upscaling pipeline

data/dataset.py

@@ -30,14 +30,15 @@ def read_frames(folder, shuffle=True, is_cape=False, testing=False, frames_file=
 
         if is_cape:
             frames = ['.'.join(frame.split('.')[:-1]) for frame in frames] # .split('_')[0]
+            frames = [Frame(frame.split('.')[0], frame.split('.')[1]) for frame in frames]
         else:
             frames = [frame.split('.')[0] for frame in frames] # .split('_')[0]
+            frames = [Frame(frame.split('_')[0], frame.split('_')[1]) for frame in frames]
 
-        if testing:
+        # if testing:
-            frames = sorted(os.listdir(os.path.join(folder, 'mesh_static') ))
+        #     frames = sorted(os.listdir(os.path.join(folder, 'mesh_static') ))
-            frames = [frame.split('.')[0] + '_4' for frame in frames]
+        #     frames = [frame.split('.')[0] + '_4' for frame in frames]
 
-        frames = [Frame(frame.split('_')[0], frame.split('_')[1]) for frame in frames]
 
     if frames_file is not None:
         frames = []
@@ -65,12 +66,12 @@ class StaticCache():
         
         if is_cape:
             self.uv_masks = dict()
-            uv_mask = np.load('custom_data/garment_uv_mask_full.npy').astype(np.int32)
+            uv_mask = torch.tensor(np.load('custom_data/garment_uv_mask_full.npy').astype(np.int32), device=device)
-            self.uv_masks[512] = uv_mask.astype(np.float32)
+            self.uv_masks[512] = uv_mask.to(torch.float32).cpu().numpy()
             for res in [32, 64, 128, 256]:
-                down = Resize(uv_mask[:,:,None], size=(res, int(res/2)), interpolation=InterpolationMode.NEAREST)
+                down = Resize(size=(res, int(res/2)), interpolation=InterpolationMode.NEAREST)(uv_mask[None,:,:])
-                down = np.squeeze(down.numpy().astype(np.float32))
+                down = torch.squeeze(down).to(torch.float32)
-                self.uv_masks[res] = down
+                self.uv_masks[res] = down.cpu().numpy()
 
 
     def read(self, id):
@@ -127,7 +128,7 @@ class Dataset(Dataset):
         self.frames = read_frames(folder, frames_file=frames_file, is_cape=is_cape, shuffle=True)
 
         self.body_faces = np.load('smpl/smpl_faces.npy')
-        self.static_cache = StaticCache(folder, signature, is_cape=is_cape)
+        self.static_cache = StaticCache(folder, signature, is_cape=is_cape, resolutions=resolutions, device=device)
 
         self.cache = dict() if use_cache else None
 

models/upscaler.py

@@ -1,172 +1,35 @@
-import tensorflow as tf
+import torch
-from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block, ConvNeXtReverseWithSkip, ConvNeXtWithSkip
+import torch.nn as nn
 
-
+class Upscaler(nn.Module):
-class Bottleneck(tf.keras.Model):
+    def __init__(self, low_res=32, kernel_size=3):
-    
-    def __init__(self, dim, kernel_size=7):
-        super(Bottleneck, self).__init__()
-
-        self.bottleneck = tf.keras.Sequential(
-                [Block(dim=dim, drop_path=0, kernel_size=kernel_size,
-                       layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)]
-            )
-
-    def call(self, inputs, training):
-        x = inputs
-
-        x = self.bottleneck(x, training=training)
-        
-        return x
-
-
-
-class Upscaler(tf.keras.Model):
-    
-    def __init__(self, bottleneck_size):
         super(Upscaler, self).__init__()
 
