helper

2023-02-21 10:37:58 +00:00
11 changed files with 59 additions and 377 deletions
@@ -1,9 +1,5 @@
 nohup python cvae.py --name="lhunor_weights_test_10_2" > nohup/iccv/weights_test_10_2.out 2>&1 &
 nohup python evaluate_ae.py --name iccv_weights_test_6 > nohup/iccv/evaluate_iccv_weights_test_6.out 2>&1 &
 nohup python calculate_error.py --name iccv_weights_test_6 > nohup/iccv/error_iccv_weights_test_6.out 2>&1 &
 one final convolutional layer for smoothing, on the final coordinates
 nohup python calculate_error.py --name error_al_all_150k > nohup/error/post_al_all_150k.out 2>&1 &
 skirts!!!!
 pyramid next week
@@ -1,188 +0,0 @@
 import os
 import sys
 from configs.config_abl_all_post import config as config_a
 os.environ["CUDA_VISIBLE_DEVICES"] = '5'
 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 glob
 import time
 import argparse
 import wandb
 import tqdm
 import random
 import numpy as np
 import plotly.graph_objects as go
 import plotly.express as ex
 from PIL import Image
 from postprocessing import fix_collision
 if config_a['is_skirt']:
    from losses import edge_loss_skirt as edge_loss
 else:
    from losses import edge_loss
 from losses import collision_ratio, kl_loss #, concept_loss
 # from losses import collision_penalty as collision_penalty
 from losses import collision_loss as collision_penalty
 from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
 from models.cvae_model import CVAE
 from custom_utils.supersmpl_io import writeOBJ
 from plotly.subplots import make_subplots
 # uv2mesh = UV2Mesh(res=256, is_skirt=config_a['is_skirt'], is_adaptive=False)
 def plot(mesh_gt, pred_a, pred_b, body, mask):
    fig = make_subplots(rows=1, cols=3, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(3)] ])
    _, faces_garment = _maskFaces(np.zeros((14475, 3), np.float32), F, mask)
    faces = np.concatenate((F, faces_garment + 14475), axis=0)
    mesh_gt = np.concatenate((body, mesh_gt[mask, :]), axis=0)
    pred_a = np.concatenate((body, pred_a), axis=0)
    pred_b = np.concatenate((body, pred_b), axis=0)
    colors = np.concatenate((np.ones_like(body, dtype=np.int32)*100, np.ones_like(mesh_gt, dtype=np.int32)*230), axis=0)
    # colors = np.ones_like(mesh_gt, dtype=np.int32)*230
    fig.add_trace(go.Mesh3d(
            x=mesh_gt[:,0],
            y=mesh_gt[:,2],
            z=mesh_gt[:,1],
            i = faces[:,0],
            j = faces[:,1],
            k = faces[:,2],
            text="Ground Truth",
            vertexcolor=colors,
        ),
        row=1, col=1)
    fig.add_trace(go.Mesh3d(
            x=pred_a[:,0],
            y=pred_a[:,2],
            z=pred_a[:,1],
            i = faces[:,0],
            j = faces[:,1],
            k = faces[:,2],
            text="Pred A",
            vertexcolor=colors,
        ),
        row=1, col=2)
    fig.add_trace(go.Mesh3d(
            x=pred_b[:,0],
            y=pred_b[:,2],
            z=pred_b[:,1],
            i = faces[:,0],
            j = faces[:,1],
            k = faces[:,2],
            text="Pred B",
            vertexcolor=colors,
        ),
        row=1, col=3)
    fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1), 
                    scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1), 
                    scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1), 
                    )
    helper = dict(xaxis = dict(nticks=4, range=[-1,1],),
                yaxis = dict(nticks=4, range=[-1,1],),
                zaxis = dict(nticks=4, range=[-1,1],),)
    fig.update_layout(scene = helper,
                        scene2 = helper,
                        scene3 = helper,
                        )
    return fig
 if __name__ == '__main__':
