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advseg.py
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advseg.py
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# The adversarial domain adaptation is based on:
# https://github.com/wasidennis/AdaptSegNet
# Note from https://github.com/wasidennis/AdaptSegNet#note:
# The model and code are available for non-commercial research purposes only.
import os
import torch
import torch.nn.functional as F
from matplotlib import pyplot as plt
from torch import nn, optim
from torch.autograd import Variable
from mmseg.core import add_prefix
from mmseg.models import UDA, HRDAEncoderDecoder
from mmseg.models.uda.fcdiscriminator import FCDiscriminator
from mmseg.models.uda.uda_decorator import UDADecorator
from mmseg.models.utils.dacs_transforms import denorm, get_mean_std
from mmseg.models.utils.visualization import subplotimg
from mmseg.ops import resize
@UDA.register_module()
class AdvSeg(UDADecorator):
def __init__(self, **cfg):
super(AdvSeg, self).__init__(**cfg)
self.local_iter = 0
self.num_classes = cfg['model']['decode_head']['num_classes']
self.max_iters = cfg['max_iters']
self.lr_D = cfg['lr_D']
self.lr_D_power = cfg['lr_D_power']
self.lr_D_min = cfg['lr_D_min']
self.discriminator_type = cfg['discriminator_type']
self.lambda_adv_target = cfg['lambda_adv_target']
self.debug_img_interval = cfg['debug_img_interval']
self.model_D = nn.ModuleDict()
self.optimizer_D = {}
for k in ['main', 'aux'] if self.model.with_auxiliary_head \
else ['main']:
self.model_D[k] = FCDiscriminator(num_classes=self.num_classes)
self.model_D[k].train()
self.model_D[k].cuda()
self.optimizer_D[k] = optim.Adam(
self.model_D[k].parameters(), lr=self.lr_D, betas=(0.9, 0.99))
self.optimizer_D[k].zero_grad()
if self.discriminator_type == 'Vanilla':
self.loss_fn_D = torch.nn.BCEWithLogitsLoss()
elif self.discriminator_type == 'LS':
self.loss_fn_D = torch.nn.MSELoss()
else:
raise NotImplementedError(self.discriminator_type)
def train_step(self, data_batch, optimizer, **kwargs):
"""The iteration step during training.
This method defines an iteration step during training, except for the
back propagation and optimizer updating, which are done in an optimizer
hook. Note that in some complicated cases or models, the whole process
including back propagation and optimizer updating is also defined in
this method, such as GAN.
Args:
data (dict): The output of dataloader.
optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
runner is passed to ``train_step()``. This argument is unused
and reserved.
Returns:
dict: It should contain at least 3 keys: ``loss``, ``log_vars``,
``num_samples``.
``loss`` is a tensor for back propagation, which can be a
weighted sum of multiple losses.
``log_vars`` contains all the variables to be sent to the
logger.
``num_samples`` indicates the batch size (when the model is
DDP, it means the batch size on each GPU), which is used for
averaging the logs.
"""
optimizer.zero_grad()
for k in self.optimizer_D.keys():
self.optimizer_D[k].zero_grad()
self.adjust_learning_rate_D(self.optimizer_D[k], self.local_iter)
log_vars = self(**data_batch)
optimizer.step()
for k in self.optimizer_D.keys():
self.optimizer_D[k].step()
log_vars.pop('loss', None) # remove the unnecessary 'loss'
outputs = dict(
log_vars=log_vars, num_samples=len(data_batch['img_metas']))
return outputs
def adjust_learning_rate_D(self, optimizer, i_iter):
coeff = (1 - i_iter / self.max_iters)**self.lr_D_power
lr = (self.lr_D - self.lr_D_min) * coeff + self.lr_D_min
assert len(optimizer.param_groups) == 1
optimizer.param_groups[0]['lr'] = lr
def forward_train(self, img, img_metas, gt_semantic_seg, target_img,
target_img_metas):
"""Forward function for training.
Args:
img (Tensor): Input images.
img_metas (list[dict]): List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmseg/datasets/pipelines/formatting.py:Collect`.
