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finetune_ec_weight.py
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finetune_ec_weight.py
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import os
import time
import numpy as np
import torch
import torch.nn as nn
import tqdm
import imageio
from dataset.obj_dataset import OBJDataset
from models.render_ray import render_rays
from models.render_image import render_single_image
from models.model import CodecNerfModel
from models.sample_ray import RaySamplerSingleImage, RaySamplerMultipleImages
from models.criterion import Criterion
from entropy_coding.base import SimpleVAECompressionModel
from entropy_coding.weight_entropy import *
import opt
import math
from utils import (
img2mse, mse2psnr, img_HWC2CHW, img2psnr, set_seed,
get_cosine_schedule_with_warmup, ssim, msssim)
import pandas as pd
torch.set_float32_matmul_precision('high')
def finetune(args):
device = "cuda:{}".format(args.local_rank)
dataset = OBJDataset(args.data_path, args.camera_path)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False)
model = CodecNerfModel(args)
compress_model = SimpleVAECompressionModel(args).to(device)
spike_and_slap_cdf = SpikeAndSlabCDF()
weight_entropyModule = WeightEntropyModule(spike_and_slap_cdf)
weight_entropyModule.to(device)
weight_entropyModule.train()
criterion = Criterion()
scalars_to_log = {}
for train_data in data_loader:
ray_samplert = RaySamplerMultipleImages(train_data, device, args.img_hw)
time0 = time.time()
featmaps, code_mb = model.encode(train_data['src_rgbs'], train_data['src_intrinsics'], None, train_data['src_w2c_mats'])
to_optimize0 = nn.Parameter(featmaps[0], requires_grad=False).to(device)
to_optimize1 = nn.Parameter(featmaps[1], requires_grad=False).to(device)
n_feature_maps = len(featmaps)
feat_dim_list = [featmaps[0].shape[2], featmaps[1].shape[2]]
feat_res_list = [featmaps[0].shape[-1], featmaps[1].shape[-1]]
del featmaps, model.feature_net
feat_coefs = []
for i in range(n_feature_maps):
m = nn.Parameter(torch.randn((3, args.trank, feat_res_list[i], feat_res_list[i])))
v = nn.Parameter(torch.randn((args.trank, feat_dim_list[i])))
feat_coefs.append(nn.init.normal_(m))
feat_coefs.append(nn.init.zeros_(v))
compress_param = {
"net": {
name for name, param in compress_model.named_parameters()
if param.requires_grad and not name.endswith(".quantiles")
},
}
compress_param = {k: [p for p in compress_model.parameters() if p.requires_grad] for k, v in compress_param.items()}
vm_model = torch.nn.ParameterList(feat_coefs).to(device)
vm_model.train()
params = model.get_param()
params += [{'params': vm_model, 'lr':args.lrate_feat}]
params += [{'params': compress_param['net'], 'lr': args.lrate_comp}]
optim = torch.optim.Adam(params, eps=1e-15)
lr_sched = get_cosine_schedule_with_warmup(
optim, num_warmup_steps=100, num_training_steps=args.iter)
for i in range(args.iter):
m0 = vm_model[0]
m1 = vm_model[2]
m0_h, likelihood0 = compress_model(m0)
m1_h, likelihood1 = compress_model(m1)
m_0_ = m0_h.view(3, args.trank, -1)
m_1_ = m1_h.view(3, args.trank, -1)
v_0_ = vm_model[1]
v_1_ = vm_model[3]
vm_merged0 = torch.einsum('ijl,jk->ikl', m_0_, v_0_)
vm_merged1 = torch.einsum('ijl,jk->ikl', m_1_, v_1_)
vm_merged0 = vm_merged0.view(3, 32, feat_res_list[0], feat_res_list[0]).unsqueeze(1)
vm_merged1 = vm_merged1.view(3, 32, feat_res_list[1], feat_res_list[1]).unsqueeze(1)
vm_merged0 += to_optimize0
vm_merged1 += to_optimize1
ray_batch = ray_samplert.random_sample(args.N_rand)
ret = render_rays(ray_batch=ray_batch,
model=model,
featmaps=[vm_merged0, vm_merged1],
N_samples=args.N_samples,
inv_uniform=args.inv_uniform,
N_importance=args.N_importance,
det=args.det,
white_bkgd=args.white_bkgd,
)
optim.zero_grad()
loss, scalars_to_log = criterion(ret['outputs_coarse'], ray_batch, scalars_to_log)
if ret['outputs_fine'] is not None:
fine_loss, scalars_to_log = criterion(ret['outputs_fine'], ray_batch, scalars_to_log)
loss += fine_loss