    run = wandb.init(project='plots', name=config_a['name'], notes=config_a['notes'], dir='/wandb')
    parser = argparse.ArgumentParser(description='')
    parser.add_argument('--name', type=str)
    args = vars(parser.parse_args())
    skirt_modifier = '_skirt' if config_a['is_skirt'] else ''
    folder = config_a['test_dir']
    # smpl = SMPL(use_one_hot=False)
    F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
    iteration = ''
    # load_model(model, 'checkpoints/' + 'abl_all_120k')
    factory = DatasetFactory()
    dataset_a = factory.get_dataset(folder=config_a['test_dir'], 
                                       batch_size=config_a['batch_size'], 
                                       signature=config_a['signature_dynamic'], 
                                       resolutions=[config_a['resolution']], 
                                       in_memory=False, 
                                       do_repeat=False,
                                       use_one_hot=config_a['use_one_hot'],
                                       use_no_orient=config_a['remove_global_orientation'],
                                       shuffle=False,
                                       testing=True,
                                    #    frames_file='frames_abl.txt',
                                       )
    static_dataset_a = StaticDataset(folder=config_a['test_dir'], 
                                        signature=config_a['signature_static'], 
                                        resolutions=[config_a['resolution']], 
                                        in_memory=True)
    iterator_a = iter(dataset_a)
    faces_folder = '/home/workspace/data/results/support/faces' + ('_skirt' if config_a['is_skirt'] else '')
    ids = os.listdir(faces_folder)
    ids = [id.split('.')[0] for id in ids]
    faces = {id : np.load(faces_folder + '/' + id + '.npy') for id in ids }
    results_folder = '/home/workspace/data/results/post'
    count = 0
    while True:
        batch = iterator_a.get_next()
        batch = static_dataset_a.expand_batch(batch_to_dict(batch, config_a['signature_dynamic']))
        mask = batch['mesh_mask'].numpy().astype(bool)
        gt = batch['mesh_posed'].numpy()
        body = batch['mesh_body_posed'].numpy()
        for i in range(mask.shape[0]):
            id, frame_nr = batch['id'][i].numpy().decode("utf-8"), batch['frame_nr'][i].numpy().decode("utf-8")
            pred_a = np.load(results_folder + '/al_all_post-/' + id + '_' + frame_nr + '.npy')
            pred_b = np.load(results_folder + '/iccv_weights_test_6-/' + id + '_' + frame_nr + '.npy')
            # pred_c = np.load(results_folder + '/iccv_weights_test_6-/' + id + '_' + frame_nr + '.npy')
            fig = plot(gt[i], pred_a, pred_b, body[i], mask[i])
            wandb.log({f'{id}_{frame_nr}': fig}, step = count)
            count += 1
            print(count)
@@ -7,7 +7,7 @@ config = dict()
 # DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
 # DON'T FORGET TO CHECK AVG GARMENT USED
-config['use_skinning'] =              False # and deshaping
+config['use_skinning'] =              True # and deshaping
 config['remove_global_orientation'] = True
 config['use_curvature'] =             True
 config['use_extra_losses'] =          True # edge, collision, 3d mesh
@@ -19,13 +19,13 @@ config['only_curvatures'] =           False
 ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
 assert ablation_count == 1 or ablation_count == 0
-config['gpu'] = '6'
+config['gpu'] = '4'
-config['name'] = 'iccv_weights_test_10_3'
+config['name'] = 'al_all_post'
-config['notes'] = 'smaller weight loss coeff, 1e-6, convolutional weight learning, add gnn but smaller, same as 10 but training from zero and no avg garment'
+config['notes'] = ''
