-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmain_retr.py
399 lines (331 loc) · 18.2 KB
/
main_retr.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
import os
import warnings
warnings.filterwarnings("ignore")
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlimit(resource.RLIMIT_NOFILE, (8192, rlimit[1]))
import torch
import torch.nn as nn
import torchvision
torch.cuda.empty_cache()
torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
torch.multiprocessing.set_sharing_strategy('file_system')
from pytorch3d.renderer import (
VolumeRenderer,
NDCMultinomialRaysampler,
)
from pytorch3d.renderer.cameras import (
CamerasBase,
FoVPerspectiveCameras,
look_at_view_transform
)
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
from pytorch_lightning.callbacks import LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning import Trainer, LightningModule
from argparse import ArgumentParser
from typing import Optional, Sequence
from datamodule import UnpairedDataModule
from retr.inverse_renderer import RetrFrontToBackInverseRenderer
from dvr.renderer import DirectVolumeFrontToBackRenderer
class UnetLightningModule(LightningModule):
def __init__(self, hparams, **kwargs):
super().__init__()
self.logsdir = hparams.logsdir
self.lr = hparams.lr
self.shape = hparams.shape
self.filter = hparams.filter
self.weight_decay = hparams.weight_decay
self.batch_size = hparams.batch_size
self.devices = hparams.devices
self.save_hyperparameters()
self.fwd_renderer = DirectVolumeFrontToBackRenderer(
image_width=self.shape,
image_height=self.shape,
n_pts_per_ray=400,
min_depth=2.0,
max_depth=6.0
)
self.inv_renderer = RetrFrontToBackInverseRenderer(
in_channels=3
)
self.loss_smoothl1 = nn.SmoothL1Loss(reduction="mean", beta=0.02)
def forward(self, figures, elevation, azimuth):
return self.inv_renderer(
torch.cat([
figures,
elevation.view(-1,1,1,1).repeat(1,1,self.shape,self.shape),
azimuth.view(-1,1,1,1).repeat(1,1,self.shape,self.shape),
], dim=1)
)
def _common_step(self, batch, batch_idx, optimizer_idx, stage: Optional[str] = 'evaluation'):
_device = batch["image3d"].device
image3d = batch["image3d"]
image2d = batch["image2d"]
if stage=='train':
if (batch_idx % 2) == 1:
masked = image3d>0
noises = torch.rand_like(image3d) * masked.to(image3d.dtype)
alpha_ = torch.rand(self.batch_size, 1, 1, 1, 1, device=_device)
alpha_ = alpha_.expand_as(image3d)
image3d = alpha_ * image3d + (1 - alpha_) * noises
# Construct the locked camera
dist_locked = 4.0 * torch.ones(self.batch_size, device=_device)
elev_locked = torch.ones(self.batch_size, device=_device) * 0
azim_locked = torch.ones(self.batch_size, device=_device) * 0
R_locked, T_locked = look_at_view_transform(dist=dist_locked, elev=elev_locked, azim=azim_locked)
camera_locked = FoVPerspectiveCameras(R=R_locked, T=T_locked, fov=45, aspect_ratio=1).to(_device)
# Construct the random camera
dist_random = 4.0 * torch.ones(self.batch_size, device=_device)
elev_random = torch.rand(self.batch_size, device=_device) * 180 - 90
azim_random = torch.rand(self.batch_size, device=_device) * 360
R_random, T_random = look_at_view_transform(dist=dist_random, elev=elev_random, azim=azim_random)
camera_random = FoVPerspectiveCameras(R=R_random, T=T_random, fov=45, aspect_ratio=1).to(_device)
# CT pathway
src_volume_ct = image3d
src_opaque_ct = torch.ones_like(src_volume_ct)
est_figure_ct_locked = self.fwd_renderer.forward(
image3d=src_volume_ct,
opacity=src_opaque_ct,
cameras=camera_locked
)
est_figure_ct_random = self.fwd_renderer.forward(
image3d=src_volume_ct,
opacity=src_opaque_ct,
cameras=camera_random
)
# XR pathway
src_figure_xr_hidden = image2d
figure_dx = torch.cat([src_figure_xr_hidden, est_figure_ct_locked, est_figure_ct_random])
elev_dx = torch.cat([elev_locked, elev_locked, elev_random])
azim_dx = torch.cat([azim_locked, azim_locked, azim_random])
denses_dx, opaque_dx = self.forward(figure_dx, elevation=elev_dx, azimuth=azim_dx)
est_denses_xr, est_denses_ct, est_denses_rn = torch.split(denses_dx, self.batch_size)
est_volume_xr = est_denses_xr.mean(dim=1, keepdim=True)
est_volume_ct = est_denses_ct.mean(dim=1, keepdim=True)
est_volume_rn = est_denses_rn.mean(dim=1, keepdim=True)
est_opaque_xr, est_opaque_ct, est_opaque_rn = torch.split(opaque_dx, self.batch_size)
est_figure_xr_locked = self.fwd_renderer.forward(
image3d=est_denses_xr,
opacity=est_opaque_xr,
cameras=camera_locked
)
rec_figure_ct_locked = self.fwd_renderer.forward(
image3d=est_denses_ct,
opacity=est_opaque_ct,
cameras=camera_locked
)
rec_figure_ct_random = self.fwd_renderer.forward(
image3d=est_denses_ct,
opacity=est_opaque_ct,
cameras=camera_random
)
rec_figure_rn_locked = self.fwd_renderer.forward(
image3d=est_denses_rn,
opacity=est_opaque_rn,
cameras=camera_locked
)
rec_figure_rn_random = self.fwd_renderer.forward(
image3d=est_denses_rn,
opacity=est_opaque_rn,
cameras=camera_random
)
# Compute the loss
im3d_loss = self.loss_smoothl1(src_volume_ct, est_volume_ct) \
+ self.loss_smoothl1(src_volume_ct, est_volume_rn)
