new weight trials, 10_3 didn't run

This reverts commit ac27319860.

aaa_helpers.txt

@@ -1,3 +1,8 @@
+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
 
 skirts!!!!

auto_encoder_curv.py

@@ -95,7 +95,7 @@ def dice_dense(prediction, ground_truth, weight_map=None):
     return tf.reduce_mean(dice_score)
 
 
-# @tf.function
+@tf.function
 def train_step(batch):
     with tf.GradientTape() as tape:
         pred = model([batch['uv_curvature'], batch['uv_mask']], training=True)

compare_models_saved.py

@@ -0,0 +1,188 @@
+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)
+

configs/config_abl_all_post.py

@@ -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'] =              True # and deshaping
+config['use_skinning'] =              False # 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'] = '1'
+config['gpu'] = '6'
-config['name'] = 'al_all_post'
+config['name'] = 'iccv_weights_test_10_3'
-config['notes'] = ''
+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['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
 
@@ -86,14 +86,15 @@ config['use_one_hot'] = False
 config['cvae'] = dict()
 config['cvae']['is_label'] = False
 config['cvae']['add_noise'] = True
-config['cvae']['noise_amount'] = 0.001
+config['cvae']['noise_amount'] = 0.00001
 
 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

configs/config_abl_overfit_jumpsuit_2.py

@@ -19,7 +19,7 @@ config['only_curvatures'] =           True
 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'] = '3'
+config['gpu'] = '5'
 config['name'] = 'abl_overfit_jumpsuit_2'
 config['notes'] = 'remove other conditions from input as well'
 

configs/config_abl_pyr_32.py

@@ -1,7 +1,7 @@
 config = dict()
 
 config['gpu'] = '0'
-config['name'] = 'pyr_abl_32_r1'
+config['name'] = 'pyr_abl_32'
 config['notes'] = ''
 
 # DON'T FORGET TO RESET SSIM LOSS USAGE

configs/config_abl_pyr_32_skirt.py

@@ -1,8 +1,8 @@
 config = dict()
 
 config['gpu'] = '3'
-config['name'] = 'pyr_abl_32_skirt_r1'
+config['name'] = 'pyr_abl_32_skirt_2'
-config['notes'] = ''
+config['notes'] = 'continued from pyr_abl_32_skirt-180k'
 
 # DON'T FORGET TO RESET SSIM LOSS USAGE
 # DON'T FORGET TO CHECK MODEL LOADING
@@ -24,10 +24,7 @@ config['pyramid']['resolutions'] = [32]
 config['pyramid']['pretrained_pyramid'] = ''
 config['pyramid']['final_upscale_enhancer'] = 'checkpoints/32_512_skirt'
 
-config['pretrained_model'] = ''
+config['train_dir'] = '/home/workspace/local-data/train_skirt'
-config['pretrained_model_iteration'] = ''
-
-config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
 config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
 config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
 config['batch_size'] = 4
@@ -38,7 +35,7 @@ config['pyramid']['static_model'] = 'checkpoints/static_1_skirt'
 config['pyramid']['body_model'] = 'checkpoints/body_1_skirt_2_2'
 config['pyramid']['curvature_model'] = 'checkpoints/curv_1_skirt'
 
-config['dataset_in_memory'] = True
+config['dataset_in_memory'] = False
 config['resolution'] = None
 config['latent_dim'] = 128
 
@@ -68,7 +65,6 @@ config['signature_static'] = { 'uv_static': (512, 256, 3),
 config['pyramid']['is_label'] = False
 config['pyramid']['latent_dim'] = config['latent_dim']
 config['pyramid']['add_noise'] = True
-config['pyramid']['noise_amount'] = 0.001
 
 config['pyramid']['loss_weights'] = [1,1,1,1,1]
 

convnext/models/convnext_tf.py

@@ -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,
+                                     GlobalAveragePooling2D, Layer, Activation, Lambda,
                                      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.,
+                 drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1., learn_weights=False,
                  ):
         super().__init__()
         self.upsample_layers = []  # stem and 3 intermediate downsampling conv layers
@@ -255,12 +255,19 @@ class ConvNeXtReverse(tf.keras.Model):
             self.stages.append(stage)
             cur += depths[i]
 
