def generate_decapitated_googlenet(net):
"""Generates the googlenet layers until the inception_5b/output.
The output feature map is then used to feed into the lstm layers."""
google_layers = googlenet.googlenet_layers()
google_layers[0].p.bottom[0] = "data"
for layer in google_layers:
net.f(layer)
if layer.p.name == "inception_5b/output":
break
def generate_decapitated_googlenet(net):
"""Generates the googlenet layers until the inception_5b/output.
The output feature map is then used to feed into the lstm layers."""
google_layers = googlenet.googlenet_layers()
google_layers[0].p.bottom[0] = "image"
for layer in google_layers:
if "loss" in layer.p.name:
continue
net.f(layer)
if layer.p.name == "inception_5b/output":
break
def generate_decapitated_googlenet(net):
"""Generates the googlenet layers until the inception_5b/output.
The output feature map is then used to feed into the lstm layers."""
google_layers = googlenet.googlenet_layers()
google_layers[0].p.bottom[0] = "data"
for layer in google_layers:
# if layer.p.type in ["Convolution", "InnerProduct"]:
# for p in layer.p.param:
# p.lr_mult *= 4
net.f(layer)
if layer.p.name == "inception_5b/output":
break
def generate_decapitated_googlenet(net, net_config):
"""Generates the googlenet layers until the inception_5b/output.
The output feature map is then used to feed into the lstm layers."""
google_layers = googlenet.googlenet_layers()
google_layers[0].p.bottom[0] = "image"
for layer in google_layers:
if "loss" in layer.p.name:
continue
if layer.p.type in ["Convolution", "InnerProduct"]:
for p in layer.p.param:
p.lr_mult *= net_config["googlenet_lr_mult"]
net.f(layer)
if layer.p.name == "inception_5b/output":
break
def generate_decapitated_googlenet(net, net_config):
"""Generates the googlenet layers until the inception_5b/output.
The output feature map is then used to feed into the lstm layers."""
google_layers = googlenet.googlenet_layers()
google_layers[0].p.bottom[0] = "image"
for layer in google_layers:
if "loss" in layer.p.name:
continue
if layer.p.type in ["Convolution", "InnerProduct"]:
for p in layer.p.param:
p.lr_mult *= net_config["googlenet_lr_mult"]
net.f(layer)
if layer.p.name == "inception_5b/output":
break
def generate_decapitated_googlenet(net, net_config):
"""Generates the googlenet layers until the inception_5b/output.
The output feature map is then used to feed into the lstm layers."""
google_layers = googlenet.googlenet_layers()
google_layers[0].p.bottom[0] = "image"
for layer in google_layers:
if "loss" in layer.p.name:
continue
if layer.p.type in ["Convolution", "InnerProduct"]:
for p in layer.p.param:
p.lr_mult *= net_config["googlenet_lr_mult"]
net.f(layer)
if layer.p.name == "inception_3b/output":
net.f('''
name: "pool_final"
type: "Pooling"
bottom: "inception_3b/output"
top: "inception_final_output"
pooling_param {
kernel_size: 4
stride: 4
}''')
break