pyramid available with 128 res

cvae_pyramid.py

@@ -1,5 +1,5 @@
 import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+os.environ["CUDA_VISIBLE_DEVICES"] = "2"
 import tensorflow as tf
 
 gpus = tf.config.experimental.list_physical_devices('GPU')
@@ -37,7 +37,7 @@ from custom_utils.smpl import SMPL
 from models.cvae_pyramid_model import Pyramid
 
 
-name='cvae_pyr_1'
+name='cvae_pyr_1_d128'
 def init_wandb():
     run = wandb.init(project='vae', name=name,
             notes='learn offsets. smaller flattened latent vectors. with 25-85k pretraining. smaller batchsize. fixed balancing of reconst and kl loss for baseline. \
@@ -48,7 +48,7 @@ use mean l2 loss. small batch. l2 * 1000, balancing to 1 from 0.01\
 fixed gender issue and retrained body encoder\
 remove shape from unposed garment\
 training on 100 garments with offsets over the average of these\
-l2 loss on both 32 and full res, bottom predicts base, high res predicts offsets over this')
+l2 loss on both 128 and full res, bottom predicts base, high res predicts offsets over this')
     artifact = wandb.Artifact('code', type='code')
     artifact.add_file('cvae_pyramid.py')
     artifact.add_file('models/cvae_model.py')
@@ -60,9 +60,9 @@ l2 loss on both 32 and full res, bottom predicts base, high res predicts offsets
 
 uv2mesh = UV2Mesh(res=256)
 
-def plot_3d(mesh_gt, mesh_pred_unposed, mesh_pred_unposed_32, faces, collision, collision_faces, collision_color):
+def plot_3d(mesh_gt, mesh_pred_unposed, mesh_pred_unposed_low, faces, collision, collision_faces, collision_color):
     mesh_pred_unposed[:,2] += 1
-    mesh_pred_unposed_32[:,2] += 2
+    mesh_pred_unposed_low[:,2] += 2
     collision[:,2] += 3
     fig = go.Figure(data=[
         go.Mesh3d(
@@ -82,9 +82,9 @@ def plot_3d(mesh_gt, mesh_pred_unposed, mesh_pred_unposed_32, faces, collision,
             k = faces[:,2],
         ),
         go.Mesh3d(
-            x=mesh_pred_unposed_32[:,0],
+            x=mesh_pred_unposed_low[:,0],
-            y=mesh_pred_unposed_32[:,2],
+            y=mesh_pred_unposed_low[:,2],
-            z=mesh_pred_unposed_32[:,1],
+            z=mesh_pred_unposed_low[:,1],
             i = faces[:,0],
             j = faces[:,1],
             k = faces[:,2],
@@ -132,22 +132,22 @@ def train_step(batch):
 
     with tf.GradientTape() as tape:
         uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
-        uv_curvature_32 = tf.one_hot(batch['uv_curvature_32'], 7)
+        uv_curvature_low = tf.one_hot(batch['uv_curvature' + resolution_modifier], 7)
 
-        pred_full, mean_full, logvar_full, pred_32, mean_32, logvar_32, = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], uv_curvature, 
+        pred_full, mean_full, logvar_full, pred_low, mean_low, logvar_low, = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], uv_curvature, 
-                                                                                 batch['uv_unposed_32'], batch['uv_static_32'], batch['uv_body_posed_32'], uv_curvature_32])
+                                                                                 batch['uv_unposed' + resolution_modifier], batch['uv_static' + resolution_modifier], batch['uv_body_posed' + resolution_modifier], uv_curvature_low])
 
         mesh_pred_full = uv2mesh(pred_full)
         mesh_pred_posed_full = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_full)
-        mesh_pred_32 = uv2mesh(pred_32)
+        mesh_pred_low = uv2mesh(pred_low)
-        mesh_pred_posed_32 = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_32)
+        mesh_pred_posed_low = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_low)
         mesh_orig = batch['mesh_unposed']
 
         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_posed_full = mesh_pred_posed_full + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
-        mesh_pred_posed_32 = mesh_pred_posed_32 + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
+        mesh_pred_posed_low = mesh_pred_posed_low + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
 
         e_loss = tf.reduce_mean(edge_loss(mesh_pred_full, mesh_orig, batch['mesh_mask']))
         c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed_full, batch['mesh_body_posed'], batch['mesh_mask']))
@@ -156,16 +156,16 @@ def train_step(batch):
         ms_ssim = tf.image.ssim_multiscale(pred_full + 1, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=3)
         kl = kl_loss(mean_full, logvar_full)
 
