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Add common model interface to pnasnet and xception, update factory

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pull/1/head
Ross Wightman 5 years ago
parent f2029dfb65
commit c0e6e5f3db
5 changed files with 57 additions and 52 deletions
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6
data/transforms.py
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@ -20,7 +20,7 @@ def get_model_meanstd(model_name):
model_name = model_name.lower()
if 'dpn' in model_name:
return IMAGENET_DPN_MEAN, IMAGENET_DPN_STD
elif 'ception' in model_name:
elif 'ception' in model_name or 'nasnet' in model_name:
return IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
else:
return IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
@ -30,7 +30,7 @@ def get_model_mean(model_name):
model_name = model_name.lower()
if 'dpn' in model_name:
return IMAGENET_DPN_STD
elif 'ception' in model_name:
elif 'ception' in model_name or 'nasnet' in model_name:
return IMAGENET_INCEPTION_MEAN
else:
return IMAGENET_DEFAULT_MEAN
@ -40,7 +40,7 @@ def get_model_std(model_name):
model_name = model_name.lower()
if 'dpn' in model_name:
return IMAGENET_DEFAULT_STD
elif 'ception' in model_name:
elif 'ception' in model_name or 'nasnet' in model_name:
return IMAGENET_INCEPTION_STD
else:
return IMAGENET_DEFAULT_STD

5
models/model_factory.py
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@ -11,6 +11,7 @@ from .senet import seresnet18, seresnet34, seresnet50, seresnet101, seresnet152,
seresnext26_32x4d, seresnext50_32x4d, seresnext101_32x4d
from .resnext import resnext50, resnext101, resnext152
from .xception import xception
from .pnasnet import pnasnet5large
model_config_dict = {
'resnet18': {
@ -47,6 +48,8 @@ model_config_dict = {
'model_name': 'inception_resnet_v2', 'num_classes': 1000, 'input_size': 299, 'normalizer': 'le'},
'xception': {
'model_name': 'xception', 'num_classes': 1000, 'input_size': 299, 'normalizer': 'le'},
'pnasnet5large': {
'model_name': 'pnasnet5large', 'num_classes': 1000, 'input_size': 331, 'normalizer': 'le'}
}
@ -125,6 +128,8 @@ def create_model(
model = resnext152(num_classes=num_classes, pretrained=pretrained, **kwargs)
elif model_name == 'xception':
model = xception(num_classes=num_classes, pretrained=pretrained)
elif model_name == 'pnasnet5large':
model = pnasnet5large(num_classes=num_classes, pretrained=pretrained)
else:
assert False and "Invalid model"

53
models/pnasnet.py
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@ -5,7 +5,6 @@ import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
pretrained_settings = {
'pnasnet5large': {
'imagenet': {
@ -292,6 +291,8 @@ class PNASNet5Large(nn.Module):
def __init__(self, num_classes=1001):
super(PNASNet5Large, self).__init__()
self.num_classes = num_classes
self.num_features = 4320
self.conv_0 = nn.Sequential(OrderedDict([
('conv', nn.Conv2d(3, 96, kernel_size=3, stride=2, bias=False)),
('bn', nn.BatchNorm2d(96, eps=0.001))
@ -335,9 +336,20 @@ class PNASNet5Large(nn.Module):
self.relu = nn.ReLU()
self.avg_pool = nn.AvgPool2d(11, stride=1, padding=0)
self.dropout = nn.Dropout(0.5)
self.last_linear = nn.Linear(4320, num_classes)
self.last_linear = nn.Linear(self.num_features, num_classes)
def get_classifier(self):
return self.last_linear
def reset_classifier(self, num_classes):
self.num_classes = num_classes
del self.last_linear
if num_classes:
self.last_linear = nn.Linear(self.num_features, num_classes)
else:
self.last_linear = None
def features(self, x):
def forward_features(self, x, pool=True):
x_conv_0 = self.conv_0(x)
x_stem_0 = self.cell_stem_0(x_conv_0)
x_stem_1 = self.cell_stem_1(x_conv_0, x_stem_0)
@ -353,19 +365,16 @@ class PNASNet5Large(nn.Module):
x_cell_9 = self.cell_9(x_cell_7, x_cell_8)
x_cell_10 = self.cell_10(x_cell_8, x_cell_9)
x_cell_11 = self.cell_11(x_cell_9, x_cell_10)
return x_cell_11
def logits(self, features):
x = self.relu(features)
x = self.avg_pool(x)
x = x.view(x.size(0), -1)
x = self.dropout(x)
x = self.last_linear(x)
x = self.relu(x_cell_11)
if pool:
x = self.avg_pool(x)
x = x.view(x.size(0), -1)
return x
def forward(self, input):
x = self.features(input)
x = self.logits(x)
x = self.forward_features(input)
x = self.dropout(x)
x = self.last_linear(x)
return x
@ -375,7 +384,7 @@ def pnasnet5large(num_classes=1001, pretrained='imagenet'):
<https://arxiv.org/abs/1712.00559>`_ paper.
"""
if pretrained:
settings = pretrained_settings['pnasnet5large'][pretrained]
settings = pretrained_settings['pnasnet5large']['imagenet']
assert num_classes == settings[
'num_classes'], 'num_classes should be {}, but is {}'.format(
settings['num_classes'], num_classes)
@ -384,18 +393,12 @@ def pnasnet5large(num_classes=1001, pretrained='imagenet'):
model = PNASNet5Large(num_classes=1001)
model.load_state_dict(model_zoo.load_url(settings['url']))
if pretrained == 'imagenet':
new_last_linear = nn.Linear(model.last_linear.in_features, 1000)
new_last_linear.weight.data = model.last_linear.weight.data[1:]
new_last_linear.bias.data = model.last_linear.bias.data[1:]
model.last_linear = new_last_linear
model.input_space = settings['input_space']
model.input_size = settings['input_size']
model.input_range = settings['input_range']
#if pretrained == 'imagenet':
new_last_linear = nn.Linear(model.last_linear.in_features, 1000)
new_last_linear.weight.data = model.last_linear.weight.data[1:]
new_last_linear.bias.data = model.last_linear.bias.data[1:]
model.last_linear = new_last_linear
model.mean = settings['mean']
model.std = settings['std']
else:
model = PNASNet5Large(num_classes=num_classes)
return model