im2d_loss = self.loss_smoothl1(src_figure_xr_hidden, est_figure_xr_locked) \
+ self.loss_smoothl1(est_figure_ct_locked, rec_figure_ct_locked) \
+ self.loss_smoothl1(est_figure_ct_random, rec_figure_ct_random) \
+ self.loss_smoothl1(est_figure_ct_locked, rec_figure_rn_locked) \
+ self.loss_smoothl1(est_figure_ct_random, rec_figure_rn_random)
loss = im3d_loss + im2d_loss
if batch_idx == 0:
viz2d = torch.cat([
torch.cat([src_volume_ct[..., self.shape//2, :],
est_opaque_ct[..., self.shape//2, :],
est_figure_ct_random,
est_figure_ct_locked,
est_volume_ct[..., self.shape//2, :],], dim=-2).transpose(2, 3),
torch.cat([rec_figure_ct_random,
rec_figure_ct_locked,
src_figure_xr_hidden,
est_volume_xr[..., self.shape//2, :],
est_figure_xr_locked,], dim=-2).transpose(2, 3)
], dim=-2)
grid = torchvision.utils.make_grid(viz2d, normalize=False, scale_each=False, nrow=1, padding=0)
tensorboard = self.logger.experiment
tensorboard.add_image(f'{stage}_samples', grid.clamp(0., 1.), self.current_epoch*self.batch_size + batch_idx)
self.log(f'{stage}_im2d_loss', im2d_loss, on_step=(stage == 'train'), prog_bar=True, logger=True, sync_dist=True, batch_size=self.batch_size)
self.log(f'{stage}_im3d_loss', im3d_loss, on_step=(stage == 'train'), prog_bar=True, logger=True, sync_dist=True, batch_size=self.batch_size)
info = {f'loss': loss}
return info
def training_step(self, batch, batch_idx):
return self._common_step(batch, batch_idx, optimizer_idx=0, stage='train')
def validation_step(self, batch, batch_idx):
return self._common_step(batch, batch_idx, optimizer_idx=0, stage='validation')
def test_step(self, batch, batch_idx):
return self._common_step(batch, batch_idx, optimizer_idx=0, stage='test')
def _common_epoch_end(self, outputs, stage: Optional[str] = 'common'):
loss = torch.stack([x[f'loss'] for x in outputs]).mean()
self.log(f'{stage}_loss_epoch', loss, on_step=False, prog_bar=True, logger=True, sync_dist=True)
def train_epoch_end(self, outputs):
return self._common_epoch_end(outputs, stage='train')
def validation_epoch_end(self, outputs):
return self._common_epoch_end(outputs, stage='validation')
def test_epoch_end(self, outputs):
return self._common_epoch_end(outputs, stage='test')
def configure_optimizers(self):
optimizer = torch.optim.RAdam(self.parameters(), lr=self.lr, weight_decay=self.weight_decay)
# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10, eta_min=self.lr / 10)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[100, 200], gamma=0.1)
return [optimizer], [scheduler]
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--conda_env", type=str, default="Unet")
parser.add_argument("--notification_email", type=str, default="[email protected]")
# Model arguments
parser.add_argument("--batch_size", type=int, default=1, help="size of the batches")
parser.add_argument("--shape", type=int, default=256, help="spatial size of the tensor")
parser.add_argument("--epochs", type=int, default=301, help="number of epochs")
parser.add_argument("--train_samples", type=int, default=1000, help="training samples")
parser.add_argument("--val_samples", type=int, default=400, help="validation samples")
parser.add_argument("--test_samples", type=int, default=400, help="test samples")
parser.add_argument("--weight_decay", type=float, default=1e-4, help="Weight decay")
parser.add_argument("--lr", type=float, default=1e-4, help="adam: learning rate")
parser.add_argument("--ckpt", type=str, default=None, help="path to checkpoint")
parser.add_argument("--logsdir", type=str, default='logsfrecaling', help="logging directory")
parser.add_argument("--datadir", type=str, default='data', help="data directory")
parser.add_argument("--filter", type=str, default='sobel', help="None, sobel, laplacian, canny")
parser = Trainer.add_argparse_args(parser)
# Collect the hyper parameters
hparams = parser.parse_args()
# Seed the application
seed_everything(42)
# Callback
checkpoint_callback = ModelCheckpoint(
dirpath=hparams.logsdir,
filename='{epoch:02d}-{validation_loss_epoch:.2f}',
save_top_k=-1,
save_last=True,
every_n_epochs=5,
)
lr_callback = LearningRateMonitor(logging_interval='step')
# Logger
tensorboard_logger = TensorBoardLogger(save_dir=hparams.logsdir, log_graph=True)
# Init model with callbacks
trainer = Trainer.from_argparse_args(
hparams,
max_epochs=hparams.epochs,
logger=[tensorboard_logger],
callbacks=[
lr_callback,
checkpoint_callback,
],
# accumulate_grad_batches=5,
# strategy="ddp_sharded", #"horovod", #"deepspeed", #"ddp_sharded",
strategy="fsdp", # "fsdp", #"ddp_sharded", #"horovod", #"deepspeed", #"ddp_sharded",
precision=16, # if hparams.use_amp else 32,
# stochastic_weight_avg=True,
# deterministic=False,
# profiler="simple",
)
# Create data module
train_image3d_folders = [
