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Added port weights to Python 3 and new checkpoints #23

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090ad4a
Added port weights to Python 3 and new checkpoints
anas-rz Jan 12, 2023
9704e94
Update resnet.py
anas-rz Jan 20, 2023
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147 changes: 147 additions & 0 deletions Port_Weights_Assert.ipynb
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Original file line number Diff line number Diff line change
@@ -0,0 +1,147 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gGWhoJ0axBQz"
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('./assert')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MuiAFtFUxIFD"
},
"outputs": [],
"source": [
"import torch\n",
"from model import E2E"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9VnaBzts2BK4"
},
"outputs": [],
"source": [
"import itertools\n",
"protocols = ['pa', 'la']\n",
"networks = ['attentive_filtering_network', 'dilated_resnet', 'senet34', 'senet50']\n",
"all_networks = list(itertools.product(protocols, networks))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5bt6169p4nbv"
},
"outputs": [],
"source": [
"\n",
"def port_weights(protocol, network):\n",
" models_dict = {'attentive_filtering_network': 5, 'dilated_resnet': 1, 'senet34':7, \n",
" 'senet50': 6}\n",
" model_params = {\n",
" 'MODEL_SELECT' : models_dict[network], # which model \n",
" 'NUM_SPOOF_CLASS' : 2, # x-class classification\n",
" 'FOCAL_GAMMA' : None, # gamma parameter for focal loss; if obj is not focal loss, set this to None \n",
" 'NUM_RESNET_BLOCK' : 5, # number of resnet blocks in ResNet \n",
" 'AFN_UPSAMPLE' : 'Bilinear', # upsampling method in AFNet: Conv or Bilinear\n",
" 'AFN_ACTIVATION' : 'sigmoid', # activation function in AFNet: sigmoid, softmaxF, softmaxT\n",
" 'NUM_HEADS' : 3, # number of heads for multi-head att in SAFNet \n",
" 'SAFN_HIDDEN' : 10, # hidden dim for SAFNet\n",
" 'SAFN_DIM' : 'T', # SAFNet attention dim: T or F\n",
" 'RNN_HIDDEN' : 128, # hidden dim for RNN\n",
" 'RNN_LAYERS' : 4, # number of hidden layers for RNN\n",
" 'RNN_BI': True, # bidirecitonal/unidirectional for RNN\n",
" 'DROPOUT_R' : 0.0, # dropout rate \n",
" }\n",
" model = E2E(**model_params)\n",
" pa_weights = torch.load(f'./ASSERT/pretrained/{protocol}/{network}', map_location='cpu', encoding='bytes')\n",
" # Convert the first level keys.\n",
" data_dict = dict(pa_weights)\n",
" for key in list(data_dict):\n",
" if type(key) is bytes:\n",
" data_dict[key.decode()] = data_dict[key]\n",
" data_dict.pop(key)\n",
" data_dict['state_dict'] = dict(data_dict['state_dict'])\n",
" for key in list(data_dict['state_dict']):\n",
" if type(key) is bytes:\n",
" data_dict['state_dict'][key.decode()] = data_dict['state_dict'][key]\n",
" data_dict['state_dict'].pop(key)\n",
" model.load_state_dict(data_dict['state_dict'])\n",
" torch.save(data_dict, f'./ASSERT/pretrained/{protocol}/{network}.py3.ckpt')\n",
" print(f\"Ported {network} - {protocol}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LFH4ZAwB7yAE",
"outputId": "3ab0a784-8d91-4a8e-ad54-ba2ec1c8ff3e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"attentive filtering network\n",
"Ported attentive_filtering_network - pa\n",
"resnet\n",
"Ported dilated_resnet - pa\n",
"squeeze-and-excitation network\n",
"Ported senet34 - pa\n",
"squeeze-and-excitation network\n",
"Ported senet50 - pa\n",
"attentive filtering network\n",
"Ported attentive_filtering_network - la\n",
"resnet\n",
"Ported dilated_resnet - la\n",
"squeeze-and-excitation network\n",
"Ported senet34 - la\n",
"squeeze-and-excitation network\n",
"Ported senet50 - la\n"
]
}
],
"source": [
"for _p, _n in all_networks:\n",
" port_weights(_p, _n)"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "forensic_examiner_audio",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.10.8 (main, Nov 24 2022, 14:13:03) [GCC 11.2.0]"
},
"vscode": {
"interpreter": {
"hash": "e2b5310373df8c4f0bc118e06d390d9464bd5fe0a9f4e308bd14694ffbb1bd37"
}
}
},
"nbformat": 4,
"nbformat_minor": 0
}
10 changes: 9 additions & 1 deletion assert/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,13 +16,21 @@ def E2E(MODEL_SELECT, NUM_SPOOF_CLASS, NUM_RESNET_BLOCK, AFN_UPSAMPLE, AFN_ACTIV
elif MODEL_SELECT == 5:
print('attentive filtering network')
model = attentive_filtering_network.SpoofSmallAFNet257_400(NUM_SPOOF_CLASS, AFN_UPSAMPLE, AFN_ACTIVATION, NUM_RESNET_BLOCK, FOCAL_LOSS)

elif MODEL_SELECT == 6:
print('squeeze-and-excitation network')
#model = senet.se_resnet18(num_classes=NUM_SPOOF_CLASS, focal_loss=FOCAL_LOSS)
#model = senet.se_resnet34(num_classes=NUM_SPOOF_CLASS, focal_loss=FOCAL_LOSS)
# model = senet.se_resnet34(num_classes=NUM_SPOOF_CLASS, focal_loss=FOCAL_LOSS)
model = senet.se_resnet50(num_classes=NUM_SPOOF_CLASS, focal_loss=FOCAL_LOSS)
#model = senet.se_resnet101(num_classes=NUM_SPOOF_CLASS, focal_loss=FOCAL_LOSS)
#model = senet.se_resnet152(num_classes=NUM_SPOOF_CLASS, focal_loss=FOCAL_LOSS)
elif MODEL_SELECT == 7:
print('squeeze-and-excitation network')
#model = senet.se_resnet18(num_classes=NUM_SPOOF_CLASS, focal_loss=FOCAL_LOSS)
model = senet.se_resnet34(num_classes=NUM_SPOOF_CLASS, focal_loss=FOCAL_LOSS)
# model = senet.se_resnet50(num_classes=NUM_SPOOF_CLASS, focal_loss=FOCAL_LOSS)
#model = senet.se_resnet101(num_classes=NUM_SPOOF_CLASS, focal_loss=FOCAL_LOSS)
#model = senet.se_resnet152(num_classes=NUM_SPOOF_CLASS, focal_loss=FOCAL_LOSS)

return model

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