-        depths = [27, 27]
-        dims = [512, 512]
-        reverse_dims = [16] + dims[:-1]
-        self.encoder = ConvNeXtWithSkip(depths=depths, dims=dims, kernel_size=3)
-
-        self.bottleneck = Bottleneck(dims[-1], kernel_size=3)
-
-        self.decoder = ConvNeXtReverseWithSkip(depths=depths, dims=reverse_dims, kernel_size=3)
-
-    def call(self, inputs, training):
-        img, mask = inputs
-
-        img = tf.transpose(img, [0,2,3,1])
-
-        x, skip_features = self.encoder(img, training=training)
-
-        x = self.bottleneck(x, training=training)
-
-        x = self.decoder([x, skip_features], training=training)
-
-        x = x * (1 - mask[:,:,:,tf.newaxis])
-        x = x + img
-        x = tf.image.resize(x, size=(512, 256), method=tf.image.ResizeMethod.LANCZOS5)
-
-        return x
-
-
-class UpscalerBasic(tf.keras.Model):
-    
-    def __init__(self, bottleneck_size, low_res=32, high_res=64):
-        super(UpscalerBasic, self).__init__()
-
-        self.low_res = low_res
-        self.high_res = high_res
-        self.flatten = tf.keras.layers.Flatten()
-
-        self.seq = tf.keras.Sequential([
-                    tf.keras.layers.Dense(1024, activation='relu'),
-                    tf.keras.layers.BatchNormalization(),
-                    tf.keras.layers.Dense(1024, activation='relu'),
-                    tf.keras.layers.BatchNormalization(),
-                    tf.keras.layers.Dense(1024, activation='relu'),
-                    tf.keras.layers.BatchNormalization(),
-                    tf.keras.layers.Dense(low_res * (low_res//2) * 3),
-        ])
-
-        self.reshape = tf.keras.layers.Reshape((low_res, (low_res//2), 3))
-
-    def call(self, inputs, training):
-        img, mask = inputs
-
-        x = self.flatten(img)
-        x = self.seq(x, training=training)
-        x = self.reshape(x)
-
-        x = x * (1 - mask[:,:,:,tf.newaxis])
-        x = x + img
-        x = tf.image.resize(x, size=(self.high_res, self.high_res//2), method=tf.image.ResizeMethod.LANCZOS5)
-
-        return x
-
-
-
-
-class UpscalerBasicNew(tf.keras.Model):
-    
-    def __init__(self, low_res=32):
-        super(UpscalerBasicNew, self).__init__()
-
-        self.low_res = low_res
-        self.flatten = tf.keras.layers.Flatten(name='upscale_enhancer_flattent')
-
-        self.seq = tf.keras.Sequential([
-                    tf.keras.layers.Dense(4096, activation='relu', name='upscale_enhancer_dense_1'),
-                    tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_1'),
-                    tf.keras.layers.Dense(2048, activation='relu', name='upscale_enhancer_dense_2'),
-                    tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_2'),
-                    tf.keras.layers.Dense(2048, activation='relu', name='upscale_enhancer_dense_3'),
-                    tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_3'),
-                    tf.keras.layers.Dense(low_res * (low_res//2) * 3, activation='tanh', name='upscale_enhancer_dense_final'),
-        ])
-
-        self.reshape = tf.keras.layers.Reshape((low_res, (low_res//2), 3), name='upscale_enhancer_reshape')
-
-    def call(self, inputs, training):
-        img, mask = inputs
-
-        x = self.flatten(img)
-        x = self.seq(x, training=training)
-        x = self.reshape(x)
-
-        x = x * (1 - mask[:,:,:,tf.newaxis])
-        x = x + img
-
-        return x
-
-
-class UpscalerBasicConv(tf.keras.Model):
-    
-    def __init__(self, low_res=32, kernel_size=3):
-        super(UpscalerBasicConv, self).__init__()
-
         self.low_res = low_res
 