-config['avg_garment_path'] = ''# 'custom_data/train_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv  train_no_shape_one_hot_avg_uv
+config['avg_garment_path'] = 'custom_data/train_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv  train_no_shape_one_hot_avg_uv
-# if config['use_skinning']:
+if config['use_skinning']:
-#   assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
+  assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
 config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
@@ -86,15 +86,14 @@ config['use_one_hot'] = False
 config['cvae'] = dict()
 config['cvae']['is_label'] = False
 config['cvae']['add_noise'] = True
-config['cvae']['noise_amount'] = 0.00001
+config['cvae']['noise_amount'] = 0.001
 config['cvae']['kernel_size'] = 7
 config['cvae']['encoder_depths'] = [3, 3, 3, 9, 3]
 config['cvae']['encoder_dims'] = [32, 64, 128, 256, 64]
 config['cvae']['decoder_depths'] = config['cvae']['encoder_depths']
 config['cvae']['decoder_dims'] = config['cvae']['encoder_dims']
-config['cvae']['decoder_weights_depths'] = [3, 3, 3, 5, 3]
+
 config['cvae']['decoder_weights_dims'] = [24, 32, 64, 128, 256]
 config['cond_ae_static'] = dict()
 config['cond_ae_static']['is_label'] = False
@@ -2,7 +2,7 @@ from unittest import skip
 import numpy as np
 import tensorflow as tf
 from tensorflow.keras.layers import (Conv2D, Conv2DTranspose, Dense, DepthwiseConv2D,
-                                     GlobalAveragePooling2D, Layer, Activation, Lambda,
+                                     GlobalAveragePooling2D, Layer,
                                     LayerNormalization, Flatten, Reshape, UpSampling2D)
 def drop_path(inputs, drop_prob, is_training):
@@ -222,7 +222,7 @@ class ConvNeXtReverse(tf.keras.Model):
    def __init__(self, 
                 depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], kernel_size=7,
-                 drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1., learn_weights=False,
+                 drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1.,
                 ):
        super().__init__()
        self.upsample_layers = []  # stem and 3 intermediate downsampling conv layers
@@ -255,19 +255,12 @@ class ConvNeXtReverse(tf.keras.Model):
            self.stages.append(stage)
            cur += depths[i]
-        if not learn_weights:
+        self.last = Dense(3, activation='tanh')
            self.last = Dense(3, activation='tanh')
        else:
            self.last = Activation('softmax')
            # self.last = lambda x, training: x
            # self.last = Dense(24, activation='softmax')
    def call(self, x, training):
        for i in reversed(range(len(self.upsample_layers))):
            x = self.upsample_layers[i](x, training=training)
            x = self.stages[i](x, training=training)
        x = self.last(x, training=training)
        return x
@@ -2,7 +2,6 @@ import json
 import numpy as np
 import tensorflow as tf
 import scipy as sp
 import cmapy as cm
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
@@ -217,35 +216,6 @@ def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, colli
    return fig
 def plot_weights(weights, body, faces):
    fig = go.Figure()
    colors = np.array([cm.color('jet', 1 - w) for w in weights.numpy()])
    # if flip_xy:
    #     mesh_gt = flip_axis(mesh_gt)
    #     mesh_pred_unposed = flip_axis(mesh_pred_unposed)
    #     collision = flip_axis(collision, ind=14475)
    fig.add_trace(go.Mesh3d(
                x=body[:,0],
                y=body[:,2],
                z=body[:,1],
                i = faces[:,0],
                j = faces[:,1],
                k = faces[:,2],
                vertexcolor=colors
            ))
    fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),)
