I am trying to implement following network Fots for Text detection using the new tensorflow 2. The authors use the resnet as the backbone of their network, so my first thought was to use the tensoflow hub resnet for loading a pretrained network. But the problem is that i can not find a way to print the summary of the module, which is loaded from tfhub?
Is there any way to see the layers of the loaded modules from tf-hub? Thanks
Update
Unfortunately is the resnet not available for tf2-hub, so i deceided to use the builtin keras implementation of resent, at least till there is a hub impl. of it.
Here is how i get the intermediate layers of resnet using tf2.keras.applications:
import numpy as np
import tensorflow as tf
from tensorflow import keras
layers_out = ["activation_9", "activation_21", "activation_39", "activation_48"]
imgs = np.random.randn(2, 640, 640, 3).astype(np.float32)
model = keras.applications.resnet50.ResNet50(input_shape=(640, 640, 3), include_top=False)
intermid_outputs= [model.get_layer(layer_name).output for layer_name in layers_out]
shared_conds = keras.Model(inputs=model.input, outputs=intermid_outputs)
Y = conv_shared(imgs)
shapes = [y.shape for y in Y]
print(shapes)
You can do something like this to examine the intermediate outputs:
resnet = hub.Module("https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/3")
outputs = resnet(np.random.rand(1,224,224,3), signature="image_feature_vector", as_dict=True)
for intermediate_output in outputs.keys():
print(intermediate_output)
Then, if you want to link an intermediate layer of the hub module to the rest of your graph, you can do:
resnet = hub.Module("https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/3")
features = resnet(images, signature="image_feature_vector", as_dict=True)["resnet_v2_50/block4"]
flatten = tf.reshape(features, (-1, features.shape[3]))
Assuming that we want to extract the features from the last block of the ResNet.