Accessing features such as resnet_v1_50 block1/unit_1/bottleneck_v1/ and vgg block2_conv2

Dear All,
I have a pipeline in Keras wherein I extract fixed features from images (e.g. forward only) like so.
For instance, I extract 1408 features for VGG and 2048 features for Resnet50. To understand what I am doing conceptually, please refer to:
https://github.com/alexander-rakhlin/ICIAR2018

class ResNetFV:
    __name__ = "ResNet"        
    # Construct a feature extractor based on pre-trained model    

    def __init__(self,input_size=224,num_class=1,batch_size=32):
        self.model = ResNet50(include_top=False, weights='imagenet')
        
        self.input = Input(shape=(input_size, input_size, 3), name='image_input')
        feature_extractor = self.model(self.input)
        flattener = Flatten()(feature_extractor)
        self.bottleneck_feature_extractor = Model(inputs=self.input, outputs=flattener)        
        
        self.batch_size = batch_size
        self.data_format = K.image_data_format()

    def predict(self, x):
        if self.data_format == "channels_first":
            x = x.transpose(0, 3, 1, 2)
        x = preprocess_resnet(x.astype(K.floatx()))
        
        self.bottleneck_features_train_raw = self.bottleneck_feature_extractor.predict(x, batch_size=self.batch_size)
        self.bottleneck_features_train_reduced =  self.bottleneck_features_train_raw.squeeze()        
        return self.bottleneck_features_train_reduced

    
class VGGFV:
    __name__ = "VGGFV"

    def __init__(self, feature_layer="block5_conv3", input_size=224,num_class=1,batch_size=32):
        base_model = VGG16(weights='imagenet', include_top=False,
                           input_shape=[input_size,input_size,3], classes=num_class)
                                        
        x2 = GlobalAveragePooling2D()(base_model.get_layer("block2_conv2").output)  # 128
        x3 = GlobalAveragePooling2D()(base_model.get_layer("block3_conv3").output)  # 256
        x4 = GlobalAveragePooling2D()(base_model.get_layer("block4_conv3").output)  # 512
        x5 = GlobalAveragePooling2D()(base_model.get_layer("block5_conv3").output)  # 512        
        x = Concatenate()([x2,x3,x4,x5])        

        model = Model(inputs=base_model.input, outputs=x)        
        optimizer = Adam(lr=0.0001)
        model.compile(loss='categorical_crossentropy',
                      optimizer=optimizer,
                      metrics=['accuracy'])
        
        self.model=model    
        self.batch_size = batch_size
        self.data_format = K.image_data_format()
    
    def predict(self, x):
        if self.data_format == "channels_first":
            x = x.transpose(0, 3, 1, 2)
        x = preprocess_vgg(x.astype(K.floatx()))
        return self.model.predict(x, batch_size=self.batch_size).squeeze()

I am now trying to do the same in TRT.

trt_graph_def = trt.create_inference_graph(
    input_graph_def=classifier_graph_def,
    outputs=['resnet_v1_50/predictions/Reshape_1'],
    #outputs=['resnet_v1_50/block1'], THIS DOES NOT WORK
    max_batch_size=batch_size,
    max_workspace_size_bytes=workspace_size_bytes,
    precision_mode=precision_mode)
#trt_graph_def=trt.calib_graph_to_infer_graph(trt_graph_def) # For only 'INT8'
print('Generated TensorRT graph def')

After loading the graph, i am trying to access resnet_v1_50/block1", however, this results in the following exception:

Fetch node resnet_v1_50/block1 doesn't exist in graph

What I do not understand is why these CNN blocks are not accessible? (e.g. block1/unit_2/bottleneck_v1/conv3)?

Same for VGG, how can I replicate the code in TRT to extract 1408 features?

The code I am using is very similar to this one:
https://tsmatz.wordpress.com/2018/07/07/tensorrt-tensorflow-python-on-azure-tutorial/

BR,

It looks like that isn’t a valid layer name in your graph. Can you double check the names and confirm it exists?