-        e_loss_32 = tf.reduce_mean(edge_loss(mesh_pred_32, mesh_orig, batch['mesh_mask']))
+        e_loss_low = tf.reduce_mean(edge_loss(mesh_pred_low, mesh_orig, batch['mesh_mask']))
-        c_loss_32 = tf.reduce_mean(collision_penalty(mesh_pred_posed_32, batch['mesh_body_posed'], batch['mesh_mask']))
+        c_loss_low = tf.reduce_mean(collision_penalty(mesh_pred_posed_low, batch['mesh_body_posed'], batch['mesh_mask']))
-        l1_loss_32 = tf.reduce_sum(tf.reduce_sum((tf.math.abs(tf.transpose(batch['uv_unposed_32'], [0,2,3,1]) - pred_32)), axis=-1), axis=[1, 2])
+        l1_loss_low = tf.reduce_sum(tf.reduce_sum((tf.math.abs(tf.transpose(batch['uv_unposed' + resolution_modifier], [0,2,3,1]) - pred_low)), axis=-1), axis=[1, 2])
-        l2_loss_32 = 1e3 * tf.reduce_mean(tf.reduce_sum(((tf.transpose(batch['uv_unposed_32'], [0,2,3,1]) - pred_32) ** 2), axis=-1), axis=[1, 2])
+        l2_loss_low = 1e3 * tf.reduce_mean(tf.reduce_sum(((tf.transpose(batch['uv_unposed' + resolution_modifier], [0,2,3,1]) - pred_low) ** 2), axis=-1), axis=[1, 2])
-        ms_ssim_32 = tf.image.ssim(pred_32 + 1, tf.transpose(batch['uv_unposed_32'], [0,2,3,1]) + 1, max_val=3)
+        ms_ssim_low = tf.image.ssim(pred_low + 1, tf.transpose(batch['uv_unposed' + resolution_modifier], [0,2,3,1]) + 1, max_val=3)
-        kl_32 = kl_loss(mean_full, logvar_full)
+        kl_low = kl_loss(mean_full, logvar_full)
 
-        reconstr_loss = l2_loss + l2_loss_32
+        reconstr_loss = l2_loss + l2_loss_low
         reconstr_loss = tf.reduce_mean(reconstr_loss)
-        loss = reconstr_loss + balancing_coeff * (kl + kl_32)
+        loss = reconstr_loss + balancing_coeff * (kl + kl_low)
 
         ms_ssim = tf.reduce_mean(ms_ssim)
         l1_loss = tf.reduce_mean(l1_loss)
@@ -173,25 +173,24 @@ def train_step(batch):
         mean = tf.reduce_mean(mean_full)
         logvar = tf.reduce_mean(logvar_full)
 
-        ms_ssim_32 = tf.reduce_mean(ms_ssim_32)
+        ms_ssim_low = tf.reduce_mean(ms_ssim_low)
-        l1_loss_32 = tf.reduce_mean(l1_loss_32)
+        l1_loss_low = tf.reduce_mean(l1_loss_low)
-        l2_loss_32 = tf.reduce_mean(l2_loss_32)
+        l2_loss_low = tf.reduce_mean(l2_loss_low)
-        mean_32 = tf.reduce_mean(mean_32)
+        mean_low = tf.reduce_mean(mean_low)
-        logvar_32 = tf.reduce_mean(logvar_32)
+        logvar_low = tf.reduce_mean(logvar_low)
 