    helper = dict(xaxis = dict(nticks=4, range=[-1,1],),
                yaxis = dict(nticks=4, range=[-1,1],),
                zaxis = dict(nticks=4, range=[-1,1],),)
    fig.update_layout(scene = helper,)
    return fig
 def plot_3d_collision(gt, collision_1, collision_2, collision_faces, collision_color, flip_xy=False):
    fig = make_subplots(rows=1, cols=3, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(3)] ])
@@ -406,8 +406,7 @@ class SMPL:
        return T_out, J_out
-    def run_garment_with_weights(self, pose, betas, v_cloth, weights, J=None):
+    def run_garment_with_weights(self, R, betas, v_cloth, weights, J=None):
        R = self.run_rodrigues(pose)
        if J is None:
            J = self.get_joints_template(betas)
@@ -431,12 +430,13 @@ class SMPL:
            J = tf.gather(J, tf.constant([0, 1, 2], tf.int32), axis=1)
            T = tf.tensordot(weights, results, axes=((1), (0)))
-            rest_shape_h = tf.concat((v_cloth, tf.ones((v_cloth.shape[0], 1), dtype=tf.float32)), axis=1)
+            rest_shape_h = tf.concat((v_cloth, tf.ones((v_cloth.get_shape().as_list()[0], 1), dtype=tf.float32)), axis=1)
            v = tf.matmul(T, tf.reshape(rest_shape_h, (-1, 4, 1)))
            v = tf.reshape(v, (-1, 4))[:, :3]
            return v, tf.squeeze(J)
        # gender = tf.cast(tf.greater_equal(betas[:, 10] , 0), tf.int32)
        T_out, J_out = tf.map_fn(single_run, (R, J, v_cloth, weights), (tf.float32, tf.float32))
        J0 = tf.reshape(J_out[:,0,:], (-1, 1, 3))
@@ -38,11 +38,10 @@ else:
 from losses import collision_ratio, kl_loss, normal_loss #, concept_loss
 # from losses import collision_penalty as collision_penalty
 from losses import collision_loss as collision_penalty
-from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, UV2MeshLabel, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision, plot_weights
+from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
 from models.cvae_model import CVAENew as CVAE
 from models.gnn import Smoother
 def init_wandb():
@@ -63,7 +62,6 @@ def init_wandb():
    run.log_artifact(artifact)
 uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_adaptive=False, is_smpl_orig=config['is_cape'])
 uv2mesh_label = UV2MeshLabel(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
 def kl_loss(mean, logvar):
@@ -73,15 +71,9 @@ def kl_loss(mean, logvar):
    return kl_loss
 def blend_weights_reg(pred_w, gender=None):
    orig = smpl.weights
    l1 = tf.reduce_mean(tf.abs(pred_w - orig[None, 0, :, :]))
    return l1
 # 3d loss on posed !!!!!!!!!!!!
-@tf.function
+# @tf.function
 def train_step(batch):
    with tf.GradientTape() as tape:
@@ -90,12 +82,9 @@ def train_step(batch):
        else:
            mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
-        pred, mean, logvar, weights = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=True)
+        pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=True)
        mesh_pred = uv2mesh(pred)
        mesh_pred = smoother(mesh_pred, training=True)
        mesh_weigths = uv2mesh_label(weights)
        mesh_weigths = smoother_weights(mesh_weigths, training=True)
        if config['use_skinning']:
            shape = batch['pose_shape'][:, 72:]
@@ -105,31 +94,25 @@ def train_step(batch):
            mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
            mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
        else:
-            shape = batch['pose_shape'][:, 72:]
+            mesh_pred_posed = mesh_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 * batch['mesh_mask'][:,:,tf.newaxis]
            mesh_pred_posed = smpl.run_garment_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped, mesh_weigths)
