small updates

2023-07-08 11:54:21 +02:00
parent c41a577672
commit 57b7aed4ba
15 changed files with 884 additions and 221 deletions
@@ -0,0 +1,160 @@
+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())
+
+
+from configs.iccvw.pyr_64_2 import config
+
+os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = '5'
+if args['debug']:
+    os.environ["CUDA_LAUNCH_BLOCKING"] = '1'
+
+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
+
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.profiler import profile, record_function, ProfilerActivity
+from data.dataset import Dataset
+from utils.checkpoints import  load_model
+from utils.utils import InfiniteDataLoader as DataLoader
+from losses.losses import  kl_loss
+from custom_utils.custom_utils import UV2Mesh, Accumulator, group_inputs_for_levels
+from smpl.smpl_torch import SMPL
+from models.pyramid_model import Pyramid
+
+
+
+uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
+
+def train_step(batch):
+    model.train()
+    # with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=use_amp):
+    levels_input, conds = group_inputs_for_levels(batch, config)
+
+    out_levels = model([levels_input, conds])
+
+    if config['use_skinning']:
+        mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
+    else:
+        mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
+
+    reconstr_loss_total = 0
+    kl_total = 0
+
+    #for i, res in enumerate(config['pyramid']['resolutions']):
+    i = -1
+    pred, mean, logvar = out_levels[i]
+
+    mesh_pred = uv2mesh(pred)
+
+    if config['use_skinning']:
+        shape = batch['pose_shape'][:, 72:]
+        gender = torch.greater_equal(shape[:, 10] , 0.5).to(dtype=torch.int64)
+        shape_offsets = torch.stack([torch.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], dims=[[1], [2]])[0,smpl.to_keep,:] for i in range(shape.shape[0])])
+
+        mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,None]
+        mesh_pred_posed = smpl(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)[0]
+    else:
+        mesh_pred_posed = mesh_pred
+
+
+    kl = kl_loss(mean, logvar)
+
+    loss: torch.Tensor =  g_balancing_coeff * kl
+
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    model.eval()
+    with torch.inference_mode():
+        metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred.detach()) ** 2, axis=-1))) / torch.sum(batch['mesh_mask'])
+
+
+    return { **{ 'train/loss': loss, 'train/reconstr_loss_total': reconstr_loss_total, 'train/kl_total': kl_total } }
+
+
+def train():
+    global g_balancing_coeff
+    global step
+
+    for batch in train_dataloader:
+        train_step(batch)
+
+        step += 1
+        print(step)
+        profiler.step()
+
+        if step > 300:
+            break
+
+
+
+if __name__ == '__main__':
+    smpl = SMPL(orig_smpl=config['is_cape'], path='smpl', device=config['device'])
+    skirt_modifier = '_skirt' if config['is_skirt'] else ''
+    dataset_resolutions = config['pyramid']['resolutions'].copy()
+    if 512 not in dataset_resolutions:
+        dataset_resolutions.append(512)
+
+    dataset_train = Dataset(config['train_dir'], config['signature'], device=config['device'], frames_file='frames_single.txt', use_cache=True, resolutions=dataset_resolutions, is_cape=config['is_cape'])
+    train_dataloader = DataLoader(dataset_train, batch_size=4, drop_last=True, shuffle=True, )
+    train_accumulator = Accumulator()
+
+    model: Pyramid = Pyramid(config)
+
+    optimizer = optim.Adam(model.parameters(), lr=config['lr'])
+
+    g_balancing_coeff = config['starting_balancing_coeff']
+    reg_coeff = 1
+
+    step = 0
+    if config['pretrained_model'] != '':
+        # assert len(config['pyramid']['resolutions']) == 1
+        load_model(model, config['pretrained_model'], optimizer=optimizer, iteration=config['pretrained_model_iteration'])
+
+    if len(config['pyramid']['resolutions']) == 1:
+        for param in model.static_enc.parameters(): param.requires_grad = False
+        for param in model.body_enc.parameters(): param.requires_grad = False
+        for param in model.norm_enc.parameters(): param.requires_grad = False
+    else:
+        load_model(model, config['pyramid']['pretrained_pyramid'], iteration=config['pyramid']['pretrained_pyramid_iteration'])
+        for param in model.static_enc.parameters(): param.requires_grad = False
+        for param in model.body_enc.parameters(): param.requires_grad = False
+        for param in model.norm_enc.parameters(): param.requires_grad = False
+
+    for i in range(len(model.levels)-1):
+        for param in model.levels[i].parameters(): param.requires_grad = False
+
+    # create a profiler object
+    profiler = torch.profiler.profile(
+        activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+        schedule=torch.profiler.schedule(wait=2, warmup=5, active=3, repeat=2),
+        on_trace_ready=torch.profiler.tensorboard_trace_handler("log_dir"),
+    )
+
+    with profiler:
+        train()
+
+    profiler.stop()
+    print(profiler.key_averages().table(sort_by="cpu_time_total", row_limit=25))