     mesh_orig_2 = uv2mesh(batch['uv_unposed'])
     metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred_full) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
-    metric_32 = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred_32) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
+    metric_low = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred_low) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
     metric_2 = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_2 - mesh_pred_full) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
     gradients = tape.gradient(loss, model.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/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim,
              'train/euclid_error_reconst': metric_2, 'train/reconstr_loss': reconstr_loss, 'train/mean': mean, 'train/logvar': logvar, 'train/kl_loss': kl,
              'train/var': np.e ** logvar, 'train/reg_coeff': reg_coeff, 'train/l2_loss': l2_loss,
-             'train/edge_loss_32': e_loss_32, 'train/collision_loss_32': c_loss_32, 'train/l1_loss_32': l1_loss_32, 'train/euclid_error_32': metric_32, 'train/ms_ssim_32': ms_ssim_32,
+             'train/edge_loss' + resolution_modifier: e_loss_low, 'train/collision_loss' + resolution_modifier: c_loss_low, 'train/l1_loss' + resolution_modifier: l1_loss_low, 'train/euclid_error' + resolution_modifier: metric_low, 'train/ms_ssim' + resolution_modifier: ms_ssim_low,
-             'train/mean_32': mean_32, 'train/logvar_32': logvar_32, 'train/kl_loss_32': kl_32,
+             'train/mean' + resolution_modifier: mean_low, 'train/logvar' + resolution_modifier: logvar_low, 'train/kl_loss' + resolution_modifier: kl_low,
-             'train/var_32': np.e ** logvar_32, 'train/l2_loss_32': l2_loss_32 }
+             'train/var' + resolution_modifier: np.e ** logvar_low, 'train/l2_loss' + resolution_modifier: l2_loss_low }
-        
 
 
 @tf.function
@@ -235,26 +234,26 @@ def valid_step(batch):
 
 def plot_everything(batch: dict, type: str):
     uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
-    uv_curvature_32 = tf.one_hot(batch['uv_curvature_32'], 7)
+    uv_curvature_low = tf.one_hot(batch['uv_curvature' + resolution_modifier], 7)
-    pred_full, mean_full, logvar_full, pred_32, mean_32, logvar_32, = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], uv_curvature, 
+    pred_full, mean_full, logvar_full, pred_low, mean_low, logvar_low, = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], uv_curvature, 
-                                                                             batch['uv_unposed_32'], batch['uv_static_32'], batch['uv_body_posed_32'], uv_curvature_32])
+                                                                             batch['uv_unposed' + resolution_modifier], batch['uv_static' + resolution_modifier], batch['uv_body_posed' + resolution_modifier], uv_curvature_low])
 
     mesh_pred_full = uv2mesh(pred_full)
-    mesh_pred_32 = uv2mesh(pred_32)
+    mesh_pred_low = uv2mesh(pred_low)
     mesh_orig = batch['mesh_unposed']
     
     orig_img = tf.transpose(batch['uv_unposed'], [0,2,3,1])
     uv_static_img = tf.transpose(batch['uv_static'], [0,2,3,1])
     uv_body_posed_img = tf.transpose(batch['uv_body_posed'], [0,2,3,1])
     pred_img = pred_full
-    pred_32_up = tf.image.resize(pred_32, (512, 256), tf.image.ResizeMethod.LANCZOS5)
+    pred_low_up = tf.image.resize(pred_low, (512, 256), tf.image.ResizeMethod.LANCZOS5)
-    img = tf.concat((uv_static_img, uv_body_posed_img, orig_img, pred_img, pred_32_up), axis=2)
+    img = tf.concat((uv_static_img, uv_body_posed_img, orig_img, pred_img, pred_low_up), axis=2)
     imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)]
 
     F = np.load("custom_utils/smpl_faces.npy")
     mask = batch['mesh_mask'].numpy().astype(bool)
     garment_unposed, faces = maskFacesBatch(mesh_pred_full.numpy(), F, mask)
-    garment_unposed_32, faces = maskFacesBatch(mesh_pred_32.numpy(), F, mask)
+    garment_unposed_low, faces = maskFacesBatch(mesh_pred_low.numpy(), F, mask)
     gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred_full.shape[0])]
     
     body = batch['mesh_body_posed'].numpy()
@@ -269,7 +268,7 @@ def plot_everything(batch: dict, type: str):
     collision_faces = [np.concatenate((F, faces[i] + 14475), axis=0) for i in range(len(garment_posed))]
     collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(garment_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(garment_posed))]
 
-    figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], garment_unposed_32[i], faces[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) }
+    figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], garment_unposed_low[i], faces[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) }
     
     mean_hist = wandb.Histogram(sequence=mean_full.numpy().mean(axis=0))
     logvar_hist = wandb.Histogram(sequence=logvar_full.numpy().mean(axis=0))
@@ -277,8 +276,6 @@ def plot_everything(batch: dict, type: str):
     return { **figs, **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } }
 