        # is the output already masked?
        e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
        c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
        c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
-        # l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
+        l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
        l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
        n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
        # conc_loss = concept_loss(pred, batch['uv_unposed'])
        kl = kl_loss(mean, logvar)
        weights_l = blend_weights_reg(mesh_weigths)
-        reconstr_loss = l2_mesh_loss + 1e-6 * weights_l
+        reconstr_loss = l2_loss
        if config['use_extra_losses']:
            if 'block_collision_loss' in config.keys() and config['block_collision_loss'] == True:
-                reconstr_loss = reconstr_loss + (e_loss) * config['reg_loss_coeff']
+                reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss) * config['reg_loss_coeff']
            else:
-                reconstr_loss = reconstr_loss + (e_loss + c_loss) * config['reg_loss_coeff']
+                reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
        if config['use_norm_loss'] and step > 2000:
            reconstr_loss = reconstr_loss + n_loss * config['reg_loss_coeff']
@@ -139,14 +122,14 @@ def train_step(batch):
        logvar = tf.reduce_mean(logvar)
    # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
-    metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred_posed) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
+    metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
-    gradients = tape.gradient(loss, model.trainable_variables + smoother.trainable_variables + smoother_weights.trainable_variables)
+    gradients = tape.gradient(loss, model.trainable_variables)
-    optimizer.apply_gradients(zip(gradients, model.trainable_variables + smoother.trainable_variables + smoother_weights.trainable_variables))
+    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/collision_loss': c_loss, 'train/euclid_error': metric,
             'train/reconstr_loss': reconstr_loss, 'train/collision_ratio': c_ratio,
             'train/mean': mean, 'train/logvar': logvar, 'train/kl_loss': kl, 'train/var': np.e ** logvar,
-             'train/l2_mesh_loss': l2_mesh_loss, 'train/normal_loss': n_loss, 'train/weights_loss': weights_l,
+             'train/l2_loss': l2_loss, 'train/l2_mesh_loss': l2_mesh_loss, 'train/normal_loss': n_loss,
-             # 'train/ms_ssim': ms_ssim, 'train/l2_loss': l2_loss, 
+             # 'train/ms_ssim': ms_ssim, 
            }
@@ -157,12 +140,9 @@ def valid_step(batch):
    else:
        mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
-    pred, mean, logvar, weights = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
+    pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
    mesh_pred = uv2mesh(pred)
    mesh_pred = smoother(mesh_pred, training=False)
    mesh_weigths = uv2mesh_label(weights)
    mesh_weigths = smoother_weights(mesh_weigths, training=False)
    if config['use_skinning']:
        shape = batch['pose_shape'][:, 72:]
@@ -172,28 +152,22 @@ def valid_step(batch):
        mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
        mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
    else:
-        shape = batch['pose_shape'][:, 72:]
+        mesh_pred_posed = mesh_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 * batch['mesh_mask'][:,:,tf.newaxis]
        mesh_pred_posed = smpl.run_garment_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped, mesh_weigths)
    # is the output already masked?
    e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
    c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
    c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
-    # l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
+    l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
    l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
    n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
    # conc_loss = concept_loss(pred, batch['uv_unposed'])
    kl = kl_loss(mean, logvar)
    weights_l = blend_weights_reg(mesh_weigths)
-    reconstr_loss = l2_mesh_loss + 1e-6 * weights_l
+    reconstr_loss = l2_loss
    if config['use_extra_losses']:
-        reconstr_loss = reconstr_loss + (e_loss + c_loss) * config['reg_loss_coeff']
+        reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
    if config['use_norm_loss']:
        reconstr_loss = reconstr_loss + n_loss * config['reg_loss_coeff']
@@ -203,12 +177,12 @@ def valid_step(batch):
    logvar = tf.reduce_mean(logvar)
    # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
-    metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred_posed) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
+    metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