-        self.seq = tf.keras.Sequential([
+        self.seq = nn.Sequential(
-                    tf.keras.layers.Conv2D(256, kernel_size=kernel_size, padding='same', activation='relu', name='upscale_enhancer_conv_1'),
+            nn.Conv2d(3, 256, kernel_size=kernel_size, padding='same'),
-                    tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_1'),
+            nn.ReLU(),
-                    tf.keras.layers.Conv2D(128, kernel_size=kernel_size, padding='same', activation='relu', name='upscale_enhancer_conv_2'),
+            nn.BatchNorm2d(256),
-                    tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_2'),
+            nn.Conv2d(256, 128, kernel_size=kernel_size, padding='same'),
-                    tf.keras.layers.Conv2D(64, kernel_size=kernel_size, padding='same', activation='relu', name='upscale_enhancer_conv_3'),
+            nn.ReLU(),
-                    tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_3'),
+            nn.BatchNorm2d(128),
-                    tf.keras.layers.Conv2D(32, kernel_size=kernel_size, padding='same', activation='relu', name='upscale_enhancer_conv_4'),
+            nn.Conv2d(128, 64, kernel_size=kernel_size, padding='same'),
-                    tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_4'),
+            nn.ReLU(),
-                    tf.keras.layers.Dense(3, activation='tanh', name='upscale_enhancer_dense_final'),
+            nn.BatchNorm2d(64),
-        ])
+            nn.Conv2d(64, 32, kernel_size=kernel_size, padding='same'),
+            nn.ReLU(),
+            nn.BatchNorm2d(32),
+            nn.Conv2d(32, 3, kernel_size=kernel_size, padding='same'),
+            nn.Tanh()
+        )
 
-
+    def forward(self, inputs) -> torch.Tensor:
-    def call(self, inputs, training):
         img, mask = inputs
 
-        x = self.seq(img, training=training)
+        x = self.seq(img)
 
-        x = x * (1 - mask[:,:,:,tf.newaxis])
+        x = x * (1 - mask.unsqueeze(1))
         x = x + img
 
         return x
-
-# class Upscaler(tf.keras.Model):
-    
-#     def __init__(self, bottleneck_size):
-#         super(Upscaler, self).__init__()
-
-#         self.decoder = ConvNeXtReverse(depths=list(reversed([9, 9, 9])), dims=list(reversed([bottleneck_size, 128, 32, 16])))
-
-#     def call(self, inputs, training):
-#         x, mask = inputs
-
-#         x = tf.transpose(x, [0,2,3,1])
-#         x = self.decoder(x)
-
-#         x = x * mask[:,:,:,tf.newaxis]
-
-#         return x
-

