Different bitrates computed using the saved file size and computed using the likelihoods from the entropy model
Reza-Asiyabi opened this issue · comments
I'm using the EntropyBottleneck module in a factorized prior network. While training, the aux_loss and bpp_loss are decreasing but when I use the trained network to compress and save the compressed images, the bitrate computed using the saved file size is much larger than the bitrate computed using the likelihoods from the entropy model (about 2.7 bpp in comparison to 1.1 bpp).
The network architecture is below:
import torch
import torch.nn as nn
from compressai.compressai.entropy_models import EntropyBottleneck
from compressai.compressai.models import CompressionModel
from compressai.compressai.layers import GDN
class FactorizedPrior_Net1(CompressionModel):
def __init__(self, in_channels=1, out_channels=1, layer_num=3, intermediate_channels1=128, intermediate_channels2=192):
super().__init__()
self.layer_num = layer_num
N = intermediate_channels1
M = intermediate_channels2
Encoder_layers = []
for i in range(self.layer_num - 2):
Encoder_layers.append( nn.Conv2d(in_channels=N, out_channels=N, kernel_size=5, stride=2, padding=2))
Encoder_layers.append(GDN(N))
self.Encoder = nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=N, kernel_size=5, stride=2, padding=2),
GDN(N),
*Encoder_layers,
nn.Conv2d(in_channels=N, out_channels=M, kernel_size=5, stride=2, padding=2),
)
self.entropy_bottleneck = EntropyBottleneck(channels=M)
Decoder_layers = []
for i in range(self.layer_num - 2):
Decoder_layers.append(nn.ConvTranspose2d(in_channels=N, out_channels=N, kernel_size=5, stride=2, output_padding=1, padding=2))
Decoder_layers.append(GDN(N, inverse=True))
self.Decoder = nn.Sequential(
nn.ConvTranspose2d(in_channels=M, out_channels=N, kernel_size=5, stride=2, output_padding=1, padding=2),
GDN(N, inverse=True),
*Decoder_layers,
nn.ConvTranspose2d(in_channels=N, out_channels=out_channels, kernel_size=5, stride=2, output_padding=1, padding=2),
)
def forward(self, x):
y = self.Encoder(x)
y_hat, y_likelihoods = self.entropy_bottleneck(y)
x_hat = self.Decoder(y_hat)
return {"x_hat": x_hat, "likelihoods": {'y': y_likelihoods}}
def compress(self, x):
y = self.Encoder(x)
y_strings = self.entropy_bottleneck.compress(y)
return {"EB_strings": y_strings, "EB_shape": y.size()[-2:]}
def decompress(self, strings, shape):
assert isinstance(strings, list) and len(strings) == 1
y_hat = self.entropy_bottleneck.decompress(strings[0], shape)
x_hat = self.Decoder(y_hat)
return {"rec_image": x_hat}
This is the code for training the network:
import torch
import numpy as np
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import os
from datetime import datetime
from tqdm import tqdm
import logging
import torch.optim.lr_scheduler as lr_scheduler
from torchvision.utils import make_grid
from pytorch_msssim import ms_ssim
from FFT_Exercise_Paper.ComplexValuedNN.SAR_Data_Compression.RV_CAE.Models import FactorizedPrior_Net1
from compressai.compressai.losses import RateDistortionLoss
from compressai.compressai.optimizers import net_aux_optimizer
from compressai.compressai.models.utils import update_registered_buffers
##### Parameters
in_channels = 1
out_channels = 1
intermediate_channels1 = 64
intermediate_channels2 = 128
net1_layers_num = 4 #should be more than 2
test_freq = 50
save_freq = 1
total_step = 0
epoch_step = 0
best_testSQNR = 1
total_epochs = 5
train_batch_size = 8
test_batch_size = 1
lr = 0.0001
aux_lr = 0.001
use_lr_scheduler = True
if use_lr_scheduler:
lr_scheduler_step = 1
clip_max_norm = 1
loss_lmbda = 2000
distortion_metric = "mse"
pretrained = False
if pretrained:
checkpoint_path = 'logs/2023-05-08_10_36_39/Models/Model_epoch_31.pth.tar'
device = 'cuda' if torch.cuda.is_available() else 'cpu'
description = 'Net1_Normal'
save_path = os.path.join('D:/Reza/My Python Projects/New Project/FFT_Exercise_Paper/ComplexValuedNN/SAR_Data_Compression/RV_CAE/Logs/', '{}_Layers {}_{}_{}'.format(datetime.now().strftime('%Y-%m-%d_%H_%M_%S'),intermediate_channels1, intermediate_channels2, description))
os.makedirs(os.path.join(save_path, 'Models/'), exist_ok=True)
class AverageMeter:
"""Compute running average."""
