80 lines
2.8 KiB
Python
80 lines
2.8 KiB
Python
import torch
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import torch.nn.functional as F
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import torch.optim as optim
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import sys
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import os
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from tqdm import tqdm
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sys.path.append(os.getcwd())
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def init(smpl_layer, target, device, cfg):
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params = {}
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params["pose_params"] = torch.zeros(target.shape[0], 72)
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params["shape_params"] = torch.zeros(target.shape[0], 10)
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params["scale"] = torch.ones([1])
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smpl_layer = smpl_layer.to(device)
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params["pose_params"] = params["pose_params"].to(device)
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params["shape_params"] = params["shape_params"].to(device)
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target = target.to(device)
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params["scale"] = params["scale"].to(device)
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params["pose_params"].requires_grad = True
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params["shape_params"].requires_grad = bool(cfg.TRAIN.OPTIMIZE_SHAPE)
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params["scale"].requires_grad = bool(cfg.TRAIN.OPTIMIZE_SCALE)
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optimizer = optim.Adam([params["pose_params"], params["shape_params"], params["scale"]],
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lr=cfg.TRAIN.LEARNING_RATE)
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index = {}
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smpl_index = []
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dataset_index = []
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for tp in cfg.DATASET.DATA_MAP:
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smpl_index.append(tp[0])
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dataset_index.append(tp[1])
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index["smpl_index"] = torch.tensor(smpl_index).to(device)
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index["dataset_index"] = torch.tensor(dataset_index).to(device)
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return smpl_layer, params, target, optimizer, index
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def train(smpl_layer, target,
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logger, writer, device,
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args, cfg, meters):
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res = []
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smpl_layer, params, target, optimizer, index = \
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init(smpl_layer, target, device, cfg)
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pose_params = params["pose_params"]
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shape_params = params["shape_params"]
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scale = params["scale"]
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for epoch in tqdm(range(cfg.TRAIN.MAX_EPOCH)):
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# for epoch in range(cfg.TRAIN.MAX_EPOCH):
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verts, Jtr = smpl_layer(pose_params, th_betas=shape_params)
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loss = F.smooth_l1_loss(Jtr.index_select(1, index["smpl_index"]) * 100,
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target.index_select(1, index["dataset_index"]) * 100 * scale)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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meters.update_early_stop(float(loss))
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if meters.update_res:
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res = [pose_params, shape_params, verts, Jtr]
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if meters.early_stop:
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logger.info("Early stop at epoch {} !".format(epoch))
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break
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if epoch % cfg.TRAIN.WRITE == 0:
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# logger.info("Epoch {}, lossPerBatch={:.6f}, scale={:.4f} EarlyStopSatis: {}".format(
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# epoch, float(loss),float(scale), early_stop.satis_num))
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writer.add_scalar('loss', float(loss), epoch)
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writer.add_scalar('learning_rate', float(
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optimizer.state_dict()['param_groups'][0]['lr']), epoch)
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logger.info('Train ended, min_loss = {:.4f}'.format(
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float(meters.min_loss)))
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return res
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