scripts/upscaler_train.py

@@ -0,0 +1,284 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+
+import importlib
+import argparse
+parser = argparse.ArgumentParser(description='')
+parser.add_argument('--name', type=str)
+parser.add_argument('--config', type=str)
+parser.add_argument('--debug', action='store_true')
+args = vars(parser.parse_args())
+
+os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = '7'
+
+
+import time
+import argparse
+import wandb
+import random
+import numpy as np
+import torch
+# from torchmetrics import MultiScaleStructuralSimilarityIndexMeasure as MSSSIM
+from custom_utils.custom_utils import UV2Mesh, get_summary_html, maskFacesBatch, get_summary_str
+import plotly.graph_objects as go
+import plotly.express as ex
+import torch.optim as optim
+import torch.nn.functional as F
+
+from data.dataset import Dataset
+# from torch.utils.data import DataLoader
+from custom_utils.custom_utils import plot_3d
+from models.upscaler import Upscaler
+from utils.checkpoints import CheckpointSaver, load_model
+from custom_utils.custom_utils import Accumulator
+from utils.utils import InfiniteDataLoader as DataLoader
+
+from pytorch_msssim import MS_SSIM
+
+
+low_res = 32
+is_skirt = False
+is_cape = True
+is_male = True
+skirt_modifier = '_skirt' if is_skirt else ''
+cape_modifier = '_cape' if is_cape else ''
+gender_modifier = '' if not is_cape else ('_m' if is_male else '_f')
+smpl_res = 3638 if is_cape else 14475
+use_cache = False
+name=f'iccv_{low_res}' + skirt_modifier + cape_modifier + gender_modifier
+
+if not is_skirt:
+    train_dir = '/data-local/127/train'
+    valid_dir = '/data-local/127/valid'
+else:
+    train_dir = '/data-local/128/train_skirt'
+    valid_dir = '/data-local/127/valid_skirt'
+
+if is_cape:
+    if is_male:
+        train_dir = '/data-local/127/cape_processed/train/male'
+        valid_dir = '/data-local/127/cape_processed/test/male'
+    else:
+        train_dir = '/data-local/127/cape_processed/train/female'
+        valid_dir = '/data-local/127/cape_processed/test/female'   
+
+
+
+def init_wandb():
+    run = wandb.init(project='upscale', name=name, notes='', dir='/data-local/127/temp', tags=['upscaler', 'iccv'])
+    artifact = wandb.Artifact('code', type='code')
+    artifact.add_file('scripts/upscaler_train.py')
+    artifact.add_file('losses/losses.py')
+    artifact.add_file('data/dataset.py')
+    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.')]
+    for config_file_name in config_file_names:
+        file_name = '/'.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=is_skirt, is_smpl_orig=is_cape)
+
+
+def plot_3d(mesh_gt, mesh_pred_unposed, faces):
+    mesh_pred_unposed[:,2] += 1
+    fig = go.Figure(data=[
+        go.Mesh3d(
+            # 8 vertices of a cube
+            x=mesh_gt[:,0],
+            y=mesh_gt[:,2],
+            z=mesh_gt[:,1],
+            # i, j and k give the vertices of triangles
+            i = faces[:,0],
+            j = faces[:,1],
+            k = faces[:,2]
+        ),
+        go.Mesh3d(
+            # 8 vertices of a cube
+            x=mesh_pred_unposed[:,0],
+            y=mesh_pred_unposed[:,2],
+            z=mesh_pred_unposed[:,1],
+            # i, j and k give the vertices of triangles
+            i = faces[:,0],
+            j = faces[:,1],
+            k = faces[:,2],
+        )
+    ])
+
+    fig.update_layout(scene_aspectmode='manual',
+                    scene_aspectratio=dict(x=1, y=2, z=1))
+    fig.update_layout(
+        scene = dict(
+            xaxis = dict(nticks=4, range=[-1,1],),
+                        yaxis = dict(nticks=4, range=[-1,3],),
+                        zaxis = dict(nticks=4, range=[-1,1],),)
+                        )
+
+    return fig
+
+
+def train_step(batch):
+    model.train()
+    with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=use_amp):
+        pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]])
+        pred = F.interpolate(pred, size=(512, 256), mode='bilinear')
+        
+        l1_loss = torch.mean(torch.sum(torch.abs(batch['uv_unposed'] - pred * batch['uv_mask'].unsqueeze(1)), dim=-1))
+        if low_res == 32:
+            ms_ssim = torch.mean(_MSSSIM_small(pred * batch['uv_mask'].unsqueeze(1) + 1, batch['uv_unposed'] * batch['uv_mask'].unsqueeze(1) + 1))
+        else:
+            ms_ssim = torch.mean(_MSSSIM(pred * batch['uv_mask'].unsqueeze(1) + 1, batch['uv_unposed'] * batch['uv_mask'].unsqueeze(1) + 1))
+
+        loss = 1e3 * (1 - ms_ssim)
+
+    scaler.scale(loss).backward()
+    scaler.step(optimizer)
+    scaler.update()
+    optimizer.zero_grad()
+
+    model.eval()
+    with torch.inference_mode():
+        mesh_pred = uv2mesh(pred)
+        mesh_orig = batch['mesh_unposed']
+        metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'].unsqueeze(2) * (mesh_orig - mesh_pred) ** 2, dim=-1))) / torch.sum(batch['mesh_mask'])
+
+    return { 'train/loss': loss, 'train/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim, }
+        
+
+def valid_step(batch):
+    model.eval()
+
+    with torch.inference_mode():
+        pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]])
+        pred = F.interpolate(pred, size=(512, 256), mode='bilinear')
+        
+        l1_loss = torch.mean(torch.sum(torch.abs(batch['uv_unposed'] - pred * batch['uv_mask'].unsqueeze(1)), dim=-1))
