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osmr avatar osmr commented on May 13, 2024

Hi @aif2017, in that case you can simply patch the call method:

from tf2cv.model_provider import get_model as tf2cv_get_model
import tensorflow as tf

net = tf2cv_get_model("resnet18", pretrained=True, data_format="channels_last")

def resnet_backbone_call(self, x, training=None):
    y = []
    for layer in self.features.layers:
        x = layer(x, training=training)
        y.append(x)
    x = flatten(x, self.data_format)
    x = self.output1(x)
    return x, y
import types
net.call = types.MethodType(resnet_backbone_call, net)

x = tf.random.normal((1, 224, 224, 3))
y_net = net(x)
print(y_net[1][-3].shape)
print(y_net[1][-2].shape)

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aif2017 avatar aif2017 commented on May 13, 2024

thank you solution but it was impossible for me to create a model with this outputs.
Model(input,[out1,out2,out3]) . thanks anyway

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osmr avatar osmr commented on May 13, 2024

ok

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