num_pixels = args.img_hw[0] * args.img_hw[1]
bpp_loss0 = torch.log(likelihood0).sum() / (-math.log(2) * num_pixels)
bpp_loss1 = torch.log(likelihood1).sum() / (-math.log(2) * num_pixels)
loss += 1e-5 * (bpp_loss0 + bpp_loss1)
w_bpp_sum = 0
for key in model.net_coarse.state_dict():
if 'lora' in key:
weight = model.net_coarse.state_dict()[key]
og_shape = weight.shape
weight = weight.reshape(1,1,-1)
w_hat, w_likelihood = weight_entropyModule(weight, True)
w_hat = w_hat.reshape(og_shape)
w_bpp = torch.log(w_likelihood) / (-math.log(2) * num_pixels)
w_bpp_sum += w_bpp.sum()
for key in model.net_fine.state_dict():
if 'lora' in key:
weight = model.net_fine.state_dict()[key]
og_shape = weight.shape
weight = weight.reshape(1,1,-1)
w_hat, w_likelihood = weight_entropyModule(weight, True)
w_hat = w_hat.reshape(og_shape)
w_bpp = torch.log(w_likelihood) / (-math.log(2) * num_pixels)
w_bpp_sum += w_bpp.sum()
loss += 1e-5 * w_bpp_sum
loss.backward()
scalars_to_log['loss'] = loss.item()
optim.step()
lr_sched.step()
compress_model.update(force=True)
dt = time.time() - time0
if args.local_rank == 0:
if i % args.i_print == 0 or i < 10:
# write mse and psnr stats
mse_error = img2mse(ret['outputs_coarse']['rgb'], ray_batch['rgb']).item()
scalars_to_log['train/coarse-loss'] = mse_error
scalars_to_log['train/coarse-psnr-training-batch'] = mse2psnr(mse_error)
if ret['outputs_fine'] is not None:
mse_error = img2mse(ret['outputs_fine']['rgb'], ray_batch['rgb']).item()
scalars_to_log['train/fine-loss'] = mse_error
scalars_to_log['train/fine-psnr-training-batch'] = mse2psnr(mse_error)
try:
logstr = '{} step: {} '.format(train_data['scene_name'][0], i)
except:
logstr = '{} step: {} '.format(args.data_path.split('/')[-1], i)
for k in scalars_to_log.keys():
logstr += ' {}: {:.6f}'.format(k, scalars_to_log[k])
print(logstr)
print('each iter time {:.05f} seconds'.format(dt))
if i % 1000 == 0:
vm_model.eval()
model.switch_to_eval()
compress_model.eval()
weight_entropyModule.eval()
m0 = vm_model[0]
m1 = vm_model[2]
string0 = compress_model.entropy_bottleneck.compress(m0)
string1 = compress_model.entropy_bottleneck.compress(m1)
shape0 = m0.size()[2:]
shape1 = m1.size()[2:]
m0_hat = compress_model.entropy_bottleneck.decompress(string0, shape0)
m1_hat = compress_model.entropy_bottleneck.decompress(string1, shape1)
m_0_ = m0_hat.view(3, args.trank, -1)
m_1_ = m1_hat.view(3, args.trank, -1)
v_0_ = vm_model[1]
v_1_ = vm_model[3]
vm_merged0 = torch.einsum('ijl,jk->ikl', m_0_, v_0_)
vm_merged1 = torch.einsum('ijl,jk->ikl', m_1_, v_1_)
vm_merged0 = vm_merged0.view(3, 32, feat_res_list[0], feat_res_list[0]).unsqueeze(1)
vm_merged1 = vm_merged1.view(3, 32, feat_res_list[1], feat_res_list[1]).unsqueeze(1)
vm_merged0 += to_optimize0
vm_merged1 += to_optimize1
string_mb = (len(string0[0]) + len(string0[1]) + len(string0[2]) +\
len(string1[0]) + len(string1[1]) + len(string1[2]))/(10**6)
bit_sum = 0
state_dict_coarse = model.net_coarse.state_dict()
for key in model.net_coarse.state_dict():
if 'lora' in key:
weight = model.net_coarse.state_dict()[key]
og_shape = weight.shape
weight = weight.reshape(1,1,-1)
bits = weight_entropyModule.compress(weight)
bit_sum += len(bits[0])
w_hat = weight_entropyModule.decompress(bits, og_shape)
w_hat = w_hat.reshape(og_shape)
state_dict_coarse[key] = w_hat.cpu()
model.net_coarse.load_state_dict(state_dict_coarse, strict=False)
state_dict_fine = model.net_fine.state_dict()
for key in model.net_fine.state_dict():
if 'lora' in key:
weight = model.net_fine.state_dict()[key]
og_shape = weight.shape
weight = weight.reshape(1,1,-1)
bits = weight_entropyModule.compress(weight)
bit_sum += len(bits[0])
w_hat = weight_entropyModule.decompress(bits, og_shape)
w_hat = w_hat.reshape(og_shape)
state_dict_fine[key] = w_hat.cpu()
model.net_fine.load_state_dict(state_dict_fine, strict=False)
bit_sum = bit_sum/(10**6)
storage = bit_sum + string_mb + (args.trank * 32 * 2 * 4)/(10**6) # vector size
print('Logging a random validation view...')