-        self.last = Dense(3, activation='tanh')
+        if not learn_weights:
+            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
 

curvature_analysis.py

@@ -1,348 +0,0 @@
-import os
-import sys
-os.environ["CUDA_VISIBLE_DEVICES"] = '4'
-
-
-import tqdm
-import argparse
-import random
-import string
-import time
-import numpy as np
-import trimesh as tm
-import cmapy as cm
-import scipy as sp
-import scipy.io as sio
-import plotly.graph_objects as go
-from pyntcloud import PyntCloud
-from scipy.sparse import csr_matrix, coo_matrix
-from scipy.sparse.csgraph import floyd_warshall, dijkstra
-from plotly.subplots import make_subplots
-from custom_utils.custom_utils import _maskFaces
-from postprocessing import fix_collision
-
-from custom_utils.supersmpl_io import readPC2, readPC2Frame, writePC2, readOBJ
-from custom_utils.custom_utils import _maskFaces
-from auto_encoder_curv import dice_dense
-
-def postprocess(mesh, faces, mask=None, body=None, do_smoothing=True):
-    if body is not None:
-        mesh_fixed = fix_collision(mesh, body[np.newaxis,:,:], mask[np.newaxis,:])[0].numpy()[mask, :]
-    else:
-        mesh_fixed = mesh
-
-    if do_smoothing:
-        m = tm.Trimesh(mesh_fixed, faces, process=False, validate=False)
-        outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
-        outliers = np.nonzero(outliers)[0]
-        outliers_n_neighbours = get_closest_n_points(mesh_fixed, outliers, 2)
-        outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
-        faces_fixed = np.concatenate((faces, outliers_faces), axis=0)
-        m = tm.Trimesh(mesh_fixed, faces_fixed, process=False, validate=False)
-        tm.smoothing.filter_humphrey(m, iterations=1, alpha=0.6, beta=0.4)
-
-        mesh = m.vertices
-    else:
-        mesh = mesh_fixed
-
-    return mesh
-
-def export_to_ply(mesh, file):
-    ply = tm.exchange.ply.export_ply(mesh, encoding='binary', vertex_normal=True, include_attributes=True)
-    output_file = open(file, "wb+")
-    output_file.write(ply)
-    output_file.close()
-
-
-def get_closest_n_points(verts, idx, n):
-    dist = sp.spatial.distance.cdist(verts[idx, :], verts)
-    dist[dist == 0] = 1e9
-    idx_sort = np.argsort(dist, axis=-1)
-    closest = idx_sort[:, :n]
-    return closest
-
-
-def calculate_dist(folder, neighbourhood):
-    out = dict()
-    frames = sorted(os.listdir(os.path.join(folder, 'mesh_posed')))[int(args.start):int(args.end)]
-
-    for frame in tqdm.tqdm(frames):
-        id = frame.split('_')[0]
-        if id in out.keys():
-            continue
-
-        mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
-        k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
-        out[id] = k_neighbors
-
-    return out
-
-
-def histedges_equalN(x, nbin):
-    npt = len(x)
-    return np.interp(np.linspace(0, npt, nbin + 1),
-                     np.arange(npt),
-                     np.sort(x))
-
-
-def get_bins(values, nr_of_bins):
-    flattened = []
-    for c in values:
-        flattened.extend(c.flatten())
-    flattened = np.array(flattened)
-    bins = histedges_equalN(flattened, nr_of_bins)
-    return bins
-
-
-def generate_curvatures(v, faces, mask, neighbourhood=25, nr_of_labels=6):
-    # id = frame.split('_')[0]
-    # frame_nr = int(frame.split('_')[1].split('.')[0])
-
-    # mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
-    # _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
-    # v = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
-    
-    m = tm.Trimesh(v, faces, process=False, validate=False)
-
-    outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
-    outliers = np.nonzero(outliers)[0]
-    outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
-    outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
-    faces = np.concatenate((faces, outliers_faces), axis=0)
-
-    verts = v
-
-    m = tm.Trimesh(verts, faces, process=False, validate=False)
-    export_to_ply(m, temp_file)
-
-    cloud = PyntCloud.from_file(temp_file)
-
-    k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood]
-    # k_neighbors = cloud.get_neighbors(k=neighbourhood)
-    # tree = sp.spatial.cKDTree(verts)
-    # k_neighbors = tree.query(verts, 25)[1]
-    #k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
-    ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
-    cloud.add_scalar_field("curvature", ev=ev)
-    curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
