def main(
path: str,
file,
workers: int,
resblocks: int,
n_feats: int,
scale: int,
load: str,
k: int,
crop: int,
) -> None:
configs.n_feats = n_feats
configs.resblocks = resblocks
configs.K = k
configs.scale = scale
print(sys.argv)
print([item for item in dir(configs) if not item.startswith("__")])
if load != "/dev/null":
checkpoint = torch.load(load)
print(f"Loaded model from {load}.")
print("Epoch:", checkpoint["epoch"])
else:
checkpoint = {}
compressor = network.Compressor()
if checkpoint:
compressor.nets.load_state_dict(checkpoint["nets"])
compressor = compressor.cuda()
print(f"Number of parameters: {count_params(compressor.nets)}")
print(compressor.nets)
transforms = [] # type: ignore
if crop > 0:
transforms.insert(0, T.CenterCrop(crop))
dataset = lc_data.ImageFolder(path,
[filename.strip() for filename in file],
scale, T.Compose(transforms))
loader = data.DataLoader(
dataset,
batch_size=1,
shuffle=False,
num_workers=workers,
drop_last=False,
)
print(f"Loaded dataset with {len(dataset)} images")
bits, inp_size, individual_bpsps = run_eval(loader, compressor)
for key in bits.get_keys():
print(f"{key}:", bits.get_scaled_bpsp(key, inp_size))
print("Average total bpsp is: ", bits.get_total_bpsp(inp_size))
# Get the individual images' bpsp
# save the individual_bpsps
with open('test.p', 'wb') as f:
pickle.dump(individual_bpsps, f)
def main(
path: str, file, resblocks: int, n_feats: int, scale: int,
load: str, k: int, save_path: str,
) -> None:
ImageFile.LOAD_TRUNCATED_IMAGES = True
configs.n_feats = n_feats
configs.resblocks = resblocks
configs.K = k
configs.scale = scale
configs.collect_probs = False
print(sys.argv)
checkpoint = torch.load(load)
print(f"Loaded model from {load}.")
print("Epoch:", checkpoint["epoch"])
compressor = network.Compressor()
compressor.nets.load_state_dict(checkpoint["nets"])
compressor = compressor.cuda()
print(compressor.nets)
filenames = [filename.strip() for filename in file]
print(f"Loaded directory with {len(filenames)} images")
os.makedirs(save_path, exist_ok=True)
coder = bitcoding.Bitcoding(compressor)
decoder_time_accumulator = timer.TimeAccumulator()
total_num_bytes = 0
total_num_subpixels = 0
for filename in filenames:
assert filename.endswith(".srec"), (
f"{filename} is not a .srec file")
filepath = os.path.join(path, filename)
with decoder_time_accumulator.execute():
x = coder.decode(filepath)
x = x.byte().squeeze(0).cpu()
img = T.functional.to_pil_image(x)
img.save(os.path.join(save_path, f"{filename[:-5]}.png"))
print(
"Decomp: "
f"{decoder_time_accumulator.mean_time_spent():.3f};\t"
"Decomp Time By Scale: ",
end="")
decomp_scale_times = coder.decomp_scale_times()
print(
", ".join(f"{scale_time:.3f}" for scale_time in decomp_scale_times),
end="; ")
total_num_bytes += os.path.getsize(filepath)
total_num_subpixels += np.prod(x.size())
print(
f"Bpsp: {total_num_bytes*8/total_num_subpixels:.3f}", end="\r")
print()
def main(
train_path: str,
eval_path: str,
train_file,
eval_file,
batch: int,
workers: int,
plot: str,
epochs: int,
resblocks: int,
n_feats: int,
scale: int,
load: str,
lr: float,
eval_iters: int,
lr_epochs: int,
plot_iters: int,
k: int,
clip: float,
crop: int,
gd: str,
) -> None:
ImageFile.LOAD_TRUNCATED_IMAGES = True
configs.n_feats = n_feats
configs.scale = scale
configs.resblocks = resblocks
configs.K = k
configs.plot = plot
print(sys.argv)
os.makedirs(plot, exist_ok=True)
model_load = os.path.join(plot, "train.pth")
if os.path.isfile(model_load):
load = model_load
if os.path.isfile(load) and load != "/dev/null":
checkpoint = torch.load(load)
print(f"Loaded model from {load}.")
print("Epoch:", checkpoint["epoch"])
if checkpoint.get("best_bpsp") is None:
print("Warning: best_bpsp not found!")
