{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os\nimport shutil\nimport time\n\n\nimport pandas as pd\nimport numpy as np\nimport itertools\nimport matplotlib.pyplot as plt\n\nfrom PIL import Image\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.nn.init as init\nfrom torch.nn import Conv2d, LeakyReLU, InstanceNorm2d\nfrom torch.utils.data import Dataset, DataLoader\n\nfrom torchvision import transforms as T\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.2156Z","start_time":"2022-09-04T09:58:02.682786Z"},"papermill":{"duration":2.034497,"end_time":"2022-09-22T09:21:30.743725","exception":false,"start_time":"2022-09-22T09:21:28.709228","status":"completed"},"scrolled":true,"tags":[],"execution":{"iopub.status.busy":"2023-04-13T16:53:08.200102Z","iopub.execute_input":"2023-04-13T16:53:08.200689Z","iopub.status.idle":"2023-04-13T16:53:09.829477Z","shell.execute_reply.started":"2023-04-13T16:53:08.200549Z","shell.execute_reply":"2023-04-13T16:53:09.828500Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## \n

\n


Constants and Variables

","metadata":{}},{"cell_type":"code","source":"MONET_DIR = '../input/gan-getting-started/monet_jpg/'\n\nPHOTO_DIR = '../input/gan-getting-started/photo_jpg/'\n\nSAVE_PATH = '/kaggle/working/result2-4'\n\ntest = '../input/ca3-test/CA3/Part 2-4'\n\nRADNOM_STATE = 12345\n\nIMAGE_SIZE = [256, 256]\n\nBATCH_SIZE = 1","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.220829Z","start_time":"2022-09-04T09:58:04.218018Z"},"papermill":{"duration":0.020824,"end_time":"2022-09-22T09:21:30.802146","exception":false,"start_time":"2022-09-22T09:21:30.781322","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-13T16:53:09.831810Z","iopub.execute_input":"2023-04-13T16:53:09.832360Z","iopub.status.idle":"2023-04-13T16:53:09.839305Z","shell.execute_reply.started":"2023-04-13T16:53:09.832321Z","shell.execute_reply":"2023-04-13T16:53:09.838286Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n\ndevice","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.279417Z","start_time":"2022-09-04T09:58:04.223734Z"},"papermill":{"duration":0.088082,"end_time":"2022-09-22T09:21:30.902763","exception":false,"start_time":"2022-09-22T09:21:30.814681","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-13T16:53:09.840699Z","iopub.execute_input":"2023-04-13T16:53:09.841117Z","iopub.status.idle":"2023-04-13T16:53:09.905516Z","shell.execute_reply.started":"2023-04-13T16:53:09.841075Z","shell.execute_reply":"2023-04-13T16:53:09.904585Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## \n\n

