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Softmax and log-softmax no longer applied in models. #239

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Softmax and log-softmax no longer applied in models. #239

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723812c
Softmax and log-softmax no longer applied in models.
Britefury Jul 21, 2020
f491232
Apply interpolation to deep supervision logits.
Britefury Jul 21, 2020
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5 changes: 2 additions & 3 deletions eval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -116,10 +116,9 @@ def main(cfg, gpu):
arch=cfg.MODEL.arch_decoder.lower(),
fc_dim=cfg.MODEL.fc_dim,
num_class=cfg.DATASET.num_class,
weights=cfg.MODEL.weights_decoder,
use_softmax=True)
weights=cfg.MODEL.weights_decoder)

crit = nn.NLLLoss(ignore_index=-1)
crit = nn.CrossEntropyLoss(ignore_index=-1)

segmentation_module = SegmentationModule(net_encoder, net_decoder, crit)

Expand Down
5 changes: 2 additions & 3 deletions eval_multipro.py
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Original file line number Diff line number Diff line change
Expand Up @@ -106,10 +106,9 @@ def worker(cfg, gpu_id, start_idx, end_idx, result_queue):
arch=cfg.MODEL.arch_decoder.lower(),
fc_dim=cfg.MODEL.fc_dim,
num_class=cfg.DATASET.num_class,
weights=cfg.MODEL.weights_decoder,
use_softmax=True)
weights=cfg.MODEL.weights_decoder)

crit = nn.NLLLoss(ignore_index=-1)
crit = nn.CrossEntropyLoss(ignore_index=-1)

segmentation_module = SegmentationModule(net_encoder, net_decoder, crit)

Expand Down
105 changes: 38 additions & 67 deletions mit_semseg/models/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,22 +29,19 @@ def __init__(self, net_enc, net_dec, crit, deep_sup_scale=None):
def forward(self, feed_dict, *, segSize=None):
# training
if segSize is None:
if self.deep_sup_scale is not None: # use deep supervision technique
(pred, pred_deepsup) = self.decoder(self.encoder(feed_dict['img_data'], return_feature_maps=True))
else:
pred = self.decoder(self.encoder(feed_dict['img_data'], return_feature_maps=True))
pred_dict = self.decoder(self.encoder(feed_dict['img_data'], return_feature_maps=True))

loss = self.crit(pred, feed_dict['seg_label'])
loss = self.crit(pred_dict['logits'], feed_dict['seg_label'])
if self.deep_sup_scale is not None:
loss_deepsup = self.crit(pred_deepsup, feed_dict['seg_label'])
loss_deepsup = self.crit(pred_dict['deepsup_logits'], feed_dict['seg_label'])
loss = loss + loss_deepsup * self.deep_sup_scale

acc = self.pixel_acc(pred, feed_dict['seg_label'])
acc = self.pixel_acc(pred_dict['logits'], feed_dict['seg_label'])
return loss, acc
# inference
else:
pred = self.decoder(self.encoder(feed_dict['img_data'], return_feature_maps=True), segSize=segSize)
return pred
pred_dict = self.decoder(self.encoder(feed_dict['img_data'], return_feature_maps=True), segSize=segSize)
return pred_dict['logits']


class ModelBuilder:
Expand Down Expand Up @@ -112,39 +109,33 @@ def build_encoder(arch='resnet50dilated', fc_dim=512, weights=''):
@staticmethod
def build_decoder(arch='ppm_deepsup',
fc_dim=512, num_class=150,
weights='', use_softmax=False):
weights=''):
arch = arch.lower()
if arch == 'c1_deepsup':
net_decoder = C1DeepSup(
num_class=num_class,
fc_dim=fc_dim,
use_softmax=use_softmax)
fc_dim=fc_dim)
elif arch == 'c1':
net_decoder = C1(
num_class=num_class,
fc_dim=fc_dim,
use_softmax=use_softmax)
fc_dim=fc_dim)
elif arch == 'ppm':
net_decoder = PPM(
num_class=num_class,
fc_dim=fc_dim,
use_softmax=use_softmax)
fc_dim=fc_dim)
elif arch == 'ppm_deepsup':
net_decoder = PPMDeepsup(
num_class=num_class,
fc_dim=fc_dim,
use_softmax=use_softmax)
fc_dim=fc_dim)
elif arch == 'upernet_lite':
net_decoder = UPerNet(
num_class=num_class,
fc_dim=fc_dim,
use_softmax=use_softmax,
fpn_dim=256)
elif arch == 'upernet':
net_decoder = UPerNet(
num_class=num_class,
fc_dim=fc_dim,
use_softmax=use_softmax,
fpn_dim=512)
else:
raise Exception('Architecture undefined!')
Expand Down Expand Up @@ -325,10 +316,8 @@ def forward(self, x, return_feature_maps=False):

# last conv, deep supervision
class C1DeepSup(nn.Module):
def __init__(self, num_class=150, fc_dim=2048, use_softmax=False):
def __init__(self, num_class=150, fc_dim=2048):
super(C1DeepSup, self).__init__()
self.use_softmax = use_softmax

self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1)
self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1)

Expand All @@ -342,29 +331,26 @@ def forward(self, conv_out, segSize=None):
x = self.cbr(conv5)
x = self.conv_last(x)

if self.use_softmax: # is True during inference
if segSize is not None:
x = nn.functional.interpolate(
x, size=segSize, mode='bilinear', align_corners=False)
x = nn.functional.softmax(x, dim=1)
return x