else:
configs.best_bpsp = checkpoint["best_bpsp"]
print("Best bpsp:", configs.best_bpsp)
else:
checkpoint = {}
compressor = network.Compressor()
if checkpoint:
compressor.nets.load_state_dict(checkpoint["nets"])
compressor = compressor.cuda()
optimizer: optim.Optimizer # type: ignore
if gd == "adam":
optimizer = optim.Adam(compressor.parameters(), lr=lr, weight_decay=0)
elif gd == "sgd":
optimizer = optim.SGD(compressor.parameters(),
lr=lr,
momentum=0.9,
nesterov=True)
elif gd == "rmsprop":
optimizer = optim.RMSprop( # type: ignore
compressor.parameters(), lr=lr)
else:
raise NotImplementedError(gd)
starting_epoch = checkpoint.get("epoch") or 0
print(compressor)
train_dataset = lc_data.ImageFolder(
train_path,
[filename.strip() for filename in train_file],
scale,
T.Compose([
T.RandomHorizontalFlip(),
T.RandomCrop(crop),
]),
)
dataset_index = checkpoint.get("index") or 0
train_sampler = lc_data.PreemptiveRandomSampler(
checkpoint.get("sampler_indices")
or torch.randperm(len(train_dataset)).tolist(),
dataset_index,
)
train_loader = data.DataLoader(
train_dataset,
batch_size=batch,
sampler=train_sampler,
num_workers=workers,
drop_last=True,
)
print(f"Loaded training dataset with {len(train_loader)} batches "
f"and {len(train_loader.dataset)} images")
eval_dataset = lc_data.ImageFolder(
eval_path,
[filename.strip() for filename in eval_file],
scale,
T.Lambda(lambda x: x),
)
eval_loader = data.DataLoader(
eval_dataset,
batch_size=1,
shuffle=False,
num_workers=workers,
drop_last=False,
)
print(f"Loaded eval dataset with {len(eval_loader)} batches "
f"and {len(eval_dataset)} images")
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, lr_epochs, gamma=0.75)
for _ in range(starting_epoch):
lr_scheduler.step() # type: ignore
train_iter = checkpoint.get("train_iter") or 0
if eval_iters == 0:
eval_iters = len(train_loader)
for epoch in range(starting_epoch, epochs):
with tensorboard.SummaryWriter(plot) as plotter:
# input: List[Tensor], downsampled images.
# sizes: N scale 4
for _, inputs in train_loader:
train_iter += 1
batch_size = inputs[0].shape[0]
train_loop(inputs, compressor, optimizer, train_iter, plotter,
plot_iters, clip)
# Increment dataset_index before checkpointing because
# dataset_index is starting index of index of the FIRST
# unseen piece of data.
dataset_index += batch_size
if train_iter % plot_iters == 0:
plotter.add_scalar(
"train/lr",
lr_scheduler.get_lr()[0], # type: ignore
train_iter)
save(compressor, train_sampler.indices, dataset_index,
epoch, train_iter, plot, "train.pth")
if train_iter % eval_iters == 0:
run_eval(eval_loader, compressor, train_iter, plotter,
epoch)
lr_scheduler.step() # type: ignore
dataset_index = 0
with tensorboard.SummaryWriter(plot) as plotter:
run_eval(eval_loader, compressor, train_iter, plotter, epochs)
save(compressor, train_sampler.indices, train_sampler.index, epochs,
train_iter, plot, "train.pth")
print("training done")
def main(
path: str,
file,
resblocks: int,
n_feats: int,
scale: int,
load: str,
k: int,
crop: int,
log_likelihood: bool,
decode: bool,
save_path: str,
) -> None:
configs.n_feats = n_feats
configs.resblocks = resblocks
configs.K = k
configs.scale = scale
configs.log_likelihood = log_likelihood
configs.collect_probs = True
print(sys.argv)
checkpoint = torch.load(load)
print(f"Loaded model from {load}.")
print("Epoch:", checkpoint["epoch"])
compressor = network.Compressor()
compressor.nets.load_state_dict(checkpoint["nets"])
compressor = compressor.cuda()
print(compressor.nets)
transforms = [] # type: ignore
if crop > 0:
transforms.insert(0, T.CenterCrop(crop))
dataset = lc_data.ImageFolder(path,
[filename.strip() for filename in file],
scale, T.Compose(transforms))
loader = data.DataLoader(
dataset,
batch_size=1,
shuffle=False,
num_workers=0,
drop_last=False,
)
print(f"Loaded directory with {len(dataset)} images")
os.makedirs(save_path, exist_ok=True)
coder = bitcoding.Bitcoding(compressor)
encoder_time_accumulator = timer.TimeAccumulator()
decoder_time_accumulator = timer.TimeAccumulator()
total_file_bytes = 0
total_num_subpixels = 0
total_entropy_coding_bytes: np.ndarray = 0 # type: ignore
total_log_likelihood_bits = network.Bits()
for i, (filenames, x) in enumerate(loader):
assert len(filenames) == 1, filenames
filename = filenames[0]
file_id = filename.split(".")[0]
filepath = os.path.join(save_path, f"{file_id}.srec")
with encoder_time_accumulator.execute():
log_likelihood_bits, entropy_coding_bytes = coder.encode(
x, filepath)
total_file_bytes += os.path.getsize(filepath)
total_entropy_coding_bytes += np.array(entropy_coding_bytes)
total_num_subpixels += np.prod(x.size())
if configs.log_likelihood:
total_log_likelihood_bits.add_bits(log_likelihood_bits)
if decode:
with decoder_time_accumulator.execute():
y = coder.decode(filepath)
y = y.cpu()
assert torch.all(x == y), (x[x != y], y[x != y])
if configs.log_likelihood:
theoretical_bpsp = total_log_likelihood_bits.get_total_bpsp(
total_num_subpixels).item()
print(f"Theoretical Bpsp: {theoretical_bpsp:.3f};\t", end="")
print(
f"Bpsp: {total_file_bytes*8/total_num_subpixels:.3f};\t"
f"Images: {i + 1};\t"
f"Comp: {encoder_time_accumulator.mean_time_spent():.3f};\t",
end="")
if decode:
print(
"Decomp: "
f"{decoder_time_accumulator.mean_time_spent():.3f}",
end="")
print(end="\r")
print()
if decode:
print("Decomp Time By Scale: ", end="")
print(", ".join(f"{scale_time:.3f}"
for scale_time in coder.decomp_scale_times()))
else:
print("Scale Bpsps: ", end="")
print(", ".join(f"{scale_bpsp:.3f}"
for scale_bpsp in total_entropy_coding_bytes * 8 /
total_num_subpixels))