    # c_ratio = tf.reduce_mean(collision_ratio(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
    return { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/collision_loss': c_loss, 'valid/euclid_error': metric, 
             'valid/reconstr_loss': reconstr_loss, 'valid/l2_mesh_loss': l2_mesh_loss, 'valid/collision_ratio': c_ratio,
-             'valid/mean': mean, 'valid/logvar': logvar, 'valid/kl_loss': kl, 'valid/var': np.e ** logvar, 'valid/weights_loss': weights_l,
+             'valid/mean': mean, 'valid/logvar': logvar, 'valid/kl_loss': kl, 'valid/var': np.e ** logvar,
              'valid/normal_loss': n_loss,# 'valid/ms_ssim': ms_ssim, 
              }
@@ -220,7 +194,7 @@ def test_step(batch):
    else:
        mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
-    pred, mean, logvar, weights = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
+    pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
    mesh_pred = uv2mesh(pred)
@@ -234,7 +208,7 @@ def test_step(batch):
        mesh_orig_shaped = mesh_orig + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
        mesh_orig_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_orig_shaped)
    else:
-        mesh_pred_posed = smpl.run_garment_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred, weights)
+        mesh_pred_posed = mesh_pred
        mesh_orig_posed = mesh_orig
    mesh_pred_posed_fixed = fix_collision(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
@@ -276,12 +250,9 @@ def plot_everything(batch: dict, type: str):
    else:
        mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
-    pred, mean, logvar, weights = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
+    pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
-    mesh_pred = uv2mesh(pred)
+    mesh_pred = uv2mesh(pred).numpy()
    mesh_pred = smoother(mesh_pred, training=False).numpy()
    mesh_weigths = uv2mesh_label(weights)
    mesh_weigths = smoother_weights(mesh_weigths, training=False)
    orig_img = uv_orig
    uv_static_img = batch['uv_static']
@@ -289,9 +260,6 @@ def plot_everything(batch: dict, type: str):
    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)]
    # min max normalization of weights
    weights = (weights - tf.reduce_min(weights, axis=[1,2,3], keepdims=True)) / (tf.reduce_max(weights, axis=[1,2,3], keepdims=True) - tf.reduce_min(weights, axis=[1,2,3], keepdims=True))
    weights_imgs = [wandb.Image(np.concatenate((np.concatenate([weights[b,:,:,i].numpy() for i in range(12)], axis=-1), np.concatenate([weights[b,:,:,i].numpy() for i in range(12,24)], axis=-1)), axis=0)) for b in range(4)]
    F = np.load("custom_utils/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
    mask = batch['mesh_mask'].numpy().astype(bool)
@@ -309,12 +277,7 @@ def plot_everything(batch: dict, type: str):
            mesh_pred[i, mask[i], :] += shape_offsets.numpy()[i, mask[i], :]
        mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred).numpy()
    else:
-        shape = batch['pose_shape'][:, 72:]
+        mesh_posed = mesh_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 * batch['mesh_mask'][:,:,tf.newaxis]
        mesh_posed = smpl.run_garment_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped, mesh_weigths).numpy()
        gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred.shape[0])]
        garment_unposed, faces = maskFacesBatch(mesh_pred, F, mask)
@@ -333,16 +296,13 @@ def plot_everything(batch: dict, type: str):
    collision_fixed = [np.concatenate((body[i], garment_posed_fixed[i]), axis=0) for i in range(len(garment_posed))]
    collision_gt = [np.concatenate((body[i], gt_posed[i]), axis=0) for i in range(len(garment_posed))]
-    # figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], garment_posed[i], faces[i], collision_color[i], flip_xy=(not config['use_skinning'] and config['remove_global_orientation'])) for i in range(4) }
+    figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i], flip_xy=(not config['use_skinning'] and config['remove_global_orientation'])) for i in range(4) }
-    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) }
+    figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision_gt[i], collision[i], collision_fixed[i], collision_faces[i], collision_color[i], flip_xy=(not config['use_skinning'] and config['remove_global_orientation'])) for i in range(4) }
    figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision_gt[i], collision[i], collision_fixed[i], collision_faces[i], collision_color[i],) for i in range(4) }
    figs_weights = { type + '/weights_' + str(i): plot_weights(mesh_weigths[0,:,i], body[0], F) for i in range(24) }
    # figs_weights = { type + '/weights_' + str(i): plot_weights(smpl.weights[0,:,i], body[0], F) for i in range(24) }
    mean_hist = wandb.Histogram(sequence=mean.numpy().mean(axis=0))
    logvar_hist = wandb.Histogram(sequence=logvar.numpy().mean(axis=0))