try:
ren_folder = os.path.join(args.outdir, train_data['scene_name'][0], str(i))
except:
ren_folder = os.path.join(args.outdir, args.data_path.split('/')[-1], str(i))
print(f'Rendering to {ren_folder}')
os.makedirs(ren_folder, exist_ok=True)
data_input = dict(
tgt_intrinsic=train_data['all_intrinsics'][0][0:1],
depth_range=train_data['depth_range'],
)
render_poses = train_data['all_c2w_mats'][0]
view_indices = np.arange(0, len(render_poses), 1)
psnr_ = 0
ssim_ = 0
msssim_ = 0
with torch.no_grad():
for idx, pose in tqdm.tqdm(zip(view_indices, render_poses), total=len(view_indices)):
filename = os.path.join(ren_folder, f'{idx:06}.png')
data_input['tgt_c2w_mat'] = pose[None, :]
ray_sampler = RaySamplerSingleImage(data_input, device, args.img_hw, render_stride=1)
ray_batch = ray_sampler.get_all()
ret = render_single_image(ray_sampler=ray_sampler,
ray_batch=ray_batch,
model=model,
chunk_size=args.chunk_size,
N_samples=args.N_samples,
inv_uniform=args.inv_uniform,
N_importance=args.N_importance,
det=True,
white_bkgd=args.white_bkgd,
render_stride=1,
featmaps=[vm_merged0, vm_merged1])
check_ = ret['outputs_fine']['rgb']
check_ = torch.clamp(check_.detach().cpu(), 0.0, 1.0)
psnr_ += img2psnr(check_, train_data['all_rgbs'][0][idx])
ssim_ += ssim(check_, train_data['all_rgbs'][0][idx])
msssim_ += msssim(check_, train_data['all_rgbs'][0][idx])
rgb_im = img_HWC2CHW(ret['outputs_fine']['rgb'].detach().cpu())
rgb_im = torch.clamp(rgb_im, 0.0, 1.0)
rgb_im = rgb_im.permute([1, 2, 0]).cpu().numpy()
rgb_im = (rgb_im * 255.).astype(np.uint8)
imageio.imwrite(filename, rgb_im)
try:
name = train_data['scene_name'][0]
except:
name = args.data_path.split('/')[-1]
pd_data = {name : {'ITER': i, 'PSNR' : psnr_/len(render_poses), 'SSIM' : ssim_/len(render_poses),
'MSSSIM' : msssim_/len(render_poses),
"CODE" : code_mb, "REST": storage}}
new_df = pd.DataFrame([(folder, metrics['ITER'], metrics['PSNR'], metrics['SSIM'], metrics['MSSSIM'],
metrics['CODE'], metrics['REST']) for folder, metrics in pd_data.items()],
columns=['FileName', 'ITER', 'PSNR', 'SSIM', 'MSSSIM', 'CODE', 'REST'])
existing_csv_file_path = f"{i}_ec_weight.csv"
if os.path.exists(existing_csv_file_path):
new_df.to_csv(existing_csv_file_path, index=False, mode='a', header=False)
print(f"Data appended to the existing CSV file at: {existing_csv_file_path}")
else:
new_df.to_csv(existing_csv_file_path, index=False)
vm_model.train()
model.switch_to_train()
compress_model.train()
weight_entropyModule.train()
if __name__ == '__main__':
parser = opt.config_parser()
args = parser.parse_args()
set_seed(args.seed)
finetune(args)