3
models/resnext.py
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@ -142,7 +142,6 @@ def resnext50(cardinality=32, base_width=4, pretrained=False, **kwargs):
Args:
cardinality (int): Cardinality of the aggregated transform
base_width (int): Base width of the grouped convolution
shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
"""
model = ResNeXt(
ResNeXtBottleneckC, [3, 4, 6, 3], cardinality=cardinality, base_width=base_width, **kwargs)
@ -155,7 +154,6 @@ def resnext101(cardinality=32, base_width=4, pretrained=False, **kwargs):
Args:
cardinality (int): Cardinality of the aggregated transform
base_width (int): Base width of the grouped convolution
shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
"""
model = ResNeXt(
ResNeXtBottleneckC, [3, 4, 23, 3], cardinality=cardinality, base_width=base_width, **kwargs)
@ -168,7 +166,6 @@ def resnext152(cardinality=32, base_width=4, pretrained=False, **kwargs):
Args:
cardinality (int): Cardinality of the aggregated transform
base_width (int): Base width of the grouped convolution
shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
"""
model = ResNeXt(
ResNeXtBottleneckC, [3, 8, 36, 3], cardinality=cardinality, base_width=base_width, **kwargs)

42
models/xception.py
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@ -127,6 +127,7 @@ class Xception(nn.Module):
"""
super(Xception, self).__init__()
self.num_classes = num_classes
self.num_features = 2048
self.conv1 = nn.Conv2d(3, 32, 3, 2, 0, bias=False)
self.bn1 = nn.BatchNorm2d(32)
@ -156,10 +157,10 @@ class Xception(nn.Module):
self.bn3 = nn.BatchNorm2d(1536)
# do relu here
self.conv4 = SeparableConv2d(1536, 2048, 3, 1, 1)
self.bn4 = nn.BatchNorm2d(2048)
self.conv4 = SeparableConv2d(1536, self.num_features, 3, 1, 1)
self.bn4 = nn.BatchNorm2d(self.num_features)
self.fc = nn.Linear(2048, num_classes)
self.fc = nn.Linear(self.num_features, num_classes)
# #------- init weights --------
for m in self.modules():
@ -169,7 +170,18 @@ class Xception(nn.Module):
m.weight.data.fill_(1)
m.bias.data.zero_()
def forward_features(self, input):
def get_classifier(self):
return self.fc
def reset_classifier(self, num_classes):
self.num_classes = num_classes
del self.fc
if num_classes:
self.fc = nn.Linear(self.num_features, num_classes)
else:
self.fc = None
def forward_features(self, input, pool=True):
x = self.conv1(input)
x = self.bn1(x)
x = self.relu(x)
@ -197,19 +209,16 @@ class Xception(nn.Module):
x = self.conv4(x)
x = self.bn4(x)
return x
def logits(self, features):
x = self.relu(features)
x = self.relu(x)
x = F.adaptive_avg_pool2d(x, (1, 1))
x = x.view(x.size(0), -1)
x = self.last_linear(x)
if pool:
x = F.adaptive_avg_pool2d(x, (1, 1))
x = x.view(x.size(0), -1)
return x
def forward(self, input):
x = self.forward_features(input)
x = self.logits(x)
x = self.fc(x)
return x
@ -223,13 +232,4 @@ def xception(num_classes=1000, pretrained=False):
model = Xception(num_classes=num_classes)
model.load_state_dict(model_zoo.load_url(config['url']))
model.input_space = config['input_space']
model.input_size = config['input_size']
model.input_range = config['input_range']
model.mean = config['mean']
model.std = config['std']
# TODO: ugly
model.last_linear = model.fc
del model.fc
return model

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