gt_semantic_seg (Tensor): Semantic segmentation masks
used if the architecture supports semantic segmentation task.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
source_label = 0
target_label = 1
if self.local_iter % self.debug_img_interval == 0:
self.model.decode_head.debug = True
else:
self.model.decode_head.debug = False
seg_debug = {}
#######################################################################
# Train Generator
#######################################################################
# don't accumulate grads in D
for param in self.model_D.parameters():
param.requires_grad = False
# train with source
source_losses = dict()
pred = self.model.forward_with_aux(img, img_metas)
seg_debug['Source'] = self.get_model().decode_head.debug_output
loss = self.model.decode_head.losses(pred['main'], gt_semantic_seg)
source_losses.update(add_prefix(loss, 'decode'))
if isinstance(self.model, HRDAEncoderDecoder):
self.model.decode_head.reset_crop()
if self.model.with_auxiliary_head:
loss_aux = self.model.auxiliary_head.losses(
pred['aux'], gt_semantic_seg)
source_losses.update(add_prefix(loss_aux, 'aux'))
source_loss, source_log_vars = self._parse_losses(source_losses)
source_loss.backward()
# train with target
pred_trg = self.model.forward_with_aux(target_img, target_img_metas)
if isinstance(self.model, HRDAEncoderDecoder):
self.model.decode_head.reset_crop()
seg_debug['Target'] = self.get_model().decode_head.debug_output
if isinstance(self.model, HRDAEncoderDecoder):
for k in pred.keys():
pred[k] = pred[k][0]
assert self.model.feature_scale == 0.5
pred[k] = resize(
input=pred[k],
size=[
int(e * self.model.feature_scale)
for e in img.shape[2:]
],
mode='bilinear',
align_corners=self.model.align_corners)
for k in pred_trg.keys():
pred_trg[k] = pred_trg[k][0]
pred_trg[k] = resize(
input=pred_trg[k],
size=[
int(e * self.model.feature_scale)
for e in img.shape[2:]
],
mode='bilinear',
align_corners=self.model.align_corners)
g_trg_losses = dict()
for k in pred_trg.keys():
D_out = self.model_D[k](F.softmax(pred_trg[k], dim=1))
loss_G = self.loss_fn_D(
D_out,
Variable(
torch.FloatTensor(
D_out.data.size()).fill_(source_label)).cuda())
# remember to have the word 'loss' in key
g_trg_losses[
f'G_trg.loss.{k}'] = self.lambda_adv_target[k] * loss_G
g_trg_loss, g_trg_log_vars = self._parse_losses(g_trg_losses)
g_trg_loss.backward()
#######################################################################
# Train Discriminator
#######################################################################
# bring back requires_grad
for param in self.model_D.parameters():
param.requires_grad = True
# train with source
d_src_losses = dict()
for k in pred.keys():
pred[k] = pred[k].detach()
D_out_src = self.model_D[k](F.softmax(pred[k], dim=1))
loss_D = self.loss_fn_D(
D_out_src,
Variable(
torch.FloatTensor(
D_out_src.data.size()).fill_(source_label)).cuda())
d_src_losses[f'D_src.loss.{k}'] = loss_D / 2
d_src_loss, d_src_log_vars = self._parse_losses(d_src_losses)
d_src_loss.backward()
# train with target
d_trg_losses = dict()
for k in pred_trg.keys():
pred_trg[k] = pred_trg[k].detach()
D_out_trg = self.model_D[k](F.softmax(pred_trg[k], dim=1))
loss_D = self.loss_fn_D(
D_out_trg,
Variable(
torch.FloatTensor(
D_out_trg.data.size()).fill_(target_label)).cuda())
d_trg_losses[f'D_trg.loss.{k}'] = loss_D / 2
d_trg_loss, d_trg_log_vars = self._parse_losses(d_trg_losses)
d_trg_loss.backward()
if self.local_iter % self.debug_img_interval == 0:
out_dir = os.path.join(self.train_cfg['work_dir'], 'debug')
os.makedirs(out_dir, exist_ok=True)
batch_size = img.shape[0]
means, stds = get_mean_std(img_metas, target_img.device)
vis_img = torch.clamp(denorm(img, means, stds), 0, 1)
vis_trg_img = torch.clamp(denorm(target_img, means, stds), 0, 1)
for j in range(batch_size):
rows, cols = 2, 3
fig, axs = plt.subplots(
rows,
cols,
figsize=(3 * cols, 3 * rows),
gridspec_kw={
'hspace': 0.1,
'wspace': 0,
'top': 0.95,
'bottom': 0,
'right': 1,
'left': 0
},
)
subplotimg(axs[0][0], vis_img[j], 'Source Image')
subplotimg(
axs[0][1],
torch.argmax(pred['main'][j], dim=0),
'Source Seg',
cmap='cityscapes')
subplotimg(axs[1][0], vis_trg_img[j], 'Target Image')
subplotimg(
axs[1][1],
torch.argmax(pred_trg['main'][j], dim=0),
'Target Seg',
cmap='cityscapes')
subplotimg(
axs[0][2],
D_out_src[j],
'Source Discriminator',
vmin=0,
vmax=1,
cmap='viridis')
subplotimg(
axs[1][2],
D_out_trg[j],
'Target Discriminator',
vmin=0,
vmax=1,
cmap='viridis')
for ax in axs.flat:
ax.axis('off')
plt.savefig(
os.path.join(out_dir,
f'{(self.local_iter + 1):06d}_{j}.png'))
plt.close()
if seg_debug['Source'] is not None and seg_debug:
for j in range(batch_size):
rows, cols = 2, len(seg_debug['Source'])
fig, axs = plt.subplots(
rows,
cols,
figsize=(3 * cols, 3 * rows),
gridspec_kw={
'hspace': 0.1,
'wspace': 0,
'top': 0.95,
'bottom': 0,
'right': 1,
'left': 0
},
)
for k1, (n1, outs) in enumerate(seg_debug.items()):
for k2, (n2, out) in enumerate(outs.items()):
if out.shape[1] == 3:
vis = torch.clamp(
denorm(out, means, stds), 0, 1)
subplotimg(axs[k1][k2], vis[j], f'{n1} {n2}')
else:
if out.ndim == 3:
args = dict(cmap='cityscapes')
else:
args = dict(cmap='gray', vmin=0, vmax=1)
subplotimg(axs[k1][k2], out[j], f'{n1} {n2}',
**args)
for ax in axs.flat:
ax.axis('off')
plt.savefig(
os.path.join(out_dir,
f'{(self.local_iter + 1):06d}_{j}_s.png'))
plt.close()
self.local_iter += 1
return {
**source_log_vars,
**g_trg_log_vars,
**d_src_log_vars,
**d_trg_log_vars
}