, :] = colors[i]
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curv_color_gt[mesh_orig == i, :] = colors[i]
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mesh_base = batch['mesh_posed'][0].numpy()[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_base[:,0],
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y=mesh_base[:,1],
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z=mesh_base[:,2],
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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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vertexcolor=curv_color_gt,
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text='gt'
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))
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for i in range(nr_of_samples):
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x = mesh_base.copy()
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x[:, 1] += 1 * (i + 1)
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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[:,1],
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z=x[:,2],
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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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vertexcolor=curv_color[i]
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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(
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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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zaxis = dict(nticks=4, range=[-1,1],),)
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)
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# fig.show()
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pred = pred.numpy().argmax(-1)
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frames = [Image.fromarray(pred[i]/6*255) for i in range(pred.shape[0])]
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frames[0].save("temp/curv2.gif", format="GIF", append_images=frames[1:],
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save_all=True, duration=500, loop=0)
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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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scce = tf.keras.losses.SparseCategoricalCrossentropy()
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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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'mesh_curvature': (14475)
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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 = CVAE(latent_dim=128, static_model='checkpoints/ae_static_3',
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body_model='checpoints/ae_body_6',)
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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_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=5)
|
||||
|
||||
step = 0
|
||||
for batch in dataset_train:
|
||||
batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic))
|
||||
|
||||
train_loss = train_step(batch)
|
||||
|
||||
# data_valid = valid_iterator.get_next()
|
||||
# data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, signature_dynamic))
|
||||
# valid_loss = valid_step(data_valid)
|
||||
valid_loss = {}
|
||||
|
||||
to_log = { **train_loss, **valid_loss, **{ 'current_step': step} }
|
||||
if step % 2500 == 0:
|
||||
train_figs = plot_everything(batch, 'train')
|
||||
# valid_figs = plot_everything(data_valid, 'valid')
|
||||
valid_figs = {}
|
||||
to_log = { **to_log, **train_figs, **valid_figs }
|
||||
|
||||
if step % 25000 == 0:
|
||||
manager.save(checkpoint_number=step)
|
||||
|
||||
if step > 25000:
|
||||
# if train_loss['train/euclid_error'] < 0.01:
|
||||
balancing_coeff = balancing_coeff + 1 / 20000
|
||||
# if train_loss['train/euclid_error'] < 0.015:
|
||||
# reg_coeff = reg_coeff + 100 / 15000
|
||||
|
||||
|
||||
# if step > 15000 and balancing_coeff <= 1e-3:
|
||||
# balancing_coeff = 1e-2
|
||||
# if step > 20000 and balancing_coeff <= 1e-3:
|
||||
# balancing_coeff = 1e-2
|
||||
# if step > 25000 and balancing_coeff <= 1e-2:
|
||||
# balancing_coeff = 1e-1
|
||||
# if step > 30000 and balancing_coeff <= 1e-1:
|
||||
# balancing_coeff = 1
|
||||
|
||||
# gamma = 0.99 * gamma + 0.01 * train_loss['train/reconstr_loss']
|
||||
|
||||
to_log = { **to_log, **{ 'train/balancing_coeff': balancing_coeff, 'train/gamma': gamma } }
|
||||
|
||||
wandb.log(to_log, step = step)
|
||||
|
||||
print(step)
|
||||
|
||||
step += 1
|
||||
Reference in New Issue
Block a user