+        if low_res == 32:
+            ms_ssim = torch.mean(_MSSSIM_small(pred * batch['uv_mask'].unsqueeze(1) + 1, batch['uv_unposed'] * batch['uv_mask'].unsqueeze(1) + 1))
+        else:
+            ms_ssim = torch.mean(_MSSSIM(pred * batch['uv_mask'].unsqueeze(1) + 1, batch['uv_unposed'] * batch['uv_mask'].unsqueeze(1) + 1))
+
+        loss = 1e3 * (1 - ms_ssim)
+        mesh_pred = uv2mesh(pred)
+        mesh_orig = batch['mesh_unposed']
+        metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'].unsqueeze(2) * (mesh_orig - mesh_pred) ** 2, dim=-1))) / torch.sum(batch['mesh_mask'])
+
+    return { 'valid/loss': loss, 'valid/l1_loss': l1_loss, 'valid/euclid_error': metric, 'valid/ms_ssim': ms_ssim, }
+       
+
+def plot_everything(batch: dict, type: str):
+    model.eval()
+    with torch.inference_mode():
+        pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]])
+        pred = F.interpolate(pred, size=(512, 256), mode='bilinear')
+    
+    mesh_pred = uv2mesh(pred)
+    mesh_orig = batch['mesh_unposed']
+
+    _F = np.load("smpl/smpl_faces" + skirt_modifier + ("_orig" if is_cape else "") + ".npy")
+    mask = batch['mesh_mask'].cpu().numpy().astype(bool)
+    garment_unposed, faces = maskFacesBatch(mesh_pred.cpu().numpy(), _F, mask)
+    # gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred.shape[0])]
+    gt = [mesh_orig[i].cpu().numpy()[mask[i], :] for i in range(pred.shape[0])]
+    
+    figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i]) 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) }
+    
+    orig_img = batch['uv_unposed']
+    img = torch.concat((orig_img, pred), axis=2)
+    imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)]
+
+    return { **figs, **{ type + '/images': imgs} }
+
+
+def train():
+    global step
+    valid_iterator = iter(valid_dataloader)
+
+    for batch in train_dataloader:
+        train_loss = train_step(batch)
+        train_accumulator.update(train_loss)
+
+        if step % 25 == 0:
+            data_valid = next(valid_iterator)
+            valid_loss = valid_step(data_valid)
+            valid_accumulator.update(valid_loss)
+
+        if step % 1000 == 0 or (step < 1000 and step % 1000 == 0):
+            to_log = dict({ **train_loss, **valid_loss, **{ 'current_step': step } })
+            if step % 5000 == 0 or (step < 5000 and step % 1000 == 0):
+                train_figs = plot_everything(batch, 'train')
+                valid_figs = plot_everything(data_valid, 'valid')
+                to_log = { **to_log, **train_figs, **valid_figs }
+            if step == 0:
+                to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
+
+            if not args['debug']:
+                wandb.log(to_log, step=step)
+            train_accumulator.reset()
+            valid_accumulator.reset()
+            print(step)
+
+        if step % 10000 == 0:
+            ckpt_saver(model, step, optimizer, scaler)
+
+        step += 1
+
+
+if __name__ == '__main__':
+    if not args['debug']:
+        init_wandb()
+    
+    use_amp = True
+    uv_mask_body = torch.ones((low_res, low_res//2), device='cuda')
+    low_resolution_modifier = '_' + str(low_res)
+    
+    signature = { 'id', 'frame_nr',
+                          'pose_shape',
+                          'uv_unposed' + low_resolution_modifier,
+                          'uv_unposed',
+                          'mesh_unposed',
+                          'mesh_posed',
+                         'mesh_mask',
+                         'uv_mask',
+                         'uv_mask' + low_resolution_modifier,
+                        }
+    
+    # dataset = Dataset('/data-local/127/train', config['signature'], resolutions=[512], device=device, frames_file='frames_jumpsuit.txt')
+    dataset_train = Dataset(train_dir, device='cuda', use_cache=use_cache, signature=signature, frames_file=None, resolutions=[512, low_res], is_cape=is_cape)
+    dataset_valid = Dataset(valid_dir, device='cuda', use_cache=use_cache, signature=signature, frames_file=None, resolutions=[512, low_res], is_cape=is_cape)
+    train_dataloader = DataLoader(dataset_train, batch_size=4, drop_last=True, shuffle=True)
+    valid_dataloader = DataLoader(dataset_valid, batch_size=4, drop_last=True, shuffle=True)
+    train_accumulator = Accumulator()
+    valid_accumulator = Accumulator()
+
+    if low_res == 32 or low_res == 64:
+        kernel_size = 3
+    elif low_res == 128 or low_res == 256:
+        kernel_size = 5
+    else:
+        kernel_size = 7
+        
+    model : Upscaler = Upscaler(kernel_size=kernel_size).to('cuda')
+    optimizer = optim.Adam(model.parameters(), lr=0.0001)
+    scaler = torch.cuda.amp.GradScaler(enabled=use_amp)
+
+    # _MSSSIM = MSSSIM(data_range=2).to(config['device'])
+    _MSSSIM = MS_SSIM(data_range=2, size_average=True, channel=3, weights=[0.5, 0.2, 0.2, 0.1])
+    _MSSSIM_small = MS_SSIM(data_range=2, size_average=True, channel=3, weights=[1.0])
+
+    step = 0
+    ckpt_saver = CheckpointSaver('checkpoints/iccv/torch/' + name, top_n=5)
+
+    print(f'started training: {name}')
+
+    if not args['debug']:
+        # model = torch.compile(model)
+        wandb.watch(model, log='all', log_freq=1000, log_graph=True)
+
+    train()
+
+
+