-    return { **figs, **figs_coll, **figs_weights, **{ type + '/images': imgs, type + '/weight_images': weights_imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } }
+    return { **figs, **figs_coll, **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } }
 def sample(folder): 
@@ -516,7 +476,7 @@ def test():
 if __name__ == '__main__':
-    init_wandb()
+    # init_wandb()
    parser = argparse.ArgumentParser(description='')
    parser.add_argument('--name', type=str)
    args = vars(parser.parse_args())
@@ -557,8 +517,6 @@ if __name__ == '__main__':
                                        is_cape=config['is_cape'])
    model = CVAE(config)
    smoother = Smoother(features=24, activation_middle='tanh', activation_final='tanh', learn_weights=False)
    smoother_weights = Smoother(features=3, activation_middle=None, activation_final='softmax', learn_weights=True)
    optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
    balancing_activated_step = 0
@@ -583,10 +541,9 @@ if __name__ == '__main__':
        train_loss = train_step(batch)
-        if step % 25 == 0:
+        data_valid = valid_iterator.get_next()
-            data_valid = valid_iterator.get_next()
+        data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
-            data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
+        valid_loss = valid_step(data_valid)
            valid_loss = valid_step(data_valid)
        to_log = { **train_loss, **valid_loss, **{ 'current_step': step} }
        if step % 5000 == 0 or (step < 5000 and step % 500 == 0):
@@ -39,7 +39,7 @@ else:
 from losses import collision_ratio, kl_loss #, concept_loss
 # from losses import collision_penalty as collision_penalty
 from losses import collision_loss as collision_penalty
-from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, UV2MeshLabel, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
+from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
 from models.cvae_model import CVAENew as CVAE
@@ -48,7 +48,6 @@ from custom_utils.supersmpl_io import writeOBJ
 uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_adaptive=False, is_smpl_orig=config['is_cape'])
 uv2mesh_label = UV2MeshLabel(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
@tf.function
 def step(batch):
@@ -57,10 +56,9 @@ def step(batch):
    else:
        uv_orig =  batch['uv_posed']
-    pred, _, _, weights = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
+    pred, _, _ = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
    mesh_pred = uv2mesh(pred)
    mesh_weigths = uv2mesh_label(weights)
    if config['use_skinning']:
        shape = batch['pose_shape'][:, 72:]
@@ -70,13 +68,7 @@ def step(batch):
        mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
        mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
    else:
-        shape = batch['pose_shape'][:, 72:]
+        mesh_pred_posed = mesh_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 * batch['mesh_mask'][:,:,tf.newaxis]
        mesh_pred_posed = smpl.run_garment_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped, mesh_weigths)
    # mesh_pred_posed_fixed = fix_collision(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
@@ -171,13 +171,13 @@ class AEEncoderNew(tf.keras.Model):
 class DecoderNew(tf.keras.Model):
-    def __init__(self, depths, dims, kernel_size=7, is_label=False, learn_weights=False):
+    def __init__(self, depths, dims, kernel_size=7, is_label=False):
        super(DecoderNew, self).__init__()
        self.is_label = is_label
        if not is_label:
-            self.decoder = ConvNeXtReverse(depths=depths, dims=dims, kernel_size=kernel_size, learn_weights=learn_weights)
+            self.decoder = ConvNeXtReverse(depths=depths, dims=dims, kernel_size=kernel_size)
        else:
            self.decoder = ConvNeXtReverseLabels(depths=depths, dims=dims, nr_of_classes=7)
@@ -181,7 +181,6 @@ class CVAENew(tf.keras.Model):
        self.encoder = VariationalEncoderNew(config['cvae']['encoder_depths'], config['cvae']['encoder_dims'], kernel_size=config['cvae']['kernel_size'])
        self.decoder = DecoderNew(config['cvae']['decoder_depths'], config['cvae']['decoder_dims'], config['cvae']['kernel_size'])
        self.decoder_weights = DecoderNew(config['cvae']['decoder_weights_depths'], config['cvae']['decoder_weights_dims'], config['cvae']['kernel_size'], learn_weights=True)
        self.reshape = tf.keras.layers.Reshape((8, 4, 64))
@@ -194,12 +193,12 @@ class CVAENew(tf.keras.Model):
        self.noise_amount = config['cvae']['noise_amount']
        self.use_curvature = config['use_curvature']
        self.only_curvatures = config['only_curvatures'] if 'only_curvatures' in config.keys() and config['only_curvatures'] == True else None
-        self.use_offsets_over_body = False
+        self.use_offsets_over_body = not config['use_skinning']
        if self.use_offsets_over_body:
            assert self.avg_garment is None, 'When we are not using skinning, we can\'t use avg_garment!'
