Change model from resnet18 to mobilenet_v2 in Transfer learning Tool kit

Hello,
I got this problem when trying to change the model from (default) ResNet18 to MobileNet_v2. I have change all the Resnet18 directory to Mobilenet_v2, and have downloaded the model with no problem.

When I try this :

!tlt-prune -pm $USER_EXPERIMENT_DIR/experiment_dir_unpruned/weights/mobilenet_detector.tlt
-o $USER_EXPERIMENT_DIR/experiment_dir_pruned/
-eq union
-pth 0.9
-k bjdtNHBlYXIwZ3Z2YW1scDg2ZHZzN3FkMXY6MTVhNDg1ZTYtNDUyNC00YTUwLTg0NWUtOTRhYWIzMDAzN2Vi

Using TensorFlow backend.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2019-12-04 09:19:28,362 [WARNING] tensorflow: From /usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2019-12-04 09:19:29.660932: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-12-04 09:19:29.737186: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-04 09:19:29.737591: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x812a450 executing computations on platform CUDA. Devices:
2019-12-04 09:19:29.737611: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): GeForce GTX 1060, Compute Capability 6.1
2019-12-04 09:19:29.760000: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2208000000 Hz
2019-12-04 09:19:29.760844: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x8243190 executing computations on platform Host. Devices:
2019-12-04 09:19:29.760880: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2019-12-04 09:19:29.761015: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: 
name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate(GHz): 1.6705
pciBusID: 0000:01:00.0
totalMemory: 5.94GiB freeMemory: 5.57GiB
2019-12-04 09:19:29.761034: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-12-04 09:19:29.761464: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-12-04 09:19:29.761477: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 
2019-12-04 09:19:29.761484: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N 
2019-12-04 09:19:29.761568: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5403 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060, pci bus id: 0000:01:00.0, compute capability: 6.1)
2019-12-04 09:19:30,679 [INFO] modulus.pruning.pruning: Exploring graph for retainable indices
2019-12-04 09:19:31,188 [INFO] modulus.pruning.pruning: Pruning model and appending pruned nodes to new graph
2019-12-04 09:19:48,057 [INFO] iva.common.magnet_prune: Pruning ratio (pruned model / original model): 0.997170896346

the problem is when I try the run :

!ls -rlt $USER_EXPERIMENT_DIR/experiment_dir_pruned/

the output file name :

total 43824
-rw-r--r-- 1 root root 44873352 Dec  4 09:19 resnet18_nopool_bn_detectnet_v2_pruned.tlt