 
-
-
 def sample(folder):
     # run = wandb.init(project='vae_plots', name=folder.split('/')[-1])
     load_model(model,folder)
@@ -400,6 +397,7 @@ def sample(folder):
 
         print()
 
+
 if __name__ == '__main__':
     init_wandb()
     allow_memory_growth()
@@ -408,6 +406,8 @@ if __name__ == '__main__':
     args = vars(parser.parse_args())
 
     batch_size = 4
+    low_resolution = 128
+    resolution_modifier = '_' + str(low_resolution)
     
     smpl = SMPL()
 
@@ -416,31 +416,30 @@ if __name__ == '__main__':
                           'pose_shape': (72 + 11,),
                         #   'uv_posed': (3, 512, 256,),
                           'uv_body_posed': (3, 512, 256,),
-                          'uv_body_posed_32': (3, 32, 16,),
+                          'uv_body_posed' + resolution_modifier: (3, low_resolution, low_resolution//2,),
                           'mesh_body_posed': (14475, 3,),
                           'mesh_posed': (14475, 3,),
                           'mesh_unposed': (14475, 3,),
                           'uv_unposed': (3, 512, 256,),
-                          'uv_unposed_32': (3, 32, 16,),
+                          'uv_unposed' + resolution_modifier: (3, low_resolution, low_resolution//2,),
                           'uv_curvature': (512, 256),
-                          'uv_curvature_32': (32, 16),
+                          'uv_curvature' + resolution_modifier: (low_resolution, low_resolution//2),
                         }
     signature_static = { 'uv_static': (3, 512, 256,),
-                         'uv_static_32': (3, 32, 16,),
+                         'uv_static' + resolution_modifier: (3, low_resolution, low_resolution//2,),
                         #  'mesh_static': (14475, 3,),
                          'mesh_mask': (14475,),
                          'uv_mask': (512, 256,),
-                         'uv_shaped_body': (3, 512, 256,),
+                        #  'uv_shaped_body': (3, 512, 256,),
                         }
 
-    dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train_100', batch_size=batch_size, signature=signature_dynamic, use_filled=True, resolutions=[512, 32])
+    dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train_100', batch_size=batch_size, signature=signature_dynamic, use_filled=True, resolutions=[512, low_resolution])
-    static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train_100', signature=signature_static, use_filled=True, resolutions=[512, 32])
+    static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train_100', signature=signature_static, use_filled=True, resolutions=[512, low_resolution])
     dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/valid', batch_size=batch_size, signature=signature_dynamic)
     static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid', signature=signature_static)
 
 
-    model = Pyramid(latent_dim=128)
+    model = Pyramid(latent_dim=128, low_res=low_resolution)
-
 
     # lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.0001, decay_steps=30000, decay_rate=0.1, staircase=False)
     optimizer = tf.optimizers.Adam(learning_rate=0.0001)

dataset.py

@@ -61,6 +61,8 @@ class StaticDataset():
         assert set(resolutions).issubset(set([32, 128, 512]))
         self.possible_keys = ['uv_static', 'uv_static_32', 'uv_static_128' 'mesh_static', 'mesh_mask', 'uv_mask', 'uv_shaped_body']
         self.keys = [value for value in signature.keys() if value in self.possible_keys]
+        if 'uv_static_128' in signature.keys():
+            self.keys.append('uv_static_128')
         self.resolutions = resolutions
         self.filled = ''
         if use_filled:

models/ae_curv_model.py

@@ -128,3 +128,54 @@ class AutoEncoder32(tf.keras.Model):
         # x = x * uv_mask[:,:,:,tf.newaxis]
 