-    
-    bins = np.load(f'custom_data/train_bins.npy')
-    # bins = get_bins(curvature, nr_of_labels)
-    labels = np.linspace(0, bins.shape[0] - 2, bins.shape[0] - 1)
-
-    frame_labels = np.zeros((curvature.shape[0]), dtype=np.float32)
-    for j in range(bins.shape[0] - 2):
-        frame_labels[(curvature >= bins[j]) * (curvature < bins[j+1])] = labels[j]
-    frame_labels[curvature >= bins[-2]] = labels[-1]
-       
-    return frame_labels, curvature
-
-import tensorflow as tf
-from configs.config_abl_overfit_jumpsuit_2 import config
-from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision, UV2MeshLabel
-
-from custom_utils.smpl import SMPL
-from models.cvae_model import CVAE
-
-uv2mesh = UV2Mesh(res=256, is_skirt=False, is_adaptive=False, is_smpl_orig=False)
-uv2meshlabel = UV2MeshLabel(res=256, is_skirt=False)
-
-def get_prediction(folder, frame, curv):
-    smpl = SMPL(use_one_hot=False, orig_smpl=False)
-    model = CVAE(config)
-    load_model(model, 'checkpoints/' + config['name'])
-
-    id = frame.split('_')[0]
-    frame_nr = frame.split('_')[1].split('.')[0]
-
-    mesh_mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
-    uv_mask = np.load(os.path.join(folder, 'uv_mask', f'{id}.npz'))['uv_mask'].astype(np.float32)  
-    pose_shape = np.load(os.path.join(folder, 'pose_shape', f'{id}_{frame_nr}.npy')).astype(np.float32)  
-    pose_shape[:3] = 0
-    uv_unposed = np.load(os.path.join(folder, 'uv_unposed_no_shape', f'{id}_{frame_nr}.npz'))['uv_unposed_no_shape']
-    uv_static = np.load(os.path.join(folder, 'uv_static', f'{id}.npz'))['uv_static']
-    uv_body_posed = np.load(os.path.join(folder, 'uv_posed_body_no_orient_2', f'{id}_{frame_nr}.npz'))['uv_posed_body_no_orient']
-
-    pred, _, _ = model([uv_unposed[None,:,:,:], uv_static[None,:,:,:], uv_body_posed[None,:,:,:], curv[None,:,:,:], uv_mask[None,:,:]], training=False)
-    mesh_pred = uv2mesh(pred)
-
-    shape = pose_shape[None, 72:]
-    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_mask[tf.newaxis,:,tf.newaxis]
-    mesh_pred_posed = smpl.run_garment(pose_shape[None, :72], pose_shape[None, 72:], mesh_pred_shaped)
-    
-    return mesh_pred_posed.numpy()[0,mesh_mask,:]
-
-
-def visualize_curvature_labels(folder, results_folder, frame):
-    id = frame.split('_')[0]
-    frame_nr = frame.split('_')[1].split('.')[0]
-
-    mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
-    _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
-    verts = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
-    pred = np.load(os.path.join(results_folder, frame + '.npy'))
-    pred = postprocess(pred, faces)
-
-    uv_curvature = np.load(os.path.join(folder, 'uv_curvature', f'{id}_{frame_nr}.npz'))['curvature_uv']
-    mesh_curvature = np.load(os.path.join(folder, 'mesh_curvature', f'{id}_{frame_nr}.npz'))['mesh_curvature'][mask]
-    mesh_curvature_raw = np.load(os.path.join(folder, 'mesh_curvature_raw', f'{id}_{frame_nr}.npz'))['mesh_curvature_raw'][mask]
-    uv_curvature = tf.one_hot(uv_curvature, 7)
-    pred_new = get_prediction(folder, frame, uv_curvature)
-    pred_new = postprocess(pred_new, faces)
-
-    curvature_labels_gt, curv_gt = generate_curvatures(verts, faces, mask, 25)
-    curvature_labels_pred, _ = generate_curvatures(pred, faces, mask, 25)
-    curvatures_new = (uv2meshlabel(uv_curvature[None,:,:,:])[0].numpy() * mask[:,None])[mask, :]
-    curvatures_pred_new, curv_gt_new = generate_curvatures(pred_new, faces, mask, 25)
-    dice = dice_dense(tf.one_hot(curvature_labels_pred, 7)[None,:], tf.one_hot(curvature_labels_gt, 7)[None,:])
-    dice_new = dice_dense(tf.one_hot(curvatures_pred_new, 7)[None,:], curvatures_new[None,:])
-    curvatures_new = curvatures_new.argmax(axis=-1) - 1
-
-    colors_binned_all = []
-
-    for curvature_labels in [curvature_labels_gt, curvature_labels_pred, curvatures_new, curvatures_pred_new]:
-        colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, int(curvature_labels.max()+1))])
-        colors_binned = np.zeros((curvature_labels.shape[0], 3))
-        for i in range(int(curvature_labels.max())+1):
-            # print(np.count_nonzero(curvature_labels == i))
-            colors_binned[curvature_labels == i, :] = colors[i]
-        colors_binned_all.append(colors_binned)
-
-    fig = make_subplots(rows=1, cols=4, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(4)] ])
-
-    fig.add_trace(go.Mesh3d(
