def load_pytorch_model(model_name):
import torchvision.models as models
switcher = {
'alexnet': lambda: models.alexnet(pretrained=True).eval(),
"vgg11": lambda: models.vgg11(pretrained=True).eval(),
"vgg11_bn": lambda: models.vgg11_bn(pretrained=True).eval(),
"vgg13": lambda: models.vgg13(pretrained=True).eval(),
"vgg13_bn": lambda: models.vgg13_bn(pretrained=True).eval(),
"vgg16": lambda: models.vgg16(pretrained=True).eval(),
"vgg16_bn": lambda: models.vgg16_bn(pretrained=True).eval(),
"vgg19": lambda: models.vgg19(pretrained=True).eval(),
"vgg19_bn": lambda: models.vgg19_bn(pretrained=True).eval(),
"resnet18": lambda: models.resnet18(pretrained=True).eval(),
"resnet34": lambda: models.resnet34(pretrained=True).eval(),
"resnet50": lambda: models.resnet50(pretrained=True).eval(),
"resnet101": lambda: models.resnet101(pretrained=True).eval(),
"resnet152": lambda: models.resnet152(pretrained=True).eval(),
"squeezenet1_0": lambda: models.squeezenet1_0(pretrained=True).eval(),
"squeezenet1_1": lambda: models.squeezenet1_1(pretrained=True).eval(),
"densenet121": lambda: models.densenet121(pretrained=True).eval(),
"densenet161": lambda: models.densenet161(pretrained=True).eval(),
"densenet201": lambda: models.densenet201(pretrained=True).eval(),
"inception_v3": lambda: models.inception_v3(pretrained=True).eval(),
}
_load_model = switcher.get(model_name, None)
_model = _load_model()
return _model
def _load_pytorch_model(model_name, summary):
import torchvision.models as models
switcher = {
'alexnet': lambda: models.alexnet(pretrained=True).eval(),
"vgg11": lambda: models.vgg11(pretrained=True).eval(),
"vgg11_bn": lambda: models.vgg11_bn(pretrained=True).eval(),
"vgg13": lambda: models.vgg13(pretrained=True).eval(),
"vgg13_bn": lambda: models.vgg13_bn(pretrained=True).eval(),
"vgg16": lambda: models.vgg16(pretrained=True).eval(),
"vgg16_bn": lambda: models.vgg16_bn(pretrained=True).eval(),
"vgg19": lambda: models.vgg19(pretrained=True).eval(),
"vgg19_bn": lambda: models.vgg19_bn(pretrained=True).eval(),
"resnet18": lambda: models.resnet18(pretrained=True).eval(),
"resnet34": lambda: models.resnet34(pretrained=True).eval(),
"resnet50": lambda: models.resnet50(pretrained=True).eval(),
"resnet101": lambda: models.resnet101(pretrained=True).eval(),
"resnet152": lambda: models.resnet152(pretrained=True).eval(),
"squeezenet1_0": lambda: models.squeezenet1_0(pretrained=True).eval(),
"squeezenet1_1": lambda: models.squeezenet1_1(pretrained=True).eval(),
"densenet121": lambda: models.densenet121(pretrained=True).eval(),
"densenet161": lambda: models.densenet161(pretrained=True).eval(),
"densenet201": lambda: models.densenet201(pretrained=True).eval(),
"inception_v3": lambda: models.inception_v3(pretrained=True).eval(),
}
_load_model = switcher.get(model_name, None)
_model = _load_model()
import torch
if torch.cuda.is_available():
_model = _model.cuda()
from perceptron.models.classification.pytorch import PyTorchModel as ClsPyTorchModel
import numpy as np
mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
pmodel = ClsPyTorchModel(_model,
bounds=(0, 1),
num_classes=1000,
preprocessing=(mean, std))
return pmodel
bbox_pred = Dense(nb_rois * 4, name="bbox_pred")(x)
return cls_score, bbox_pred
TEST_FULL_IMG = mpimg.imread("test1.jpg")
imgs = np.array([TEST_FULL_IMG])
nb_anchors = len(anchor_box_scales) * len(anchor_box_ratio)
print("imgs shape", imgs.shape)
img_input = Input(shape=(None, None, 3))
block5_conv3 = vgg16(input_tensor=img_input)
rpn_class, rpn_regr, rpn_conv = rpn(block5_conv3, nb_anchors)
cls_score, bbox_pred = rcnn(block5_conv3,
rpn_regr,
rpn_conv,
21,
21,
trainable=True)
rpn_model = Model(img_input, [rpn_regr, rpn_conv], name='rpn')
fasterRcnn = Model(img_input, [cls_score, bbox_pred], name="fasterRcnn")
model = load_vgg_weights(model)
res = model.predict(imgs)
print("res shape", res.shape)