def main():
logger = VisdomLogger("train", env=JOB)
logger.add_hook(lambda logger, data: logger.step(),
feature="energy",
freq=16)
logger.add_hook(
lambda logger, data: logger.plot(data["energy"], "free_energy"),
feature="energy",
freq=100)
task_list = [
tasks.rgb,
tasks.normal,
tasks.principal_curvature,
tasks.sobel_edges,
tasks.depth_zbuffer,
tasks.reshading,
tasks.edge_occlusion,
tasks.keypoints3d,
tasks.keypoints2d,
]
reality = RealityTask('ood',
dataset=ImagePairDataset(data_dir=OOD_DIR,
resize=(256, 256)),
tasks=[tasks.rgb, tasks.rgb],
batch_size=28)
# reality = RealityTask('almena',
# dataset=TaskDataset(
# buildings=['almena'],
# tasks=task_list,
# ),
# tasks=task_list,
# batch_size=8
# )
graph = TaskGraph(tasks=[reality, *task_list], batch_size=28)
task = tasks.rgb
images = [reality.task_data[task]]
sources = [task.name]
for _, edge in sorted(
((edge.dest_task.name, edge) for edge in graph.adj[task])):
if isinstance(edge.src_task, RealityTask): continue
reality.task_data[edge.src_task]
x = edge(reality.task_data[edge.src_task])
if edge.dest_task != tasks.normal:
edge2 = graph.edge_map[(edge.dest_task.name, tasks.normal.name)]
x = edge2(x)
images.append(x.clamp(min=0, max=1))
sources.append(edge.dest_task.name)
logger.images_grouped(images, ", ".join(sources), resize=256)
def main():
logger = VisdomLogger("train", env=JOB)
logger.add_hook(lambda logger, data: logger.step(), feature="energy", freq=16)
logger.add_hook(lambda logger, data: logger.plot(data["energy"], "free_energy"), feature="energy", freq=100)
task_list = [
tasks.rgb,
tasks.normal,
tasks.principal_curvature,
tasks.sobel_edges,
tasks.depth_zbuffer,
tasks.reshading,
tasks.edge_occlusion,
tasks.keypoints3d,
tasks.keypoints2d,
]
reality = RealityTask('almena',
dataset=TaskDataset(
buildings=['almena'],
tasks=task_list,
),
tasks=task_list,
batch_size=8
)
graph = TaskGraph(
tasks=[reality, *task_list],
batch_size=8
)
for task in graph.tasks:
if isinstance(task, RealityTask): continue
images = [reality.task_data[task].clamp(min=0, max=1)]
sources = [task.name]
for _, edge in sorted(((edge.src_task.name, edge) for edge in graph.in_adj[task])):
if isinstance(edge.src_task, RealityTask): continue
x = edge(reality.task_data[edge.src_task])
images.append(x.clamp(min=0, max=1))
sources.append(edge.src_task.name)
logger.images_grouped(images, ", ".join(sources), resize=192)
def main():
task_list = [
tasks.rgb,
tasks.normal,
tasks.principal_curvature,
tasks.sobel_edges,
tasks.depth_zbuffer,
tasks.reshading,
tasks.edge_occlusion,
tasks.keypoints3d,
tasks.keypoints2d,
]
reality = RealityTask('almena',
dataset=TaskDataset(buildings=['almena'],
tasks=[
tasks.rgb, tasks.normal,
tasks.principal_curvature,
tasks.depth_zbuffer
]),
tasks=[
tasks.rgb, tasks.normal,
tasks.principal_curvature, tasks.depth_zbuffer
],
batch_size=4)
graph = TaskGraph(
tasks=[reality, *task_list],
anchored_tasks=[reality, tasks.rgb],
reality=reality,
batch_size=4,
edges_exclude=[
('almena', 'normal'),
('almena', 'principal_curvature'),
('almena', 'depth_zbuffer'),
# ('rgb', 'keypoints3d'),
# ('rgb', 'edge_occlusion'),
],
initialize_first_order=True,
)
graph.p.compile(torch.optim.Adam, lr=4e-2)
graph.estimates.compile(torch.optim.Adam, lr=1e-2)
logger = VisdomLogger("train", env=JOB)
logger.add_hook(lambda logger, data: logger.step(),
feature="energy",
freq=16)
logger.add_hook(
lambda logger, data: logger.plot(data["energy"], "free_energy"),
feature="energy",
freq=100)
logger.add_hook(lambda logger, data: graph.plot_estimates(logger),
feature="epoch",
freq=32)
logger.add_hook(lambda logger, data: graph.update_paths(logger),
feature="epoch",
freq=32)
graph.plot_estimates(logger)
graph.plot_paths(logger,
dest_tasks=[
tasks.normal, tasks.principal_curvature,