\n


Auxilary functions and classes

","metadata":{}},{"cell_type":"markdown","source":"### Visual","metadata":{"heading_collapsed":true,"papermill":{"duration":0.012426,"end_time":"2022-09-22T09:21:30.953475","exception":false,"start_time":"2022-09-22T09:21:30.941049","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"- Unnormalize image","metadata":{"hidden":true,"papermill":{"duration":0.012349,"end_time":"2022-09-22T09:21:30.978436","exception":false,"start_time":"2022-09-22T09:21:30.966087","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def unnormalize(img, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]):\n \n # To normalize: X_norm = (X - mean) / std\n # To unnormalize: X = (X_norm * std) + mean\n\n for t, m, s in zip(img, mean, std):\n t.mul_(s).add_(s)\n \n return img","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.33211Z","start_time":"2022-09-04T09:58:04.282551Z"},"hidden":true,"papermill":{"duration":0.021826,"end_time":"2022-09-22T09:21:31.012962","exception":false,"start_time":"2022-09-22T09:21:30.991136","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-13T16:53:09.910312Z","iopub.execute_input":"2023-04-13T16:53:09.912236Z","iopub.status.idle":"2023-04-13T16:53:09.918585Z","shell.execute_reply.started":"2023-04-13T16:53:09.912206Z","shell.execute_reply":"2023-04-13T16:53:09.917687Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- Show grid of images from dataloader","metadata":{"hidden":true,"papermill":{"duration":0.013668,"end_time":"2022-09-22T09:21:31.03901","exception":false,"start_time":"2022-09-22T09:21:31.025342","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def show_images(data):\n\n fig, ax = plt.subplots(2, 4, figsize=(20, 12))\n \n it = iter(data)\n \n for i in range(4):\n \n p, m = next(it)\n\n ax[0][i].imshow(unnormalize(p[0]).permute(1,2,0))\n ax[1][i].imshow(unnormalize(m[0]).permute(1,2,0))\n ax[0][i].set_title('Photo')\n ax[1][i].set_title('Monet')\n\n plt.show()","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.338485Z","start_time":"2022-09-04T09:58:04.333386Z"},"hidden":true,"papermill":{"duration":0.023498,"end_time":"2022-09-22T09:21:31.075373","exception":false,"start_time":"2022-09-22T09:21:31.051875","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-13T16:54:37.659329Z","iopub.execute_input":"2023-04-13T16:54:37.659720Z","iopub.status.idle":"2023-04-13T16:54:37.669449Z","shell.execute_reply.started":"2023-04-13T16:54:37.659688Z","shell.execute_reply":"2023-04-13T16:54:37.668472Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- Visualize training losses per epoch","metadata":{"hidden":true,"papermill":{"duration":0.012291,"end_time":"2022-09-22T09:21:31.100587","exception":false,"start_time":"2022-09-22T09:21:31.088296","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def plot_train_loss(log):\n \n df = pd.read_csv(log)\n\n fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(7,6))\n\n df.plot(x='epoch', y='loss_gen_total_avg', \n kind='line', ax=ax[0], color='b', label='Total')\n\n df.plot(x='epoch', y='loss_gen_photo_avg', \n kind='line', ax=ax[0], color='r', label='Photo Generator')\n\n df.plot(x='epoch', y='loss_gen_monet_avg', \n kind='line', ax=ax[0], color='g', label='Monet Generator')\n\n\n df.plot(x='epoch', y='loss_disc_total_avg', \n kind='line', ax=ax[1], color='b', label='Total')\n\n df.plot(x='epoch', y='loss_disc_photo_avg', \n kind='line', ax=ax[1], color='r', label='Photo Discriminator')\n\n df.plot(x='epoch', y='loss_disc_monet_avg', \n kind='line', ax=ax[1], color='g', label='Monet Discriminator')\n\n\n ax[0].set_title('Generator Losses')\n ax[1].set_title('Discriminator Losses')\n\n ax[0].set_ylabel('Loss')\n ax[1].set_ylabel('Loss')\n\n plt.tight_layout()\n plt.show()","metadata":{"ExecuteTime":{"end_time":"2022-09-04T10:21:08.920149Z","start_time":"2022-09-04T10:21:08.911574Z"},"hidden":true,"papermill":{"duration":0.025202,"end_time":"2022-09-22T09:21:31.138383","exception":false,"start_time":"2022-09-22T09:21:31.113181","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-13T16:53:09.932423Z","iopub.execute_input":"2023-04-13T16:53:09.933160Z","iopub.status.idle":"2023-04-13T16:53:09.944669Z","shell.execute_reply.started":"2023-04-13T16:53:09.933115Z","shell.execute_reply":"2023-04-13T16:53:09.943598Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Loading data","metadata":{"heading_collapsed":true,"papermill":{"duration":0.013845,"end_time":"2022-09-22T09:21:31.165942","exception":false,"start_time":"2022-09-22T09:21:31.152097","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"- Custom dataset class for training","metadata":{"hidden":true,"papermill":{"duration":0.01393,"end_time":"2022-09-22T09:21:31.193515","exception":false,"start_time":"2022-09-22T09:21:31.179585","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class ImageDatasetCV(Dataset):\n \n def __init__(self, monet_dir, photo_dir, size=(256, 256), normalize=True):\n \n super().__init__()\n \n self.monet_dir = None\n \n if monet_dir:\n \n self.monet_dir = monet_dir\n self.monet_idx = dict()\n \n for i, filename in enumerate(os.listdir(self.monet_dir)):\n self.monet_idx[i] = filename\n\n \n self.photo_dir = photo_dir\n self.photo_idx = dict()\n \n for i, filename in enumerate(os.listdir(self.photo_dir)):\n self.photo_idx[i] = filename\n \n \n if normalize:\n self.transforms = T.Compose([T.Resize(size), \n T.ToTensor(), \n T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n ])\n \n else:\n self.transforms = T.Compose([T.Resize(size), \n T.ToTensor()\n ]) \n \n \n def __getitem__(self, idx):\n \n random_idx = idx\n \n if self.monet_dir: \n \n random_idx = int(np.random.uniform(0, len(self.monet_idx.keys())))\n \n monet_path = os.path.join(self.monet_dir, self.monet_idx[random_idx])\n monet_img = self.transforms(Image.open(monet_path))\n \n \n photo_path = os.path.join(self.photo_dir, self.photo_idx[random_idx])\n photo_img = self.transforms(Image.open(photo_path))\n \n if self.monet_dir:\n return photo_img, monet_img\n else:\n return photo_img\n \n \n def __len__(self):\n \n if self.monet_dir:\n return min(len(self.monet_idx.keys()), len(self.photo_idx.keys()))\n else:\n return len(self.photo_idx.keys())","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.401528Z","start_time":"2022-09-04T09:58:04.346509Z"},"hidden":true,"papermill":{"duration":0.033407,"end_time":"2022-09-22T09:21:31.23966","exception":false,"start_time":"2022-09-22T09:21:31.206253","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-13T16:53:09.946130Z","iopub.execute_input":"2023-04-13T16:53:09.947034Z","iopub.status.idle":"2023-04-13T16:53:09.960105Z","shell.execute_reply.started":"2023-04-13T16:53:09.946999Z","shell.execute_reply":"2023-04-13T16:53:09.959279Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Neural Netowork","metadata":{"heading_collapsed":true,"papermill":{"duration":0.0141,"end_time":"2022-09-22T09:21:31.268224","exception":false,"start_time":"2022-09-22T09:21:31.254124","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"- Save model state","metadata":{"hidden":true,"papermill":{"duration":0.012272,"end_time":"2022-09-22T09:21:31.293726","exception":false,"start_time":"2022-09-22T09:21:31.281454","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def save_checkpoint(state, save_path):\n torch.save(state, save_path)","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.41062Z","start_time":"2022-09-04T09:58:04.40315Z"},"hidden":true,"papermill":{"duration":0.020855,"end_time":"2022-09-22T09:21:31.326855","exception":false,"start_time":"2022-09-22T09:21:31.306","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-13T16:53:09.961604Z","iopub.execute_input":"2023-04-13T16:53:09.962431Z","iopub.status.idle":"2023-04-13T16:53:09.970291Z","shell.execute_reply.started":"2023-04-13T16:53:09.962396Z","shell.execute_reply":"2023-04-13T16:53:09.969341Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- Load model state","metadata":{"hidden":true,"papermill":{"duration":0.012207,"end_time":"2022-09-22T09:21:31.35154","exception":false,"start_time":"2022-09-22T09:21:31.339333","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def load_checkpoint(ckpt_path, map_location=None):\n ckpt = torch.load(ckpt_path, map_location=map_location)\n print(' [*] Loading checkpoint from %s succeed!' % ckpt_path)\n return ckpt","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.419223Z","start_time":"2022-09-04T09:58:04.412591Z"},"hidden":true,"papermill":{"duration":0.020911,"end_time":"2022-09-22T09:21:31.385478","exception":false,"start_time":"2022-09-22T09:21:31.364567","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-13T16:53:09.973308Z","iopub.execute_input":"2023-04-13T16:53:09.974100Z","iopub.status.idle":"2023-04-13T16:53:09.980452Z","shell.execute_reply.started":"2023-04-13T16:53:09.974064Z","shell.execute_reply":"2023-04-13T16:53:09.979357Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## \n