         return x
+
+
+
+class Decoder128(tf.keras.Model):
+    
+    def __init__(self, bottleneck_size, latent_dim):
+        super(Decoder128, self).__init__()
+
+        self.dense = tf.keras.layers.Dense(4 * 2 * bottleneck_size)
+        self.reshape = tf.keras.layers.Reshape((4, 2, bottleneck_size))
+        self.decoder = ConvNeXtReverseLabels(depths=list(reversed([16, 16, 9, 9])), dims=list(reversed([bottleneck_size, 256, 128, 64])), nr_of_classes=7)
+
+    def call(self, inputs, training=None):
+        x = inputs
+
+        x = self.dense(x)
+        x = self.reshape(x)
+        x = self.decoder(x)
+
+        return x
+
+        
+class AutoEncoder128(tf.keras.Model):
+    
+    def __init__(self):
+        super(AutoEncoder128, self).__init__()
+
+        bottleneck_size = 512
+        latent_dim = 128
+
+        self.encoder = ConvNeXt(depths=[16, 16, 9, 9], dims=[64, 128, 256, bottleneck_size], include_top=False)
+        self.bottleneck = Bottleneck(bottleneck_size, latent_dim)
+        self.decoder = Decoder128(bottleneck_size, latent_dim)
+
+
+    def call(self, inputs, training=None):
+        x, uv_mask = inputs
+
+        # x = tf.transpose(x, [0,2,3,1])
+        x = self.encoder(x, training=training)
+
+        x = self.bottleneck(x, training=training)
+
+        x = self.decoder(x, training=training)
+        # x = tf.transpose(x, [0,3,1,2])
+
+        # x = x * uv_mask[:,:, tf.newaxis]
+        # x = x * uv_mask[:,tf.newaxis,:,:]
+        # x = x * uv_mask[:,:,:,tf.newaxis]
+
+        return x

models/ae_static_model.py

@@ -121,3 +121,52 @@ class AutoEncoder32(tf.keras.Model):
         # x = x * uv_mask[:,tf.newaxis,:,:]
 
         return x
+
+
+class Decoder128(tf.keras.Model):
+    
+    def __init__(self, bottleneck_size, latent_dim):
+        super(Decoder128, self).__init__()
+
+        self.dense = tf.keras.layers.Dense(4 * 2 * bottleneck_size)
+        self.reshape = tf.keras.layers.Reshape((4, 2, bottleneck_size))
+        self.decoder = ConvNeXtReverse(depths=list(reversed([16, 16, 9, 9])), dims=list(reversed([bottleneck_size, 256, 128, 64])))
+
+    def call(self, inputs, training=None):
+        x = inputs
+
+        x = self.dense(x)
+        x = self.reshape(x)
+        x = self.decoder(x)
+
+        return x
+
+
+class AutoEncoder128(tf.keras.Model):
+    
+    def __init__(self):
+        super(AutoEncoder128, self).__init__()
+
+
+        bottleneck_size = 512
+        latent_dim = 128
+
+        self.encoder = ConvNeXt(depths=[16, 16, 9, 9], dims=[64, 128, 256, bottleneck_size], include_top=False)
+        self.bottleneck = Bottleneck(bottleneck_size, latent_dim)
+        self.decoder = Decoder128(bottleneck_size, latent_dim)
+
+    def call(self, inputs, training=None):
+        x = inputs
+
+        x = tf.transpose(x, [0,2,3,1])
+        x = self.encoder(x, training=training)
+
+        x = self.bottleneck(x, training=training)
+
+        x = self.decoder(x, training=training)
+        x = tf.transpose(x, [0,3,1,2])
+
+        # x = x * uv_mask[:,:, tf.newaxis]
+        # x = x * uv_mask[:,tf.newaxis,:,:]
+
+        return x

models/cvae_model.py

@@ -15,6 +15,10 @@ from models.ae_static_model import AutoEncoder as StaticAE
 from models.ae_body_model import AutoEncoder32 as BodyAE32
 from models.ae_curv_model import AutoEncoder32 as CurvatureAE32
 from models.ae_static_model import AutoEncoder32 as StaticAE32
+from models.ae_body_model import AutoEncoder128 as BodyAE128
+from models.ae_curv_model import AutoEncoder128 as CurvatureAE128
+from models.ae_static_model import AutoEncoder128 as StaticAE128
+
 
 class Bottleneck(tf.keras.Model):
     