def __init__(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def configure_optimizers(net, lr, aux_lr):
"""Separate parameters for the main optimizer and the auxiliary optimizer.
Return two optimizers"""
conf = {
"net": {"type": "Adam", "lr": lr},
"aux": {"type": "Adam", "lr": aux_lr},
}
optimizer = net_aux_optimizer(net, conf)
return optimizer["net"], optimizer["aux"]
def test(net1, test_dataloader, device, criterion, distortion_metric, epoch, total_step, best_testSQNR):
net1.eval()
loss = AverageMeter()
SQNR = AverageMeter()
bpp_loss = AverageMeter()
distortion_loss = AverageMeter()
aux_loss = AverageMeter()
with torch.no_grad():
for batch_idx, batch in enumerate(test_dataloader):
input = batch[:, :, 0, :, :].to(device)
output = net1(input)
out_criterion = criterion(output, input)
SQNR_temp = torch.sum(torch.square(abs(input)))/torch.sum(torch.square(abs(input - output['x_hat'])))
loss.update(out_criterion['loss'])
SQNR.update(10*torch.log10(SQNR_temp))
bpp_loss.update(out_criterion['bpp_loss'])
if distortion_metric == 'mse':
distortion_loss.update(out_criterion['mse_loss'])
elif distortion_metric == 'ms_ssim':
distortion_loss.update(out_criterion['ms_ssim_loss'])
aux_loss.update(net1.entropy_bottleneck.loss())
if batch_idx%1==0:
try:
grid_imgs = torch.concat((grid_imgs, torch.abs(input), torch.abs(output['x_hat'])), dim=0)
except:
grid_imgs = torch.concat((torch.abs(input), torch.abs(output['x_hat'])), dim=0)
grid = make_grid(grid_imgs, nrow=4)
logger.info("Test on test dataset: epoch-{}, step-{}".format(epoch, total_step))
if event_writer !=None:
logger.info("Add tensorboard for test dataset---epoch:{}-Step:{}".format(epoch, total_step))
if distortion_metric == "mse":
event_writer.add_scalar("Test MSE_avg", distortion_loss.avg.item(), total_step)
elif distortion_metric == "ms_ssim":
event_writer.add_scalar("Test MSE_avg", distortion_loss.avg.item(), total_step)
event_writer.add_scalar("Test SQNR_avg (dB)", SQNR.avg.item(), total_step)
event_writer.add_scalar("Test bpp_loss_avg (dB)", bpp_loss.avg.item(), total_step)
event_writer.add_scalar("Test loss_avg (dB)", loss.avg.item(), total_step)
event_writer.add_scalar("Test aux_loss_avg (dB)", aux_loss.avg.item(), total_step)
event_writer.add_image('test example', grid, global_step=total_step)
if SQNR.avg.item() > best_testSQNR:
best_testSQNR = SQNR.avg.item()
net1.entropy_bottleneck.update()
ceckpoint_dict = {
"epoch": epoch,
"state_dict": net1.state_dict(),
"optimizer": optimizer.state_dict(),
"aux_optimizer": aux_optimizer.state_dict(),
}
torch.save(ceckpoint_dict, os.path.join(save_path, 'Models/Model_best_testSQNR.pth.tar'))
net1.train()
return best_testSQNR
def train(net1, train_dataloader, test_dataloader, epoch, device, criterion, optimizer, aux_optimizer, lr_scheduler, distortion_metric, train_batch_size=8, test_freq=10, total_step=0, total_epochs=50, best_testSQNR=1):
epoch_step = 0
net1.train()
with tqdm(total=len(train_dataloader), desc=f'Epoch {epoch + 1}/{total_epochs}', unit='batch') as pbar:
for batch_idx, batch in enumerate(train_dataloader):
input = batch[:, :, 0, :, :].to(device)
output = net1(input)
out_criterion = criterion(output, input)
pbar.set_postfix(**{'train loss (batch)': out_criterion['loss'].item()})
optimizer.zero_grad()
aux_optimizer.zero_grad()
out_criterion['loss'].backward()
if clip_max_norm > 0:
torch.nn.utils.clip_grad_norm_(net1.parameters(), clip_max_norm)
optimizer.step()