\n


Load Data

","metadata":{}},{"cell_type":"markdown","source":"### Create dataset","metadata":{"papermill":{"duration":0.012508,"end_time":"2022-09-22T09:21:31.435287","exception":false,"start_time":"2022-09-22T09:21:31.422779","status":"completed"},"tags":[]}},{"cell_type":"code","source":"img_dataset = ImageDatasetCV(monet_dir=MONET_DIR, photo_dir=PHOTO_DIR)","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.427721Z","start_time":"2022-09-04T09:58:04.421771Z"},"papermill":{"duration":0.355493,"end_time":"2022-09-22T09:21:31.803033","exception":false,"start_time":"2022-09-22T09:21:31.44754","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-13T16:53:09.983657Z","iopub.execute_input":"2023-04-13T16:53:09.984282Z","iopub.status.idle":"2023-04-13T16:53:10.551899Z","shell.execute_reply.started":"2023-04-13T16:53:09.984248Z","shell.execute_reply":"2023-04-13T16:53:10.550780Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Create Dataloader ","metadata":{"papermill":{"duration":0.012104,"end_time":"2022-09-22T09:21:31.82807","exception":false,"start_time":"2022-09-22T09:21:31.815966","status":"completed"},"tags":[]}},{"cell_type":"code","source":"img_dataloader = DataLoader(img_dataset, \n batch_size=BATCH_SIZE, \n pin_memory=True)\n\n# Enable pin_memory to automatically put the fetched data Tensors \n# in pinned memory, and thus enables faster data transfer \n# to CUDA-enabled GPUs","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.431722Z","start_time":"2022-09-04T09:58:04.429428Z"},"papermill":{"duration":0.020944,"end_time":"2022-09-22T09:21:31.861497","exception":false,"start_time":"2022-09-22T09:21:31.840553","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-13T16:54:47.595104Z","iopub.execute_input":"2023-04-13T16:54:47.595474Z","iopub.status.idle":"2023-04-13T16:54:47.600191Z","shell.execute_reply.started":"2023-04-13T16:54:47.595441Z","shell.execute_reply":"2023-04-13T16:54:47.599237Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Check input images","metadata":{"papermill":{"duration":0.015594,"end_time":"2022-09-22T09:21:31.889855","exception":false,"start_time":"2022-09-22T09:21:31.874261","status":"completed"},"tags":[]}},{"cell_type":"code","source":"show_images(img_dataloader)","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:06.909069Z","start_time":"2022-09-04T09:58:04.433033Z"},"papermill":{"duration":3.90516,"end_time":"2022-09-22T09:21:35.80735","exception":false,"start_time":"2022-09-22T09:21:31.90219","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-13T16:54:49.130465Z","iopub.execute_input":"2023-04-13T16:54:49.131239Z","iopub.status.idle":"2023-04-13T16:54:52.889132Z","shell.execute_reply.started":"2023-04-13T16:54:49.131196Z","shell.execute_reply":"2023-04-13T16:54:52.887839Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## \n