tasks.depth_zbuffer
],
show_images=False)
for epochs in range(0, 4000):
logger.update("epoch", epochs)
with torch.no_grad():
free_energy = graph.free_energy(sample=16)
graph.averaging_step()
logger.update("energy", free_energy)
def main(
loss_config="conservative_full",
mode="standard",
visualize=False,
pretrained=True,
finetuned=False,
fast=False,
batch_size=None,
ood_batch_size=None,
subset_size=None,
cont=f"{BASE_DIR}/shared/results_LBP_multipercep_lat_winrate_8/graph.pth",
cont_gan=None,
pre_gan=None,
max_epochs=800,
use_baseline=False,
**kwargs,
):
# CONFIG
batch_size = batch_size or (4 if fast else 64)
energy_loss = get_energy_loss(config=loss_config, mode=mode, **kwargs)
# DATA LOADING
train_dataset, val_dataset, train_step, val_step = load_train_val(
energy_loss.get_tasks("train"),
energy_loss.get_tasks("val"),
batch_size=batch_size,
fast=fast,
)
train_subset_dataset, _, _, _ = load_train_val(
energy_loss.get_tasks("train_subset"),
batch_size=batch_size,
fast=fast,
subset_size=subset_size)
if not fast:
train_step, val_step = train_step // (16 * 4), val_step // (16)
test_set = load_test(energy_loss.get_tasks("test"))
ood_set = load_ood(energy_loss.get_tasks("ood"))
train = RealityTask("train",
train_dataset,
batch_size=batch_size,
shuffle=True)
train_subset = RealityTask("train_subset",
train_subset_dataset,
batch_size=batch_size,
shuffle=True)
val = RealityTask("val", val_dataset, batch_size=batch_size, shuffle=True)
test = RealityTask.from_static("test", test_set,
energy_loss.get_tasks("test"))
# ood = RealityTask.from_static("ood", ood_set, [energy_loss.get_tasks("ood")])
# GRAPH
realities = [
train,
val,
test,
] + [train_subset] #[ood]
graph = TaskGraph(tasks=energy_loss.tasks + realities,
finetuned=finetuned,
freeze_list=energy_loss.freeze_list)
graph.compile(torch.optim.Adam, lr=3e-5, weight_decay=2e-6, amsgrad=True)
graph.load_weights(cont)
# LOGGING
logger = VisdomLogger("train", env=JOB)
logger.add_hook(lambda logger, data: logger.step(),
feature="loss",
freq=20)
# logger.add_hook(lambda _, __: graph.save(f"{RESULTS_DIR}/graph.pth"), feature="epoch", freq=1)
energy_loss.logger_hooks(logger)
best_ood_val_loss = float('inf')
energy_losses = []
mse_losses = []
pearsonr_vals = []
percep_losses = defaultdict(list)
pearson_percep = defaultdict(list)
# # TRAINING
# for epochs in range(0, max_epochs):
# logger.update("epoch", epochs)
# if epochs == 0:
# energy_loss.plot_paths(graph, logger, realities, prefix="start" if epochs == 0 else "")
# # if visualize: return
# graph.eval()
# for _ in range(0, val_step):
# with torch.no_grad():
# losses = energy_loss(graph, realities=[val])
# all_perceps = [losses[loss_name] for loss_name in losses if 'percep' in loss_name ]
# energy_avg = sum(all_perceps) / len(all_perceps)
# for loss_name in losses:
# if 'percep' not in loss_name: continue
# percep_losses[loss_name] += [losses[loss_name].data.cpu().numpy()]
# mse = losses['mse']
# energy_losses.append(energy_avg.data.cpu().numpy())
# mse_losses.append(mse.data.cpu().numpy())
# val.step()
# mse_arr = np.array(mse_losses)
# energy_arr = np.array(energy_losses)
# # logger.scatter(mse_arr - mse_arr.mean() / np.std(mse_arr), \
# # energy_arr - energy_arr.mean() / np.std(energy_arr), \
# # 'unit_normal_all', opts={'xlabel':'mse','ylabel':'energy'})
# logger.scatter(mse_arr, energy_arr, \
# 'mse_energy_all', opts={'xlabel':'mse','ylabel':'energy'})
# pearsonr, p = scipy.stats.pearsonr(mse_arr, energy_arr)
# logger.text(f'pearsonr = {pearsonr}, p = {p}')
# pearsonr_vals.append(pearsonr)
# logger.plot(pearsonr_vals, 'pearsonr_all')
# for percep_name in percep_losses:
# percep_loss_arr = np.array(percep_losses[percep_name])
# logger.scatter(mse_arr, percep_loss_arr, f'mse_energy_{percep_name}', \
# opts={'xlabel':'mse','ylabel':'energy'})
# pearsonr, p = scipy.stats.pearsonr(mse_arr, percep_loss_arr)
# pearson_percep[percep_name] += [pearsonr]
# logger.plot(pearson_percep[percep_name], f'pearson_{percep_name}')
# energy_loss.logger_update(logger)
# if logger.data['val_mse : n(~x) -> y^'][-1] < best_ood_val_loss:
# best_ood_val_loss = logger.data['val_mse : n(~x) -> y^'][-1]
# energy_loss.plot_paths(graph, logger, realities, prefix="best")
energy_mean_by_blur = []