-                # 8 vertices of a cube
-                x=verts[:,0],
-                y=verts[:,1],
-                z=verts[:,2],
-                # i, j and k give the vertices of triangles
-                i = faces[:,0],
-                j = faces[:,1],
-                k = faces[:,2],
-                vertexcolor=colors_binned_all[0]
-            ),
-            row=1, col=1)
-
-    fig.add_trace(go.Mesh3d(
-                # 8 vertices of a cube
-                x=-pred[:,0],
-                y=pred[:,2],
-                z=pred[:,1],
-                # i, j and k give the vertices of triangles
-                i = faces[:,0],
-                j = faces[:,1],
-                k = faces[:,2],
-                vertexcolor=colors_binned_all[1],
-                text=f'dice: {dice}'
-            ),
-            row=1, col=2)
-    
-    fig.add_trace(go.Mesh3d(
-                # 8 vertices of a cube
-                x=verts[:,0],
-                y=verts[:,1],
-                z=verts[:,2],
-                # i, j and k give the vertices of triangles
-                i = faces[:,0],
-                j = faces[:,1],
-                k = faces[:,2],
-                vertexcolor=colors_binned_all[2]
-            ),
-            row=1, col=3)
-
-    fig.add_trace(go.Mesh3d(
-                # 8 vertices of a cube
-                x=-pred_new[:,0],
-                y=pred_new[:,2],
-                z=pred_new[:,1],
-                # i, j and k give the vertices of triangles
-                i = faces[:,0],
-                j = faces[:,1],
-                k = faces[:,2],
-                vertexcolor=colors_binned_all[3],
-                text=f'dice: {dice_new}'
-            ),
-            row=1, col=4)
-
-    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),
-                    scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
-                    # scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
-                    )
-    helper = dict(xaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
-                yaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
-                zaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),)
-        
-    fig.update_layout(scene = helper,
-                        scene2 = helper,
-                        scene3 = helper,
-                        scene4 = helper,
-                        # scene5 = helper,
-                        )
-
-
-    # meshes = [go.Mesh3d(
-    #             x=x[:,0],
-    #             y=x[:,1],
-    #             z=x[:,2],
-    #             i = faces[:,0],
-    #             j = faces[:,1],
-    #             k = faces[:,2],
-    #             vertexcolor=colors_binned
-    #             )]
-
-    # fig = go.Figure(data=meshes)
-    # fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1))
-    # fig.update_layout(
-    #     scene = dict(
-    #         xaxis = dict(nticks=4, range=[-1,1],),
-    #                     yaxis = dict(nticks=4, range=[-1,1],),
-    #                     zaxis = dict(nticks=4, range=[-1,1],),)
-    #                     )
-
-    return fig, dice
-
-import wandb
-
-def init_wandb():
-    run = wandb.init(project='plots', name='review_5', dir='/home/workspace/local-data/temp/')
-    artifact = wandb.Artifact('code', type='code')
-    artifact.add_file('curvature_analysis.py')
-    run.log_artifact(artifact)
-
-if __name__ == '__main__':
-    # init_wandb()
-
-    is_skirt = False
-    skirt_modifier = '_skirt' if is_skirt else ''
-    F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
-    # k_neighbours(k=25)
-
-    folder = '/home/workspace/local-data/train'
-    results_folder = '/home/workspace/data/results/post/abl_overfit_jumpsuit_2-'
-
-    frames_file = 'frames_jumpsuit_30k.txt'
-    frames = []
-    with open(os.path.join(folder, frames_file), 'r') as f:
-        lines = f.readlines()
-        lines = [line.strip() for line in lines]
-    frames = lines
-
-    temp_file = '/home/workspace/local-data/temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
-    if not is_skirt:
-        dist = np.load('custom_data/dijkstra.npy')
-    else:
-        dist = np.load('custom_data/dijkstra_skirt.npy')
-
-    i = 0
-    while True:
-        # frame = np.random.choice(frames)
-        frame = '02806_129'
-        fig, dice = visualize_curvature_labels(folder, results_folder, frame)
-        wandb.log({f'{frame}-dice:{dice}': fig}, step=i)
-        i += 1
-        print(frame)