-        # if self.avg_garment is not None:
+        if self.avg_garment is not None:
-        #     assert not self.use_offsets_over_body, 'When avg_garment is given, we must use skinning!'
+            assert not self.use_offsets_over_body, 'When avg_garment is given, we must use skinning!'
    # @tf.function
    def sample(self, uv_template, uv_body_posed, uv_curv, uv_mask, noise=None, add_noise=False):
@@ -215,7 +214,6 @@ class CVAENew(tf.keras.Model):
        z = tf.concat([eps, z_cond], axis=-1)
        x = self.decoder(z, training=False)
        weights = self.decoder_weights(z, training=False)
        if self.avg_garment is not None:
            x = x + self.avg_garment[tf.newaxis,:,:,:]
@@ -225,7 +223,7 @@ class CVAENew(tf.keras.Model):
        x = x * uv_mask[:,:,:,tf.newaxis]
-        return x, weights
+        return x
    def reparameterize(self, mean, logvar, training, fix_noise=False):
@@ -274,7 +272,6 @@ class CVAENew(tf.keras.Model):
        z = tf.concat([z, z_cond], axis=-1)
        x = self.decoder(z, training=training)
        weights = self.decoder_weights(z, training=training)
        if self.avg_garment is not None:
            x = x + self.avg_garment[tf.newaxis,:,:,:]
@@ -284,5 +281,5 @@ class CVAENew(tf.keras.Model):
        x = x * uv_mask[:,:,:,tf.newaxis]
-        return x, mean, logvar, weights
+        return x, mean, logvar
@@ -1,34 +0,0 @@
 import os
 import sys
 import scipy.io as sio
 import tensorflow as tf
 from spektral.layers import GCNConv, GeneralConv
 from spektral.utils import sp_matrix_to_sp_tensor
 from tensorflow.keras.layers import (Conv2D, Conv2DTranspose, Dense, DepthwiseConv2D,
                                     GlobalAveragePooling2D, Layer,
                                     LayerNormalization, Flatten, Reshape, UpSampling2D)
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 class Smoother(tf.keras.Model):
    def __init__(self, features=64, activation_middle='tanh', activation_final='tanh', learn_weights=False):
        super().__init__()
        self.a = sp_matrix_to_sp_tensor(sio.loadmat("custom_utils/ssmpl0.mat")['L'].tocoo())
        self.conv1 = GCNConv(features, activation=activation_middle, use_bias=False)
        self.conv2 = GCNConv(features, activation=activation_middle, use_bias=False)
        # self.conv3 = GCNConv(features, activation=activation_middle, use_bias=False)
        # self.conv4 = GCNConv(64, activation='tanh', use_bias=False)
        # self.conv5 = GCNConv(64, activation='tanh', use_bias=False)
        self.conv6 = GCNConv(3 if not learn_weights else 24, activation=activation_final, use_bias=False)
    def call(self, inputs, training=None):
        x = inputs
        x = self.conv1([x, self.a], training=training)
        x = self.conv2([x, self.a], training=training)
        # x = self.conv3([x, self.a], training=training)
        # x = self.conv4([x, self.a], training=training)
        # x = self.conv5([x, self.a], training=training)
        x = self.conv6([x, self.a], training=training)
        return x