aux_loss = net1.entropy_bottleneck.loss()
aux_loss.backward()
aux_optimizer.step()
if distortion_metric == "mse":
train_batch_sumDistorion = torch.sum(out_criterion['mse_loss'])
elif distortion_metric == "ms_ssim":
train_batch_sumDistorion = torch.sum(out_criterion['ms_ssim_loss'])
train_batch_sumbpp = torch.sum(out_criterion['bpp_loss'])
if event_writer !=None:
logger.info("Add tensorboard for Train batch---epoch:{}-Step:{}".format(epoch, total_step))
if distortion_metric == "mse":
event_writer.add_scalar("Train MSE_avg", train_batch_sumDistorion/train_batch_size, total_step)
elif distortion_metric == "ms_ssim":
event_writer.add_scalar("Train MS-SSIM_avg", train_batch_sumDistorion/train_batch_size, total_step)
event_writer.add_scalar("Train bpp_loss_avg", train_batch_sumbpp/train_batch_size, total_step)
event_writer.add_scalar("Train lr", optimizer.param_groups[0]["lr"], total_step)
if (total_step % test_freq) == 0:
best_testSQNR = test(net1=net1,
test_dataloader=test_dataloader,
device=device,
criterion=criterion,
distortion_metric=distortion_metric,
epoch=epoch,
total_step=total_step,
best_testSQNR=best_testSQNR)
epoch_step += 1
total_step += 1
pbar.update(1)
try:
lr_scheduler.step()
except:
pass
if (epoch % save_freq) == 0:
net1.entropy_bottleneck.update()
ceckpoint_dict = {
"epoch": epoch,
"state_dict": net1.state_dict(),
"optimizer": optimizer.state_dict(),
"aux_optimizer": aux_optimizer.state_dict(),
}
torch.save(ceckpoint_dict, os.path.join(save_path, 'Models/Model_epoch_{}.pth.tar'.format(epoch)))
return total_step, best_testSQNR
logger = logging.getLogger("Real-Valued autoencoder to compress and decompress the Imaginary component SAR data with Entropy Modeling")
event_writer = SummaryWriter(os.path.join(save_path, 'tb_logs/'))
summary_text = "Compression of the Imaginary component of the SLC data\n" \
"Parameters:\n" \
"in_channels = {}\n" \
"out_channels = {}\n" \
"intermediate_channels1 = {}\n" \
"intermediate_channels2 = {}\n" \
"test_freq = {}\n" \
"save_freq = {}\n" \
"total_epochs = {}\n" \
"train_batch_size = {}\n" \
"test_batch_size = {}\n" \
"lr = {}\n" \
"aux_lr = {}\n" \
"use_lr_scheduler = {}\n" \
"clip_max_norm = {}\n" \
"loss_lmbda = {}\n" \
"distortion_metric = {}\n" \
"pretrained = {}\n"\
.format(in_channels, out_channels, intermediate_channels1, intermediate_channels2, test_freq, save_freq,
total_epochs, train_batch_size, test_batch_size, lr, aux_lr, use_lr_scheduler, clip_max_norm, loss_lmbda, distortion_metric, pretrained)
event_writer.add_text("summary", summary_text)
net1 = FactorizedPrior_Net1(in_channels=1, out_channels=1, layer_num=net1_layers_num, intermediate_channels1=intermediate_channels1, intermediate_channels2=intermediate_channels2).to(device)
optimizer, aux_optimizer = configure_optimizers(net=net1, lr=lr, aux_lr=aux_lr)
if use_lr_scheduler:
lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=lr_scheduler_step, gamma=0.5)
else:
lr_scheduler = None
criterion = RateDistortionLoss(lmbda=loss_lmbda, metric=distortion_metric)
if pretrained: # load from previous checkpoint
print("Loading pretrained states from checkpoint")
checkpoint = torch.load(checkpoint_path, map_location=device)
update_registered_buffers(net1.entropy_bottleneck, "entropy_bottleneck", ["_quantized_cdf", "_offset", "_cdf_length"], checkpoint["state_dict"])
epoch = checkpoint["epoch"] + 1
net1.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