custom_utils/custom_utils.py

@@ -2,6 +2,7 @@ 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
 
@@ -216,6 +217,35 @@ 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)] ])
 

custom_utils/smpl.py

@@ -406,7 +406,8 @@ class SMPL:
         return T_out, J_out
 
 
-    def run_garment_with_weights(self, R, betas, v_cloth, weights, J=None):
+    def run_garment_with_weights(self, pose, betas, v_cloth, weights, J=None):
+        R = self.run_rodrigues(pose)
         
         if J is None:
             J = self.get_joints_template(betas)
@@ -430,13 +431,12 @@ 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.get_shape().as_list()[0], 1), dtype=tf.float32)), axis=1)
+            rest_shape_h = tf.concat((v_cloth, tf.ones((v_cloth.shape[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))

cvae.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_overfit_jumpsuit_2 import config
+from configs.config_abl_all_post import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -38,10 +38,11 @@ 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, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
+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 dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
-from models.cvae_model import CVAE
+from models.cvae_model import CVAENew as CVAE
+from models.gnn import Smoother
 
 
 def init_wandb():
@@ -62,6 +63,7 @@ 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):
@@ -71,9 +73,15 @@ 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:
@@ -82,9 +90,12 @@ def train_step(batch):
         else:
             mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
 
-        pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=True)
+        pred, mean, logvar, weights = 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:]
@@ -94,25 +105,31 @@ 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:
-            mesh_pred_posed = mesh_pred
+            shape = batch['pose_shape'][:, 72:]
+            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_loss
+        reconstr_loss = l2_mesh_loss + 1e-6 * weights_l
         if config['use_extra_losses']:
             if 'block_collision_loss' in config.keys() and config['block_collision_loss'] == True:
-                reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss) * config['reg_loss_coeff']
+                reconstr_loss = reconstr_loss + (e_loss) * config['reg_loss_coeff']
             else:
-                reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
+                reconstr_loss = reconstr_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']
 