utils/checkpoints.py

@@ -16,9 +16,9 @@ class CheckpointSaver:
         self.top_n = top_n
         self.top_model_paths = []
         
-    def __call__(self, model, iteration, optimizer):
+    def __call__(self, model, iteration, optimizer, scaler):
         model_path = os.path.join(self.dirpath, f'iteration_{iteration}.pt')
-        torch.save({ 'model': model.state_dict(), 'optimizer': optimizer }, model_path)
+        torch.save({ 'model': model.state_dict(), 'optimizer': optimizer, 'scaler': scaler }, model_path)
         self.top_model_paths.append({'path': model_path})
         if len(self.top_model_paths)>self.top_n: 
             self.cleanup()
@@ -31,12 +31,13 @@ class CheckpointSaver:
         self.top_model_paths = self.top_model_paths[len(self.top_model_paths)-self.top_n:]
 
 
-def load_model(model, checkpoint, optimizer=None, iteration=''):
+def load_model(model, checkpoint, optimizer=None, scaler=None, iteration=''):
     # list of checkpoint files in the folder checkpoints
     checkpoints = [os.path.join(checkpoint, f) for f in os.listdir(checkpoint) if f.endswith(f'{iteration}.pt')]
     checkpoint = torch.load(checkpoints[-1])
     iteration = checkpoints[-1].split('_')[-1].split('.')[0]
     model.load_state_dict(checkpoint['model'])
     if optimizer is not None: optimizer.load_state_dict(checkpoint['optimizer'])
+    if scaler is not None: scaler.load_state_dict(checkpoint['scaler'])
     return int(iteration) if iteration != '' else 0