aux_optimizer.load_state_dict(checkpoint["aux_optimizer"])
dataset = np.load("D:/Reza/Data/dataset.npy")
trainset = dataset[200:]
testset = dataset[:200]
trainset = np.expand_dims(trainset, axis=1)
testset = np.expand_dims(testset, axis=1)
train_dataloader = DataLoader(dataset=trainset,
batch_size=train_batch_size,
shuffle=True,
pin_memory=True,
num_workers=0)
test_dataloader = DataLoader(dataset=testset,
batch_size=test_batch_size,
shuffle=False,
pin_memory=True,
num_workers=0)
for epoch in range(total_epochs):
total_step, best_testSQNR = train(net1=net1,
train_dataloader=train_dataloader,
test_dataloader=test_dataloader,
epoch=epoch,
device=device,
criterion=criterion,
optimizer=optimizer,
aux_optimizer=aux_optimizer,
lr_scheduler=lr_scheduler,
distortion_metric=distortion_metric,
train_batch_size=train_batch_size,
test_freq=test_freq,
total_step=total_step,
total_epochs=total_epochs,
best_testSQNR=best_testSQNR)
net1.entropy_bottleneck.update()
ceckpoint_dict = {
"epoch": epoch,
"state_dict": net1.state_dict(),
"optimizer": optimizer.state_dict(),
"aux_optimizer": aux_optimizer.state_dict(),
}
torch.save(ceckpoint_dict, os.path.join(save_path, 'Models/Model_Final.pth.tar'))
The following is the code for compressing and decompressing using the trained network:
import torch
import numpy as np
import os
from FFT_Exercise_Paper.ComplexValuedNN.SAR_Data_Compression.RV_CAE.Models import FactorizedPrior_Net1
from compressai.compressai.models.utils import update_registered_buffers
import math
device = 'cuda' if torch.cuda.is_available() else 'cpu'
in_channels = 1
out_channels = 1
intermediate_channels1 = 64
intermediate_channels2 = 128
net1_layers_num = 4
bottleneck_size = (16, 16)
Checkpoint_path = "D:/Reza/My Python Projects/New Project/FFT_Exercise_Paper/ComplexValuedNN/SAR_Data_Compression/RV_CAE/Logs/2023-06-16_10_31_16_Layers 64_128_Net1_Natural_Normal/Models/Model_best_testSQNR.pth.tar"
############################################################ Compress
patch = np.real(np.load("D:/Reza/Data/test_data"))[0] #select only one patch
net1 = FactorizedPrior_Net1(in_channels=in_channels, out_channels=out_channels, layer_num=net1_layers_num, intermediate_channels1=intermediate_channels1, intermediate_channels2=intermediate_channels2).to(device)
print("Loading pretrained states from checkpoint")
checkpoint = torch.load(Checkpoint_path, map_location=device)
update_registered_buffers(net1.entropy_bottleneck, "entropy_bottleneck", ["_quantized_cdf", "_offset", "_cdf_length"], checkpoint["state_dict"])
epoch = checkpoint["epoch"] + 1
net1.load_state_dict(checkpoint["state_dict"])
net1.eval()
###### Compress and save
compressed_outp = net1.compress(torch.tensor(patch).to(device))
with open('Compressed_strings/compressed_patch.bin', 'wb') as f:
f.write(compressed_outp["EB_strings"][0])
############################################################ Decompress
string_path = "Compressed_strings/compressed_patch.bin"
net1 = FactorizedPrior_Net1(in_channels=in_channels, out_channels=out_channels, layer_num=net1_layers_num, intermediate_channels1=intermediate_channels1, intermediate_channels2=intermediate_channels2).to(device)
print("Loading pretrained states from checkpoint")
checkpoint = torch.load(Checkpoint_path, map_location=device)
update_registered_buffers(net1.entropy_bottleneck, "entropy_bottleneck", ["_quantized_cdf", "_offset", "_cdf_length"], checkpoint["state_dict"])
epoch = checkpoint["epoch"] + 1
