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