# deep sup
conv4 = conv_out[-2]
_ = self.cbr_deepsup(conv4)
_ = self.conv_last_deepsup(_)
deepsup = self.cbr_deepsup(conv4)
deepsup = self.conv_last_deepsup(deepsup)

x = nn.functional.log_softmax(x, dim=1)
_ = nn.functional.log_softmax(_, dim=1)
if segSize is not None:
deepsup = nn.functional.interpolate(
deepsup, size=segSize, mode='bilinear', align_corners=False)

return (x, _)
return dict(logits=x, deepsup_logits=deepsup)


# last conv
class C1(nn.Module):
def __init__(self, num_class=150, fc_dim=2048, use_softmax=False):
def __init__(self, num_class=150, fc_dim=2048):
super(C1, self).__init__()
self.use_softmax = use_softmax

self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1)

# last conv
Expand All @@ -375,23 +361,18 @@ def forward(self, conv_out, segSize=None):
x = self.cbr(conv5)
x = self.conv_last(x)

if self.use_softmax: # is True during inference
if segSize is not None:
x = nn.functional.interpolate(
x, size=segSize, mode='bilinear', align_corners=False)
x = nn.functional.softmax(x, dim=1)
else:
x = nn.functional.log_softmax(x, dim=1)

return x
return dict(logits=x)


# pyramid pooling
class PPM(nn.Module):
def __init__(self, num_class=150, fc_dim=4096,
use_softmax=False, pool_scales=(1, 2, 3, 6)):
pool_scales=(1, 2, 3, 6)):
super(PPM, self).__init__()
self.use_softmax = use_softmax

self.ppm = []
for scale in pool_scales:
self.ppm.append(nn.Sequential(
Expand Down Expand Up @@ -425,22 +406,18 @@ def forward(self, conv_out, segSize=None):

x = self.conv_last(ppm_out)

if self.use_softmax: # is True during inference
if segSize is not None:
x = nn.functional.interpolate(
x, size=segSize, mode='bilinear', align_corners=False)
x = nn.functional.softmax(x, dim=1)
else:
x = nn.functional.log_softmax(x, dim=1)
return x

return dict(logits=x)


# pyramid pooling, deep supervision
class PPMDeepsup(nn.Module):
def __init__(self, num_class=150, fc_dim=4096,
use_softmax=False, pool_scales=(1, 2, 3, 6)):
pool_scales=(1, 2, 3, 6)):
super(PPMDeepsup, self).__init__()
self.use_softmax = use_softmax

self.ppm = []
for scale in pool_scales:
self.ppm.append(nn.Sequential(
Expand Down Expand Up @@ -477,31 +454,29 @@ def forward(self, conv_out, segSize=None):

x = self.conv_last(ppm_out)

if self.use_softmax: # is True during inference
if segSize is not None:
x = nn.functional.interpolate(
x, size=segSize, mode='bilinear', align_corners=False)
x = nn.functional.softmax(x, dim=1)
return x

# deep sup
conv4 = conv_out[-2]
_ = self.cbr_deepsup(conv4)
_ = self.dropout_deepsup(_)
_ = self.conv_last_deepsup(_)
deepsup = self.cbr_deepsup(conv4)
deepsup = self.dropout_deepsup(deepsup)
deepsup = self.conv_last_deepsup(deepsup)

x = nn.functional.log_softmax(x, dim=1)
_ = nn.functional.log_softmax(_, dim=1)
if segSize is not None:
deepsup = nn.functional.interpolate(
deepsup, size=segSize, mode='bilinear', align_corners=False)

return (x, _)
return dict(logits=x, deepsup_logits=deepsup)


# upernet
class UPerNet(nn.Module):
def __init__(self, num_class=150, fc_dim=4096,
use_softmax=False, pool_scales=(1, 2, 3, 6),
pool_scales=(1, 2, 3, 6),
fpn_inplanes=(256, 512, 1024, 2048), fpn_dim=256):
super(UPerNet, self).__init__()
self.use_softmax = use_softmax

# PPM Module
self.ppm_pooling = []
Expand Down Expand Up @@ -575,12 +550,8 @@ def forward(self, conv_out, segSize=None):
fusion_out = torch.cat(fusion_list, 1)
x = self.conv_last(fusion_out)

if self.use_softmax: # is True during inference
if segSize is not None:
x = nn.functional.interpolate(
x, size=segSize, mode='bilinear', align_corners=False)
x = nn.functional.softmax(x, dim=1)
return x

x = nn.functional.log_softmax(x, dim=1)

return x
return dict(logits=x)
5 changes: 2 additions & 3 deletions test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -103,10 +103,9 @@ def main(cfg, gpu):
arch=cfg.MODEL.arch_decoder,
fc_dim=cfg.MODEL.fc_dim,
num_class=cfg.DATASET.num_class,
weights=cfg.MODEL.weights_decoder,
use_softmax=True)
weights=cfg.MODEL.weights_decoder)

crit = nn.NLLLoss(ignore_index=-1)
crit = nn.CrossEntropyLoss(ignore_index=-1)

segmentation_module = SegmentationModule(net_encoder, net_decoder, crit)

Expand Down
2 changes: 1 addition & 1 deletion train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -151,7 +151,7 @@ def main(cfg, gpus):
num_class=cfg.DATASET.num_class,
weights=cfg.MODEL.weights_decoder)

crit = nn.NLLLoss(ignore_index=-1)
crit = nn.CrossEntropyLoss(ignore_index=-1)

if cfg.MODEL.arch_decoder.endswith('deepsup'):
segmentation_module = SegmentationModule(
Expand Down