net1.load_state_dict(checkpoint["state_dict"])
net1.eval()
###### Load and Decompress
with open(string_path, 'rb') as f:
string_temp = f.read()
# Decompress the data
decompressed_outp = net1.decompress(strings=[[string_temp]], shape=bottleneck_size)
decompressed_patch = np.array(np.squeeze(decompressed_outp['rec_image'].cpu().detach().numpy()))
########################################################## Metrics
#compute bitrate using the saved file size
avg_bpp = (os.path.getsize('Compressed_strings/compressed_patch.bin')*8) /(256*256)
#compute bitrate using the likelihoods from the entropy model
def compute_bpp(out_net):
size = out_net['x_hat'].size()
num_pixels = size[0] * size[2] * size[3]
return sum(torch.log(likelihoods).sum() / (-math.log(2) * num_pixels)
for likelihoods in out_net['likelihoods'].values()).item()
outp = net1.forward(torch.tensor(patch).to(device))
print(f'Bit-rate: {compute_bpp(outp):.3f} bpp')
Is there any mistake that I'm making while training or saving the network?
the bitrate computed using the saved file size is much larger than the bitrate computed using the likelihoods from the entropy model (about 2.7 bpp in comparison to 1.1 bpp).
Did you run model.update(force=True) after training, but before encoding/decoding the images? This is done for you automatically as of CompressAI v1.2.3, but earlier versions definitely will need a manual model.update.
If the aux loss has been properly minimized, it's quite unusual to have a mismatch of more than a few bytes in the file size, since the entropy coder is quite decent and faithful to most typical distributions.
Possible things to try:
- Minimize aux_loss to 0 using the code from #231, then re-run update and eval.
- Try printing out the ranges of the distributions or plotting them to make sure they are sane, i.e., not too wide; typically, three or so channels should have dynamic range of ~50, and the rest should be <5 dynamic range.
- Ensure the evaluation measurements are correct. Compare each file.
Thank you @YodaEmbedding for the answer.
I have CompressAI v1.2.4, however, I am still running net1.entropy_bottleneck.update() before saving the trained network, won't it do the model.update(force=True).
When I run aux_loss = net1.aux_loss() the output is int type and cannot apply backward on it. That's why I am doing aux_loss = net1.entropy_bottleneck.loss() instead.
When I plot the distributions of the entropy model, I get the below figure, which I'm not sure how to interpret
Usually, for lower lambdas models, there are more channels that are "empty" with sharp peaks between 0.8--1.0, with only a few high-variance channels.
 |
|---|
| quality=1 model |
In general, as the lambda/rate/quality get higher, so too does the variance of some portion of the channels:
 |
|---|
| quality=8 model |
Zooming in:
 |
|---|
| quality=8 model |
The above images were generated using:
from compressai.zoo import bmshj2018_factorized
from compressai_trainer.plot import plot_entropy_bottleneck_distributions
for quality in range(1, 9):
model = bmshj2018_factorized(quality=quality, pretrained=True)
fig = plot_entropy_bottleneck_distributions(model.entropy_bottleneck)
fig.write_image(f"eb_distrib_q{quality}.png", scale=2)I noticed that you're using a very large lambda value. Try something smaller, e.g. 0.067. (Which corresponds to
This doesn't explain the reason for the mismatch per se, but it may help reduce it.
