resnet152-image-vision-classification-futurama.ipynb

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     "text": [
      "Using device: cuda\n"
     ]
    }
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   "source": [
    "# Import library\n",
    "\n",
    "from resnet152_lib import *\n"
   ]
  },
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   "source": [
    "# Values for z-score norm\n",
    "\n",
    "data_mean = (0.4180, 0.3998, 0.3396)\n",
    "data_std = (0.2728, 0.2571, 0.2389)\n",
    "\n",
    "# Validation size\n",
    "\n",
    "validation_size = 0.0\n",
    "seed_for_split = 42"
   ]
  },
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   "source": [
    "# Hiper-params\n",
    "\n",
    "lr_value = 0.0001\n",
    "lr_decay = 0.7\n",
    "epochs = 30\n",
    "dropout_value = 0.1\n",
    "hidden_layer = 2048\n",
    "batch_size = 16\n",
    "network_type = \"resnet152\""
   ]
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   "source": [
    "# Paths\n",
    "\n",
    "train_data_path = \"./input/train_data.csv\"\n",
    "train_img_path = \"./input/train_img\"\n",
    "sample_sub_path = \"./input/sample_submission.csv\"\n",
    "test_img_path = \"./input/test_img/\"\n",
    "sub_dst_path = \"submission-resnet152.csv\""
   ]
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   "source": [
    "# Getting the data\n",
    "\n",
    "futuramaDataset = FuturamaDataset(train_data_path, train_img_path)"
   ]
  },
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   "source": [
    "# Split into train and val\n",
    "\n",
    "train_len = int(futuramaDataset.__len__())\n",
    "test_len = futuramaDataset.__len__() - train_len\n",
    "train_set, val_set = torch.utils.data.random_split(\n",
    "    futuramaDataset,\n",
    "    [train_len, test_len],\n",
    "    generator=torch.Generator().manual_seed(seed_for_split),\n",
    ")\n",
    "\n",
    "random_transforms = [\n",
    "    transforms.RandomAffine(degrees=(0, 360), scale=(0.25, 0.3)),\n",
    "    transforms.ColorJitter(hue=(-0.5, 0.5)),\n",
    "    transforms.RandomErasing(p=1.0, scale=(0.2, 0.5), value=\"random\"),\n",
    "]\n",
    "\n",
    "\n",
    "train_set.dataset.transform = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize((224, 224)),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.AutoAugment(),\n",
    "        transforms.RandomChoice(random_transforms),\n",
    "        transforms.Normalize(data_mean, data_std),\n",
    "    ]\n",
    ")\n",
    "\n",
    "val_set.dataset.transform = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize((224, 224)),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(data_mean, data_std),\n",
    "    ]\n",
    ")\n"
   ]
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     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  cpuset_checked))\n"
     ]
    }
   ],
   "source": [
    "# Create the dataloader for train and test\n",
    "\n",
    "train_loader = DataLoader(\n",
    "    train_set, batch_size=batch_size, shuffle=True, num_workers=8, drop_last=True\n",
    ")\n",
    "val_loader = DataLoader(\n",
    "    val_set, batch_size=batch_size, shuffle=False, num_workers=8, drop_last=False\n",
    ")"
   ]
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   "outputs": [
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     "text": [
      "using resnet152\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet152-f82ba261.pth\" to /root/.cache/torch/hub/checkpoints/resnet152-f82ba261.pth\n"
     ]
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       "  0%|          | 0.00/230M [00:00<?, ?B/s]"
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   "source": [
    "# Download ResNet152 pretrained with Imaginet_V2\n",
    "\n",
    "model = FuturamaResnet(network_type, hidden_layer, dropout_value).to(device)"
   ]
  },
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     "text": [
      "Adjusting learning rate of group 0 to 1.0000e-04.\n",
      "Epoch Train[1/30], Step [509/509], Loss: 0.4431\n",
