model_summary.ipynb

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     "text": [
      "/Users/anasingh/miniconda3/envs/SCL/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from torchstat import stat\n",
    "from torchsummary import summary\n",
    "from models import Dense, ProbingModel, ProbingDense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dense_base = Dense('BASE',7,'cpu')\n",
    "dense_large = Dense('LARGE',7,'cpu')\n",
    "probing_base = ProbingModel('BASE',7)\n",
    "probing_large = ProbingModel('LARGE',7)\n",
    "probing_dense_base = ProbingDense('BASE',7,'cpu')\n",
    "probing_dense_large = ProbingDense('LARGE',7,'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequence Length: 512\n",
      "Number of Labels: 7\n",
      "\n",
      "\n",
      "Model: dense_base\n",
      "Input shape: (12, 256, 768)\n",
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv1d-1            [-1, 1, 196608]              12\n",
      "            Conv1d-2             [-1, 256, 256]         196,864\n",
      "            Conv1d-3             [-1, 256, 256]          65,792\n",
      "            Linear-4                    [-1, 7]           1,799\n",
      "================================================================\n",
      "Total params: 264,467\n",
      "Trainable params: 264,467\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 9.00\n",
      "Forward/backward pass size (MB): 2.50\n",
      "Params size (MB): 1.01\n",
      "Estimated Total Size (MB): 12.51\n",
      "----------------------------------------------------------------\n",
      "\n",
      "\n",
      "Model: dense_large\n",
      "Input shape: (24, 256, 1024)\n",
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv1d-1            [-1, 1, 262144]              24\n",
      "            Conv1d-2             [-1, 256, 256]         262,400\n",
      "            Conv1d-3             [-1, 256, 256]          65,792\n",
      "            Linear-4                    [-1, 7]           1,799\n",
      "================================================================\n",
      "Total params: 330,015\n",
      "Trainable params: 330,015\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 24.00\n",
      "Forward/backward pass size (MB): 3.00\n",
      "Params size (MB): 1.26\n",
      "Estimated Total Size (MB): 28.26\n",
      "----------------------------------------------------------------\n",
      "\n",
      "\n",
      "Model: probing_base\n",
      "Input shape: (12, 256, 768)\n",
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Linear-1                  [-1, 128]          98,432\n",
      "            Linear-2                    [-1, 7]             903\n",
      "================================================================\n",
      "Total params: 99,335\n",
      "Trainable params: 99,335\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 9.00\n",
      "Forward/backward pass size (MB): 0.00\n",
      "Params size (MB): 0.38\n",
      "Estimated Total Size (MB): 9.38\n",
      "----------------------------------------------------------------\n",
      "\n",
      "\n",
      "Model: probing_large\n",
      "Input shape: (24, 256, 1024)\n",
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Linear-1                  [-1, 128]         131,200\n",
      "            Linear-2                    [-1, 7]             903\n",
      "================================================================\n",
      "Total params: 132,103\n",
      "Trainable params: 132,103\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 24.00\n",
      "Forward/backward pass size (MB): 0.00\n",
      "Params size (MB): 0.50\n",
      "Estimated Total Size (MB): 24.50\n",
      "----------------------------------------------------------------\n",
      "\n",
      "\n",
      "Model: probing_dense_base\n",
      "Input shape: (12, 256, 768)\n",
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv1d-1             [-1, 256, 256]         196,864\n",
      "            Conv1d-2             [-1, 256, 256]          65,792\n",
      "            Linear-3                    [-1, 7]           1,799\n",
      "================================================================\n",
      "Total params: 264,455\n",
      "Trainable params: 264,455\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 9.00\n",
      "Forward/backward pass size (MB): 1.00\n",
      "Params size (MB): 1.01\n",
      "Estimated Total Size (MB): 11.01\n",
      "----------------------------------------------------------------\n",
      "\n",
      "\n",
      "Model: probing_dense_large\n",
      "Input shape: (24, 256, 1024)\n",
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv1d-1             [-1, 256, 256]         262,400\n",
      "            Conv1d-2             [-1, 256, 256]          65,792\n",
      "            Linear-3                    [-1, 7]           1,799\n",
      "================================================================\n",
      "Total params: 329,991\n",
      "Trainable params: 329,991\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 24.00\n",
      "Forward/backward pass size (MB): 1.00\n",
      "Params size (MB): 1.26\n",
      "Estimated Total Size (MB): 26.26\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "base_layers = 12\n",
    "large_layers = 24\n",
    "\n",
    "seq_length = 256\n",
    "\n",
    "base_feature_dim = 768\n",
    "large_feature_dim = 1024\n",
    "\n",
    "print('Sequence Length: 512')\n",
    "print('Number of Labels: 7')\n",
    "print('\\n\\nModel: dense_base')\n",
    "print('Input shape:',(base_layers,seq_length,base_feature_dim))\n",
    "summary(dense_base,(base_layers,seq_length,base_feature_dim))\n",
    "print('\\n\\nModel: dense_large')\n",
    "print('Input shape:',(large_layers,seq_length,large_feature_dim))\n",
    "summary(dense_large,(large_layers,seq_length,large_feature_dim))\n",
    "\n",
    "print('\\n\\nModel: probing_base')\n",
    "print('Input shape:',(base_layers,seq_length,base_feature_dim))\n",
    "summary(probing_base,(base_layers,seq_length,base_feature_dim))\n",
    "print('\\n\\nModel: probing_large')\n",
    "print('Input shape:',(large_layers,seq_length,large_feature_dim))\n",
    "summary(probing_large,(large_layers,seq_length,large_feature_dim))\n",
    "\n",
    "print('\\n\\nModel: probing_dense_base')\n",
    "print('Input shape:',(base_layers,seq_length,base_feature_dim))\n",
    "summary(probing_dense_base,(base_layers,seq_length,base_feature_dim))\n",
    "print('\\n\\nModel: probing_dense_large')\n",
    "print('Input shape:',(large_layers,seq_length,large_feature_dim))\n",
    "summary(probing_dense_large,(large_layers,seq_length,large_feature_dim))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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