def create_model(model_name, num_classes=1000, pretrained=False, **kwargs):
if 'test_time_pool' in kwargs:
test_time_pool = kwargs.pop('test_time_pool')
else:
test_time_pool = True
if 'extra' in kwargs:
extra = kwargs.pop('extra')
else:
extra = True
if model_name == 'dpn68':
model = dpn68(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool)
elif model_name == 'dpn68b':
model = dpn68b(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool)
elif model_name == 'dpn92':
model = dpn92(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool,
extra=extra)
elif model_name == 'dpn98':
model = dpn98(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool)
elif model_name == 'dpn131':
model = dpn131(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool)
elif model_name == 'dpn107':
model = dpn107(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool)
elif model_name == 'resnet18':
model = resnet18(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet34':
model = resnet34(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet50':
model = resnet50(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet101':
model = resnet101(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet152':
model = resnet152(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet121':
model = densenet121(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet161':
model = densenet161(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet169':
model = densenet169(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet201':
model = densenet201(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'inception_v3':
model = inception_v3(num_classes=num_classes,
pretrained=pretrained,
transform_input=False,
**kwargs)
else:
assert False, "Unknown model architecture (%s)" % model_name
return model
and callable(pretrainedmodels.__dict__[name]))
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from collections import OrderedDict
import dpn
path_img = 'data/hc.jpg'
# Load Model
model = dpn.dpn92()
model.eval()
path_img = path_img
# Load and Transform one input image
load_img = utils.LoadImage()
tf_img = utils.TransformImage(model)
input_data = load_img(path_img) # 3x400x225
input_data = tf_img(input_data) # 3x299x299
input_data = input_data.unsqueeze(0) # 1x3x299x299
input = torch.autograd.Variable(input_data)
# Load Imagenet Synsets
with open('data/imagenet_synsets.txt', 'r') as f:
synsets = f.readlines()