      "Adjusting learning rate of group 0 to 7.0000e-05.\n",
      "Epoch Train[2/30], Step [509/509], Loss: 0.0827\n",
      "Adjusting learning rate of group 0 to 4.9000e-05.\n",
      "Epoch Train[3/30], Step [509/509], Loss: 0.0269\n",
      "Adjusting learning rate of group 0 to 3.4300e-05.\n",
      "Epoch Train[4/30], Step [509/509], Loss: 0.0127\n",
      "Adjusting learning rate of group 0 to 2.4010e-05.\n",
      "Epoch Train[5/30], Step [509/509], Loss: 0.0074\n",
      "Adjusting learning rate of group 0 to 1.6807e-05.\n",
      "Epoch Train[6/30], Step [509/509], Loss: 0.0036\n",
      "Adjusting learning rate of group 0 to 1.1765e-05.\n",
      "Epoch Train[7/30], Step [509/509], Loss: 0.0028\n",
      "Adjusting learning rate of group 0 to 8.2354e-06.\n",
      "Epoch Train[8/30], Step [509/509], Loss: 0.0031\n",
      "Adjusting learning rate of group 0 to 5.7648e-06.\n",
      "Epoch Train[9/30], Step [509/509], Loss: 0.0024\n",
      "Adjusting learning rate of group 0 to 4.0354e-06.\n",
      "Epoch Train[10/30], Step [509/509], Loss: 0.0015\n",
      "Adjusting learning rate of group 0 to 2.8248e-06.\n",
      "Epoch Train[11/30], Step [509/509], Loss: 0.0017\n",
      "Adjusting learning rate of group 0 to 1.9773e-06.\n",
      "Epoch Train[12/30], Step [509/509], Loss: 0.0011\n",
      "Adjusting learning rate of group 0 to 1.3841e-06.\n",
      "Epoch Train[13/30], Step [509/509], Loss: 0.0010\n",
      "Adjusting learning rate of group 0 to 9.6889e-07.\n",
      "Epoch Train[14/30], Step [509/509], Loss: 0.0011\n",
      "Adjusting learning rate of group 0 to 6.7822e-07.\n",
      "Epoch Train[15/30], Step [509/509], Loss: 0.0011\n",
      "Adjusting learning rate of group 0 to 4.7476e-07.\n",
      "Epoch Train[16/30], Step [509/509], Loss: 0.0013\n",
      "Adjusting learning rate of group 0 to 3.3233e-07.\n",
      "Epoch Train[17/30], Step [509/509], Loss: 0.0007\n",
      "Adjusting learning rate of group 0 to 2.3263e-07.\n",
      "Epoch Train[18/30], Step [509/509], Loss: 0.0009\n",
      "Adjusting learning rate of group 0 to 1.6284e-07.\n",
      "Epoch Train[19/30], Step [509/509], Loss: 0.0014\n",
      "Adjusting learning rate of group 0 to 1.1399e-07.\n",
      "Epoch Train[20/30], Step [509/509], Loss: 0.0007\n",
      "Adjusting learning rate of group 0 to 7.9792e-08.\n",
      "Epoch Train[21/30], Step [509/509], Loss: 0.0006\n",
      "Adjusting learning rate of group 0 to 5.5855e-08.\n",
      "Epoch Train[22/30], Step [509/509], Loss: 0.0012\n",
      "Adjusting learning rate of group 0 to 3.9098e-08.\n",
      "Epoch Train[23/30], Step [509/509], Loss: 0.0007\n",
      "Adjusting learning rate of group 0 to 2.7369e-08.\n",
      "Epoch Train[24/30], Step [509/509], Loss: 0.0011\n",
      "Adjusting learning rate of group 0 to 1.9158e-08.\n",
      "Epoch Train[25/30], Step [509/509], Loss: 0.0011\n",
      "Adjusting learning rate of group 0 to 1.3411e-08.\n",
      "Epoch Train[26/30], Step [509/509], Loss: 0.0007\n",
      "Adjusting learning rate of group 0 to 9.3875e-09.\n",
      "Epoch Train[27/30], Step [509/509], Loss: 0.0007\n",
      "Adjusting learning rate of group 0 to 6.5712e-09.\n",
      "Epoch Train[28/30], Step [509/509], Loss: 0.0006\n",
      "Adjusting learning rate of group 0 to 4.5999e-09.\n",
      "Epoch Train[29/30], Step [509/509], Loss: 0.0006\n",
      "Adjusting learning rate of group 0 to 3.2199e-09.\n",
      "Epoch Train[30/30], Step [509/509], Loss: 0.0009\n",
      "Adjusting learning rate of group 0 to 2.2539e-09.\n"
     ]
    }
   ],
   "source": [
    "# Training\n",
    "\n",
    "training(model, device, None, lr_value, lr_decay, epochs, train_loader, val_loader)"
   ]
  },
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   "source": [
    "# Create a submission using the trained model\n",
    "\n",
    "create_submission(\n",
    "    model, sample_sub_path, test_img_path, sub_dst_path, data_mean, data_std)"
   ]
  }
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