energy_std_by_blur = []
mse_mean_by_blur = []
mse_std_by_blur = []
for blur_size in np.arange(0, 10, 0.5):
tasks.rgb.blur_radius = blur_size if blur_size > 0 else None
train_subset.step()
# energy_loss.plot_paths(graph, logger, realities, prefix="start" if epochs == 0 else "")
energy_losses = []
mse_losses = []
for epochs in range(subset_size // batch_size):
with torch.no_grad():
flosses = energy_loss(graph,
realities=[train_subset],
reduce=False)
losses = energy_loss(graph,
realities=[train_subset],
reduce=False)
all_perceps = np.stack([
losses[loss_name].data.cpu().numpy()
for loss_name in losses if 'percep' in loss_name
])
energy_losses += list(all_perceps.mean(0))
mse_losses += list(losses['mse'].data.cpu().numpy())
train_subset.step()
mse_losses = np.array(mse_losses)
energy_losses = np.array(energy_losses)
logger.text(
f'blur_radius = {blur_size}, mse = {mse_losses.mean()}, energy = {energy_losses.mean()}'
)
logger.scatter(mse_losses, energy_losses, \
f'mse_energy, blur = {blur_size}', opts={'xlabel':'mse','ylabel':'energy'})
energy_mean_by_blur += [energy_losses.mean()]
energy_std_by_blur += [np.std(energy_losses)]
mse_mean_by_blur += [mse_losses.mean()]
mse_std_by_blur += [np.std(mse_losses)]
logger.plot(energy_mean_by_blur, f'energy_mean_by_blur')
logger.plot(energy_std_by_blur, f'energy_std_by_blur')
logger.plot(mse_mean_by_blur, f'mse_mean_by_blur')
logger.plot(mse_std_by_blur, f'mse_std_by_blur')
torch.save((perturbation.data, images.data, targets),
f"{output_path}/{k}.pth")
if __name__ == "__main__":
model = DataParallelModel(
DecodingModel(n=DIST_SIZE, distribution=transforms.training))
params = itertools.chain(model.module.classifier.parameters(),
model.module.features[-1].parameters())
optimizer = torch.optim.Adam(params, lr=2.5e-3)
init_data("data/amnesia")
logger = VisdomLogger("train", server="35.230.67.129", port=8000, env=JOB)
logger.add_hook(lambda x: logger.step(), feature="epoch", freq=20)
logger.add_hook(lambda data: logger.plot(data, "train_loss"),
feature="loss",
freq=50)
logger.add_hook(lambda data: logger.plot(data, "train_bits"),
feature="bits",
freq=50)
logger.add_hook(
lambda x: model.save("output/train_test.pth", verbose=True),
feature="epoch",
freq=100)
model.save("output/train_test.pth", verbose=True)
files = glob.glob(f"data/amnesia/*.pth")
for i, save_file in enumerate(
random.choice(files) for i in range(0, 2701)):
from torch.autograd import Variable
from models import DecodingModel, DataParallelModel
from torchvision import models
from logger import Logger, VisdomLogger
from utils import *
import IPython
import transforms
# LOGGING
logger = VisdomLogger("encoding", server="35.230.67.129", port=8000, env=JOB)
logger.add_hook(lambda x: logger.step(), feature="epoch", freq=20)
logger.add_hook(lambda x: logger.plot(x, "Encoding Loss", opts=dict(ymin=0)), feature="loss", freq=50)
"""
Computes the changed images, given a a specified perturbation, standard deviation weighting,
and epsilon.
"""
def compute_changed_images(images, perturbation, std_weights, epsilon=EPSILON):
perturbation_w2 = perturbation * std_weights
perturbation_zc = (
perturbation_w2
/ perturbation_w2.view(perturbation_w2.shape[0], -1)
.norm(2, dim=1, keepdim=True)