@@ -122,14 +139,14 @@ 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) ** 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_posed) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
-    gradients = tape.gradient(loss, model.trainable_variables)
+    gradients = tape.gradient(loss, model.trainable_variables + smoother.trainable_variables + smoother_weights.trainable_variables)
-    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
+    optimizer.apply_gradients(zip(gradients, model.trainable_variables + smoother.trainable_variables + smoother_weights.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_loss': l2_loss, 'train/l2_mesh_loss': l2_mesh_loss, 'train/normal_loss': n_loss,
+             'train/l2_mesh_loss': l2_mesh_loss, 'train/normal_loss': n_loss, 'train/weights_loss': weights_l,
-             # 'train/ms_ssim': ms_ssim, 
+             # 'train/ms_ssim': ms_ssim, 'train/l2_loss': l2_loss, 
             }
 
 
@@ -140,9 +157,12 @@ def valid_step(batch):
     else:
         mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
 
-    pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
+    pred, mean, logvar, weights = 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:]
@@ -152,22 +172,28 @@ 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:
-        mesh_pred_posed = mesh_pred
+        shape = batch['pose_shape'][:, 72:]
+        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_loss
+    reconstr_loss = l2_mesh_loss + 1e-6 * weights_l
     if config['use_extra_losses']:
-        reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
+        reconstr_loss = reconstr_loss + (e_loss + c_loss) * config['reg_loss_coeff']
     if config['use_norm_loss']:
         reconstr_loss = reconstr_loss + n_loss * config['reg_loss_coeff']
 
@@ -177,12 +203,12 @@ 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) ** 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_posed) ** 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/mean': mean, 'valid/logvar': logvar, 'valid/kl_loss': kl, 'valid/var': np.e ** logvar, 'valid/weights_loss': weights_l,
               'valid/normal_loss': n_loss,# 'valid/ms_ssim': ms_ssim, 
               }
 
@@ -194,7 +220,7 @@ def test_step(batch):
     else:
         mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
     
-    pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
+    pred, mean, logvar, weights = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
 
     mesh_pred = uv2mesh(pred)
 
@@ -208,7 +234,7 @@ 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 = mesh_pred
+        mesh_pred_posed = smpl.run_garment_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred, weights)
         mesh_orig_posed = mesh_orig
     
     mesh_pred_posed_fixed = fix_collision(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
@@ -250,9 +276,12 @@ def plot_everything(batch: dict, type: str):
     else:
         mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
 
-    pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
+    pred, mean, logvar, weights = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
     
-    mesh_pred = uv2mesh(pred).numpy()
+    mesh_pred = uv2mesh(pred)
+    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']
@@ -260,6 +289,9 @@ 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)
@@ -277,7 +309,12 @@ 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:
-        mesh_posed = mesh_pred
+        shape = batch['pose_shape'][:, 72:]
+        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)
 
@@ -296,13 +333,16 @@ 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], 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], 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_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 = { 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],) 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, **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } }
+    return { **figs, **figs_coll, **figs_weights, **{ type + '/images': imgs, type + '/weight_images': weights_imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } }
 
 
 def sample(folder): 
@@ -476,7 +516,7 @@ def test():
 
 
 if __name__ == '__main__':
-    # init_wandb()
+    init_wandb()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str)
     args = vars(parser.parse_args())
@@ -517,6 +557,8 @@ 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
@@ -541,9 +583,10 @@ if __name__ == '__main__':
 
         train_loss = train_step(batch)
 
-        data_valid = valid_iterator.get_next()
+        if step % 25 == 0:
-        data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
+            data_valid = valid_iterator.get_next()
-        valid_loss = valid_step(data_valid)
+            data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
+            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):

cvae_pyramid.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_pyr_32_skirt import config
+from configs.post.config_abl_pyr_64_128_5 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -41,7 +41,7 @@ from losses import collision_loss as collision_penalty
 from custom_utils.custom_utils import batch_to_dict, load_model
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
-from models.pyramid_model_optimized import Pyramid
+from models.pyramid_model_optimized import PyramidNew as Pyramid
 from postprocessing import fix_collision
 