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/NSCLC/processed/train/images'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-0'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-1'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-2'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-3'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-4'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/Imagenglab/processed/train/images'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MELA2022/raw/train/images'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MELA2022/raw/val/images'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/AMOS2022/raw/train/images'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/AMOS2022/raw/val/images'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2019/raw/train/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2020/raw/train/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2019/raw/val/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2020/raw/val/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2019/raw/test/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2020/raw/test/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/UWSpine/processed/train/images'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/UWSpine/processed/test/images/'),
]
train_label3d_folders = [
]
train_image2d_folders = [
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/JSRT/processed/images/'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/ChinaSet/processed/images/'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/Montgomery/processed/images/'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/VinDr/v1/processed/train/images/'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/VinDr/v1/processed/test/images/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/T62020/20200501/raw/images'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/T62021/20211101/raw/images'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/VinDr/v1/processed/train/images/'),
# # os.path.join(hparams.datadir, 'SpineXRVertSegmentation/VinDr/v1/processed/test/images/'),
]
train_label2d_folders = [
]
val_image3d_folders = [
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/NSCLC/processed/train/images'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-0'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-1'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-2'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-3'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-4'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/Imagenglab/processed/train/images'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MELA2022/raw/train/images'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MELA2022/raw/val/images'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/AMOS2022/raw/train/images'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/AMOS2022/raw/val/images'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2019/raw/train/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2020/raw/train/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2019/raw/val/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2020/raw/val/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2019/raw/test/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2020/raw/test/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/UWSpine/processed/train/images'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/UWSpine/processed/test/images/'),
]
val_image2d_folders = [
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/JSRT/processed/images/'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/ChinaSet/processed/images/'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/Montgomery/processed/images/'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/VinDr/v1/processed/train/images/'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/VinDr/v1/processed/test/images/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/T62020/20200501/raw/images'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/T62021/20211101/raw/images'),
# # os.path.join(hparams.datadir, 'SpineXRVertSegmentation/VinDr/v1/processed/train/images/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/VinDr/v1/processed/test/images/'),
]
test_image3d_folders = val_image3d_folders
test_image2d_folders = val_image2d_folders
datamodule = UnpairedDataModule(
train_image3d_folders=train_image3d_folders,
train_image2d_folders=train_image2d_folders,
val_image3d_folders=val_image3d_folders,
val_image2d_folders=val_image2d_folders,
test_image3d_folders=test_image3d_folders,
test_image2d_folders=test_image2d_folders,
train_samples=hparams.train_samples,
val_samples=hparams.val_samples,
test_samples=hparams.test_samples,
batch_size=hparams.batch_size,
img_shape=hparams.shape,
vol_shape=hparams.shape
)
datamodule.setup()
####### Test camera mu and bandwidth ########
# test_random_uniform_cameras(hparams, datamodule)
#############################################
model = UnetLightningModule(
hparams=hparams
)
model = model.load_from_checkpoint(hparams.ckpt, strict=False) if hparams.ckpt is not None else model
trainer.fit(
model,
datamodule,
)
# test
# serve