@@ -339,6 +343,177 @@ class CVAE32(tf.keras.Model):
         return logits
 
 
+    def call(self, inputs, training=None, zero_latent=False):
+        uv_unposed, uv_template, uv_body_posed, uv_curv = inputs
+        uv_unposed, uv_template, uv_body_posed = tf.transpose(uv_unposed, [0,2,3,1]), tf.transpose(uv_template, [0,2,3,1]), tf.transpose(uv_body_posed, [0,2,3,1])
+        z_static = self.static_enc(uv_template)
+        z_body = self.body_enc(uv_body_posed)
+        z_curv = self.curv_enc(uv_curv)
+
+        x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
+
+        mean, logvar = self.encoder(x)
+        z = self.reparameterize(mean, logvar)
+        z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
+        z_cond = self.cond_encoder(z_cond)
+
+        if zero_latent:
+            z = tf.zeros_like(z)
+
+        z = tf.concat([z, z_cond], axis=-1)
+
+        # z = self.shuffle(z)
+
+        # z = tf.concat([z, z_curv], axis=-1)
+        x = self.decoder(z)
+
+
+        # x = tf.transpose(x, [0,3,1,2])
+        # x = x * uv_mask[:,tf.newaxis,:,:]
+        # x = tf.image.resize(x, (512, 256), tf.image.ResizeMethod.LANCZOS5)
+        # x = x + tf.transpose(uv_shaped_body, [0,2,3,1])
+        if self.avg_garment is not None:
+            x = x + self.avg_garment[tf.newaxis,:,:,:]
+        # x = x * uv_mask[:,:,:,tf.newaxis]
+
+        return x, mean, logvar, z
+
+
+
+
+class Encoder128(tf.keras.Model):
+    
+    def __init__(self, latent_dim):
+        super(Encoder128, self).__init__()
+
+        self.encoder = ConvNeXt(depths=[16, 16, 9, 9], dims=[256, 256, 128, 64], include_top=False)
+        self.flatten = tf.keras.layers.Flatten()
+        self.mean = tf.keras.layers.Dense(latent_dim)
+        self.log_var = tf.keras.layers.Dense(latent_dim)
+
+    def call(self, inputs, training=None):
+        x = inputs
+
+        x = self.encoder(x)
+        x = self.flatten(x)
+        mean = self.mean(x)
+        log_var = self.log_var(x)
+
+        return mean, log_var
+
+
+class Decoder128(tf.keras.Model):
+    
+    def __init__(self):
+        super(Decoder128, self).__init__()
+
+        self.dense = tf.keras.layers.Dense(4 * 2 * 128)
+        self.reshape = tf.keras.layers.Reshape((4, 2, 128))
+        self.decoder = ConvNeXtReverse(depths=list(reversed([16, 16, 9, 9])), dims=list(reversed([256, 256, 128, 64])))
+
+
+    def call(self, inputs, training=None):
+        x = inputs
+
+        x = self.dense(x)
+        x = self.reshape(x)
+        x = self.decoder(x)
+
+        return x
+
+
+class CVAE128(tf.keras.Model):
+    
+    def __init__(self, latent_dim=128,
+                 avg_garment_path='',
+                 static_model='',
+                 body_model='',
+                 curvature_model='', 
+                 freeze_encoders=False):
+        super(CVAE128, self).__init__()
+        self.latent_dim = latent_dim
+
+        model : StaticAE128 = StaticAE128()
+        if static_model != '':
+            load_model(model, static_model)
+        self.static_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
+
+        model : BodyAE128 = BodyAE128()
+        if body_model != '':
+            load_model(model, body_model)
+        self.body_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
+
+        model : CurvatureAE128 = CurvatureAE128()
+        if curvature_model != '':
+            load_model(model, curvature_model)
+        self.curv_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
+        
+        if freeze_encoders:
+            self.static_enc.trainable = False
+            self.body_enc.trainable = False
+            self.curv_enc.trainable = False
+
+        self.encoder = Encoder128(latent_dim)
+        self.decoder = Decoder128()
+
+        if avg_garment_path != '':
+            self.avg_garment = np.load(avg_garment_path)
+        else:
+            self.avg_garment = None
+
+        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.Dense(latent_dim),])
+
+
+    # @tf.function
+    def sample(self, uv_template, uv_body_posed, uv_curv, uv_mask, nr_of_samples=4, fix_noise=False, zero_latent=False):
+        if not fix_noise:
+            eps = tf.random.normal(shape=(nr_of_samples, self.latent_dim))
+        else:
+            eps = tf.random.normal(shape=(1, self.latent_dim))
+            eps = tf.tile(eps, [nr_of_samples,1])
+
+        uv_template = tf.tile(tf.transpose(uv_template, [1,2,0])[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
+        uv_body_posed = tf.tile(tf.transpose(uv_body_posed, [1,2,0])[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
+        if len(uv_curv.shape) == 3:
+            uv_curv = tf.tile(uv_curv[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
+        uv_mask = tf.tile(uv_mask[tf.newaxis,:,:], [nr_of_samples,1,1])
+
+        # uv_template, uv_body_posed = tf.transpose(uv_template, [0,2,3,1]), tf.transpose(uv_body_posed, [0,2,3,1])
+        z_static = self.static_enc(uv_template)
+        z_body = self.body_enc(uv_body_posed)
+        z_curv = self.curv_enc(uv_curv)
+
+        z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
+        z_cond = self.cond_encoder(z_cond)
+
+        if zero_latent:
+            eps = tf.zeros_like(eps)
+
+        z = tf.concat([eps, z_cond], axis=-1)
+
+        x = self.decoder(z)
+        x = x + self.avg_garment[tf.newaxis,:,:,:]
+        x = x * uv_mask[:,:,:,tf.newaxis]
+
+        return x
+
+
+    def reparameterize(self, mean, logvar):
+        eps = tf.random.normal(shape=mean.shape)
+        return eps * tf.exp(logvar * .5) + mean
+
+
+    def decode(self, z, apply_sigmoid=False):
+        logits = self.decoder(z)
+        if apply_sigmoid:
+            probs = tf.sigmoid(logits)
+            return probs
+        return logits
+
+
     def call(self, inputs, training=None, zero_latent=False):
         uv_unposed, uv_template, uv_body_posed, uv_curv = inputs
         uv_unposed, uv_template, uv_body_posed = tf.transpose(uv_unposed, [0,2,3,1]), tf.transpose(uv_template, [0,2,3,1]), tf.transpose(uv_body_posed, [0,2,3,1])