 
@@ -59,11 +59,9 @@ def init_wandb():
     artifact.add_file('models/cond_encoder.py')
     artifact.add_file('dataset.py')
     artifact.add_file('convnext/models/convnext_tf.py')
-    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs')]
+    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.post.')]
     for config_file_name in config_file_names:
-        file_name = '/'.join(config_file_name.split('.')) + '.py'
+        artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
-        if os.path.exists(file_name):
-            artifact.add_file(file_name)
     run.log_artifact(artifact)
 
 uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
@@ -210,7 +208,7 @@ def plot_3d(mesh_gt, mesh_pred_unposed_levels, faces, mesh_posed, collision, col
     return fig
 
 
-# @tf.function
+@tf.function
 def train_step(batch):
     levels_input, conds = group_inputs_for_levels(batch, config)
 
@@ -263,8 +261,8 @@ def train_step(batch):
         else:
             reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
 
-        # if config['use_norm_loss'] and step > 2000:
+        if config['use_norm_loss'] and step > 2000:
-        #     reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
+            reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
         
         kl_total += kl
 
@@ -336,8 +334,8 @@ def valid_step(batch):
     else:
         reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
     
-    # if config['use_norm_loss'] and step > 2000:
+    if config['use_norm_loss'] and step > 2000:
-    #     reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
+        reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
         
     kl_total += kl
 
@@ -733,7 +731,7 @@ def speed_step(levels_input, conds):
 
 
 if __name__ == '__main__':
-    # init_wandb()
+    init_wandb()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str, default='lhunor')
     args = vars(parser.parse_args())
@@ -792,7 +790,7 @@ if __name__ == '__main__':
     # generate_results()
 
     ckpt = tf.train.Checkpoint(model=model)
-    manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + config['name'], max_to_keep=20)
+    manager = tf.train.CheckpointManager(ckpt, 'checkpoints/post/' + config['name'], max_to_keep=20)
 
     # load_model(model, 'checkpoints/pyr_abl_32_skirt', iteration='180000')
 
@@ -809,18 +807,18 @@ if __name__ == '__main__':
         model.static_enc.trainable = False
         model.body_enc.trainable = False
         model.curv_enc.trainable = False
-        model.cond_encoder.trainable = False
+
 
     # load_model(model.levels[0].upscale_enhancer, config['pyramid']['levels'][0]['upscale_enhancer'])
     # model.levels[0].upscale_enhancer.trainable = False
 
 
-    for i in range(len(model.levels)-1):
+    for i in range(len(model.levels)):
         load_model(model.levels[i].upscale_enhancer, config['pyramid']['levels'][i]['upscale_enhancer'])
         model.levels[i].upscale_enhancer.trainable = False
-    if config['pyramid']['resolutions'][-1] != 512:
+    # if config['pyramid']['resolutions'][-1] != 512:
-        load_model(model.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
+    #     load_model(model.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
-        model.upscale_enhancer.trainable = False
+    #     model.upscale_enhancer.trainable = False
 
     for i in range(len(model.levels)-1):
         model.levels[i].trainable = False

docker/Dockerfile

@@ -2,9 +2,41 @@ FROM tensorflow/tensorflow:2.9.0-gpu
 
 RUN apt-get update
 RUN apt-get upgrade -y
+
+# Add the current user to the image
+ARG USER_NAME=lhunor
+ARG USER_ID=11017
+ARG GROUP_NAME=guest
+ARG GROUP_ID=11000
+
+# Update the system and install required packages
+RUN apt-get update && apt-get upgrade -y && apt-get install -y sudo git
+
+# Create a new group with the same GID as the host
+RUN groupadd --gid $GROUP_ID $GROUP_NAME
+
+# Create a new user with the same UID and GID as the host
+RUN useradd --uid $USER_ID --gid $GROUP_ID --shell /bin/bash --create-home $USER_NAME
+
+# don't require password with sudo, for convenience
+# not the safest thing to do, but hopefully okay inside the container
+RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
+
+# Add the new user to the sudo group
+RUN usermod -aG sudo $USER_NAME
+
+# Set the default user to the new user
+USER $USER_NAME
+
+WORKDIR //home-net
+
 RUN apt-get install git ffmpeg libsm6 libxext6 libopenexr-dev zip unzip htop nano -y
 RUN pip install numpy matplotlib opencv-python wandb OpenEXR tensorflow-graphics scipy plotly pandas tensorboardX opencv-python cmapy
 