Linking to related issue for future reference: #180
I tried 0.067 as the lambda value for training. After 4 epochs, the aux_loss reaches <1 and all the likelihoods are 1, so the computed bpp from the likelihoods is 0 for all of the test samples.
I'm not sure why
It might be worthwhile to try running net1.update(force=True) after net1.load_state_dict(). And then compress/decompress.
Also, confirm that your net1.entropy_bottleneck.quantiles (left, middle, and right points for each distribution) are all [< -10, near 0, > 10] as the plot you posted indicates they would be for tail_mass < 1e-4.
If nothing else works, ensure everything is set up correctly by trying to train/eval using the original CompressAI models and/or training scripts.
Thank you @YodaEmbedding for the help.
I used net1.entropy_bottleneck.update(force=True) after net1.load_state_dict() and now the computed bpps are much closer (about 0.3 bpp difference). Is this difference normal?
It's good to see that they're getting closer. However, the differences are usually very small, e.g. 0.0001bpp. Is that the only EntropyBottleneck / GaussianConditional used by your model?
Also, try adding these lines immediately before update():
_update_quantiles(net1.entropy_bottleneck)
quantiles = net1.entropy_bottleneck.quantiles
print((quantiles.min(), quantiles.max())) # This should output something like (-30, 30).
net1.entropy_bottleneck.update(force=True)where:
@torch.no_grad()
def _update_quantiles(self):
device = self.quantiles.device
shape = (self.channels, 1, 1)
low = torch.full(shape, -1e9, device=device)
high = torch.full(shape, 1e9, device=device)
def f(y, self=self):
return self._logits_cumulative(y, stop_gradient=True)
for i in range(len(self.target)):
q_i = _search_target(f, self.target[i], low, high)
self.quantiles[:, :, i] = q_i[:, :, 0]
def _search_target(f, target, low, high):
assert (low <= high).all()
assert ((f(low) <= target) & (target <= f(high))).all()
while not torch.isclose(low, high).all():
mid = (low + high) / 2
f_mid = f(mid)
low = torch.where(f_mid <= target, mid, low)
high = torch.where(f_mid >= target, mid, high)
return (low + high) / 2Your training loop should have minimized aux_loss, but in case it didn't, the above code will reduce it to nearly 0.
I added the suggested lines before net1.entropy_bottleneck.update(force=True) while testing the trained network but it didn't change the performance. The printed output is as following.
(tensor(-50.2349, device='cuda:0', grad_fn=<MinBackward1>), tensor(50.0519, device='cuda:0', grad_fn=<MaxBackward1>))
I have only one EntropyBottleneck in the network but I'm using it to encode two different (highly correlated) feature maps, simultaneously.
I faced another issue as well, when I don't normalize the input data (for one of my applications I cannot normalize) while training, the aux_loss and bpp_loss decrease for a few thousand steps but then start increasing exponentially. I read #148 but still cannot figure out the problem.
Here is the aux_loss and bpp_loss for not-normalized data with different lambda values (This problem does not occur with normalized data).
The first figure is the aux_loss and the second one is the bpp_loss:
However, the aux_loss and bpp_loss don't start increasing at the same step. The bpp_loss starts increasing around step 2500, while the aux_loss keeps decreasing for a while and starts increasing around step 3500.
When I keep on the training, the bpp_loss increases to a point that the final compressed file is larger that the input file (As you can see in the figure, more than 9 bpp for a 8-bit input image)!!! Although the distortion loss (MSE) decreases reasonably and the quality of the reconstructed image is high.
@Reza-Asiyabi did you ever figure this out?