models/cvae_pyramid_model.py

@@ -7,15 +7,18 @@ import numpy as np
 # import plotly.express as ex
 
 from custom_utils.custom_utils import load_model
-from models.cvae_model import CVAE, CVAE32
+from models.cvae_model import CVAE, CVAE128, CVAE32
 
 
 class Pyramid(tf.keras.Model):
     
-    def __init__(self, latent_dim=128, avg_garment_path=''):
+    def __init__(self, low_res=32, latent_dim=128, avg_garment_path=''):
         super(Pyramid, self).__init__()
 
-        self.model_32 = CVAE32(latent_dim=latent_dim)
+        if low_res == 32:
+            self.model_low = CVAE32(latent_dim=latent_dim)
+        if low_res == 128:
+            self.model_low = CVAE128(latent_dim=latent_dim)
         self.model_full = CVAE(latent_dim=latent_dim)
         
         if avg_garment_path != '':
@@ -24,16 +27,16 @@ class Pyramid(tf.keras.Model):
             self.avg_garment = None
 
     def call(self, inputs, training=None):
-        uv_unposed, uv_static, uv_body_posed, uv_curv, uv_unposed_32, uv_static_32, uv_body_posed_32, uv_curv_32 = inputs
+        uv_unposed, uv_static, uv_body_posed, uv_curv, uv_unposed_low, uv_static_low, uv_body_posed_low, uv_curv_low = inputs
 
-        pred_32, mean_32, logvar_32, _ = self.model_32([uv_unposed_32, uv_static_32, uv_body_posed_32, uv_curv_32], training=training)
+        pred_low, mean_low, logvar_low, _ = self.model_low([uv_unposed_low, uv_static_low, uv_body_posed_low, uv_curv_low], training=training)
 
-        x = tf.image.resize(pred_32, (512, 256), tf.image.ResizeMethod.LANCZOS5)
+        x = tf.image.resize(pred_low, (512, 256), tf.image.ResizeMethod.LANCZOS5)
 
         pred_full, mean_full, logvar_full, _ = self.model_full([uv_unposed, uv_static, uv_body_posed, uv_curv], training=training)
 
         x = x + pred_full
         
-        return x, mean_full, logvar_full, pred_32, mean_32, logvar_32,
+        return x, mean_full, logvar_full, pred_low, mean_low, logvar_low,