+# defining the volumes that will be mounted
+VOLUME [ "/data-net" ]
+VOLUME [ "/data-local" ]
+VOLUME [ "/home-net" ]
 
-WORKDIR /home/workspace/data
+# Start the container with a bash shell
-# RUN wandb login
+CMD ["/bin/bash"]

evaluate_ae.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_overfit_jumpsuit_2 import config
+from configs.config_abl_all_post import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -39,15 +39,16 @@ 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, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
+from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, UV2MeshLabel, 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 models.cvae_model import CVAENew as CVAE
 from custom_utils.supersmpl_io import writeOBJ
 # from unif_metric import chamfer_and_score
 
 
 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):
@@ -56,9 +57,10 @@ def step(batch):
     else:
         uv_orig =  batch['uv_posed']
     
-    pred, _, _ = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
+    pred, _, _, weights = 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:]
@@ -68,7 +70,13 @@ 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:
-        mesh_pred_posed = mesh_pred
+        shape = batch['pose_shape'][:, 72:]
+        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'])
 
@@ -104,7 +112,7 @@ if __name__ == '__main__':
 
     iteration = ''
 
-    load_model(model, 'checkpoints/' + checkpoint_name, iteration=iteration)
+    load_model(model, 'checkpoints/post/' + checkpoint_name, iteration=iteration)
     # load_model(model, 'checkpoints/' + 'abl_all_120k')
 
     output_folder = '/home/workspace/data/results/post/' + checkpoint_name + '-' + iteration

models/base.py

@@ -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):
+    def __init__(self, depths, dims, kernel_size=7, is_label=False, learn_weights=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)
+            self.decoder = ConvNeXtReverse(depths=depths, dims=dims, kernel_size=kernel_size, learn_weights=learn_weights)
         else:
             self.decoder = ConvNeXtReverseLabels(depths=depths, dims=dims, nr_of_classes=7)
             

models/cvae_model.py

@@ -43,7 +43,7 @@ class CVAE(tf.keras.Model):
         self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
                                                  tf.keras.layers.Dense(256, activation='relu'),
                                                  tf.keras.layers.Dense(128, activation='relu'),
-                                                #  tf.keras.layers.Lambda(lambda x: x / 1000),
+                                                 tf.keras.layers.Lambda(lambda x: x / 1000),
                                                  tf.keras.layers.Dense(config['latent_dim'], activation='tanh')])
 
         self.encoder = VariationalEncoder(config['cvae']['latent_dim'], config['cvae']['encoder_depths'], config['cvae']['encoder_dims'], kernel_size=config['cvae']['kernel_size'])
@@ -181,6 +181,7 @@ 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))
 
@@ -193,12 +194,12 @@ 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 = not config['use_skinning']
+        self.use_offsets_over_body = False
 
         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):
@@ -214,6 +215,7 @@ 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,:,:,:]
@@ -223,7 +225,7 @@ class CVAENew(tf.keras.Model):
 
         x = x * uv_mask[:,:,:,tf.newaxis]
 
-        return x
+        return x, weights
 
 
     def reparameterize(self, mean, logvar, training, fix_noise=False):
@@ -272,6 +274,7 @@ 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,:,:,:]
@@ -281,5 +284,5 @@ class CVAENew(tf.keras.Model):
 
         x = x * uv_mask[:,:,:,tf.newaxis]
 
-        return x, mean, logvar
+        return x, mean, logvar, weights
 

models/gnn.py

@@ -0,0 +1,34 @@
+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