-
Notifications
You must be signed in to change notification settings - Fork 1
/
pl_module_sam_seg.py
188 lines (147 loc) · 7.06 KB
/
pl_module_sam_seg.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
from functools import partial
import torch
from pytorch_lightning import LightningModule
from torch import nn
import torch.nn.functional as F
from torchmetrics import MetricCollection
import time
from losses import SAMLoss
class SamSeg(LightningModule):
def __init__(
self,
cfg,
sam_model: nn.Module,
metrics: MetricCollection,
num_classes: int,
focal_cof: float = 20.,
dice_cof: float = 1.,
iou_cof: float = 1.,
ce_cof: float = 0.,
lr: float = 0.0001,
weight_decay: float = 0.01,
lr_steps: list = (10, 20),
warmup_steps: int = 0,
ignored_index=None,
):
super().__init__()
self.save_hyperparameters(ignore=["sam_model", "metrics"]) # 这将自动记录所有通过 __init__ 传入的参数
self.model = sam_model
self.num_classes = num_classes
self.loss = SAMLoss(focal_cof, dice_cof, ce_cof, iou_cof)
self.train_metrics = metrics.clone(postfix='/train')
self.valid_metrics = nn.ModuleList([metrics.clone(postfix='/val'), metrics.clone(postfix='/test')])
self.test_metrics = metrics.clone(prefix='final_test/')
self.lr = lr
self.ignored_index = ignored_index
self.time_and_cnt = [0., 0]
def forward(self, images):
# use forward for inference/predictions
pred_masks, iou_predictions = self.model(images)
# pred_masks and iou_predictions are lists 将list 变成 torch 张量
pred_masks = torch.stack(pred_masks, dim=0)
iou_predictions = torch.stack(iou_predictions, dim=0)
return pred_masks, iou_predictions
def calc_loss(self, pred_masks, gt_masks, iou_predictions, ignored_masks):
loss_dict = self.loss(pred_masks, gt_masks, iou_predictions, ignored_masks=ignored_masks)
assert "loss" in loss_dict
return loss_dict
@torch.no_grad()
def process_masks(self, gt_masks):
# gt_cls_masks = [gt_masks == i for i in range(0, self.num_classes + 1)]
ignored_masks = gt_masks == 0
# gt_cls_masks = torch.stack(gt_cls_masks[1:], dim=1).float()
ignored_masks = ignored_masks.unsqueeze(1).long()
return gt_masks, ignored_masks
def predict_mask(self, pred_masks, gt_masks, ignored_masks):
# pred_masks = [batch_size, #classes, h, w]
# note class 0 is always for ignored classes
pred_masks = torch.argmax(pred_masks[:, 1:, ...], dim=1) + 1
pred_masks = pred_masks * (1 - ignored_masks.squeeze(1))
if self.ignored_index is not None:
pred_masks[pred_masks == self.ignored_index] = 0
gt_masks[gt_masks == self.ignored_index] = 0
return pred_masks, gt_masks
def training_step(self, batch, batch_idx):
images, gt_masks = batch
gt_masks, ignored_masks = self.process_masks(gt_masks)
pred_masks, iou_predictions = self(images)
losses = self.calc_loss(pred_masks, gt_masks, iou_predictions, ignored_masks=ignored_masks)
self.log_losses(losses, "train")
mask_cls_pred, gt_masks = self.predict_mask(pred_masks, gt_masks, ignored_masks=ignored_masks)
self.train_metrics.update(mask_cls_pred, gt_masks)
# self.train_metrics(mask_cls_pred, gt_masks)
self.log_dict(self.train_metrics.compute(), on_step=False, on_epoch=True)
return losses["loss"]
def on_train_epoch_end(self):
self.log_dict(self.train_metrics.compute())
self.train_metrics.reset()
def validation_step(self, batch, batch_idx, dataloader_idx=None):
images, gt_masks = batch
gt_masks, ignored_masks = self.process_masks(gt_masks)
prefix = get_prefix_from_val_id(dataloader_idx)
metrics_idx = dataloader_idx if dataloader_idx is not None else 0
pred_masks, iou_predictions = self(images)
losses = self.calc_loss(pred_masks, gt_masks, iou_predictions, ignored_masks=ignored_masks)
mask_cls_pred, gt_masks = self.predict_mask(pred_masks, gt_masks, ignored_masks=ignored_masks)
if not self.trainer.sanity_checking:
self.log_losses(losses, prefix)
self.valid_metrics[metrics_idx].update(mask_cls_pred, gt_masks)
# self.valid_metrics[metrics_idx](mask_cls_pred, gt_masks)
# self.log_dict(self.valid_metrics[metrics_idx], on_step=False, on_epoch=True,
# add_dataloader_idx=False)
def on_validation_epoch_end(self):
if not self.trainer.sanity_checking:
for valM in self.valid_metrics:
self.log_dict(valM.compute(), add_dataloader_idx=False)
valM.reset()
def predict_step(self, batch, batch_idx, dataloader_idx: int = 0):
images, gt_masks = batch
gt_masks, ignored_masks = self.process_masks(gt_masks)
# pred_masks, iou_predictions = self(images)
with torch.no_grad():
time_start = time.perf_counter()
pred_masks, iou_predictions = self.model(images)
time_predict = time.perf_counter() - time_start
pred_masks = torch.stack(pred_masks, dim=0)
iou_predictions = torch.stack(iou_predictions, dim=0)
self.time_and_cnt[0] += time_predict
self.time_and_cnt[1] += 1
print("Average prediction time: %f" % (self.time_and_cnt[0] / self.time_and_cnt[1]))
mask_cls_pred, gt_masks = self.predict_mask(pred_masks, gt_masks, ignored_masks=ignored_masks)
return mask_cls_pred
def log_losses(self, losses, prefiex):
if prefiex == "train":
for t in losses:
self.log("Loss/%s_%s" % (prefiex, t), losses[t], on_epoch=True, on_step=True, sync_dist=True)
else:
for t in losses:
self.log("Loss/%s_%s" % (prefiex, t), losses[t], on_epoch=True, on_step=False, sync_dist=True,
add_dataloader_idx=False)
def configure_optimizers(self):
# self.hparams available because we called self.save_hyperparameters()
optimizer = torch.optim.AdamW(self.parameters(), lr=self.lr, weight_decay=self.hparams.weight_decay)
# scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, self.hparams.lr_steps, verbose=False)
def lr_lambda(step):
if step < self.hparams.warmup_steps:
return step / self.hparams.warmup_steps
elif step < self.hparams.lr_steps[0]:
return 1.0
elif step < self.hparams.lr_steps[1]:
return 0.1
else:
return 0.01
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_lambda, verbose=False)
return {
'optimizer': optimizer,
'lr_scheduler': {
'scheduler': scheduler,
'interval': 'step'
}
}#[optimizer], [scheduler]
def get_prefix_from_val_id(dataloader_idx):
if dataloader_idx is None or dataloader_idx == 0:
return "val"
elif dataloader_idx == 1:
return "test"
else:
raise NotImplementedError