\n


Build CycleGAN Model

","metadata":{}},{"cell_type":"markdown","source":"### Generator","metadata":{"papermill":{"duration":0.01632,"end_time":"2022-09-22T09:21:35.873773","exception":false,"start_time":"2022-09-22T09:21:35.857453","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"#### Convolutional Layer","metadata":{"heading_collapsed":true,"papermill":{"duration":0.016278,"end_time":"2022-09-22T09:21:35.906822","exception":false,"start_time":"2022-09-22T09:21:35.890544","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def Convlayer(in_ch, out_ch, kernel_size=3, stride=2, \n use_leaky=True, use_inst_norm=True, use_pad=True):\n \n \n # Convolution\n if use_pad:\n conv = nn.Conv2d(in_ch, out_ch, kernel_size, stride, 1, bias=True)\n else:\n conv = nn.Conv2d(in_ch, out_ch, kernel_size, stride, 0, bias=True)\n \n \n # Activation Function\n if use_leaky:\n actv = nn.LeakyReLU(negative_slope=0.2, inplace=True)\n else:\n actv = nn.GELU()\n \n \n # Normalization\n if use_inst_norm:\n norm = nn.InstanceNorm2d(out_ch)\n else:\n norm = nn.BatchNorm2d(out_ch)\n \n \n return nn.Sequential(conv, \n norm, \n actv)\n ","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:06.915001Z","start_time":"2022-09-04T09:58:06.910822Z"},"hidden":true,"papermill":{"duration":0.026999,"end_time":"2022-09-22T09:21:35.950091","exception":false,"start_time":"2022-09-22T09:21:35.923092","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-11T05:37:22.551769Z","iopub.execute_input":"2023-04-11T05:37:22.552192Z","iopub.status.idle":"2023-04-11T05:37:22.561944Z","shell.execute_reply.started":"2023-04-11T05:37:22.552158Z","shell.execute_reply":"2023-04-11T05:37:22.560559Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Transpose Convolutional Layer","metadata":{"heading_collapsed":true,"papermill":{"duration":0.016246,"end_time":"2022-09-22T09:21:35.982706","exception":false,"start_time":"2022-09-22T09:21:35.96646","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def Upsample(in_ch, out_ch, use_dropout=False, dropout_ratio=0.5):\n \n # Transposed Convolution\n convtrans = nn.ConvTranspose2d(in_ch, out_ch, 3, stride=2, padding=1, output_padding=1)\n \n # Normalization\n norm = nn.InstanceNorm2d(out_ch)\n \n # Activatin Function\n actv = nn.GELU()\n \n \n if use_dropout:\n \n # Dropout layer\n drop = nn.Dropout(dropout_ratio)\n \n return nn.Sequential(convtrans, \n norm, \n drop, \n actv)\n\n else: \n return nn.Sequential(convtrans, \n norm, \n actv)\n ","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:06.921716Z","start_time":"2022-09-04T09:58:06.916634Z"},"hidden":true,"papermill":{"duration":0.026379,"end_time":"2022-09-22T09:21:36.0263","exception":false,"start_time":"2022-09-22T09:21:35.999921","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-11T05:37:22.563612Z","iopub.execute_input":"2023-04-11T05:37:22.564451Z","iopub.status.idle":"2023-04-11T05:37:22.574146Z","shell.execute_reply.started":"2023-04-11T05:37:22.564363Z","shell.execute_reply":"2023-04-11T05:37:22.572928Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Residual block","metadata":{"heading_collapsed":true,"papermill":{"duration":0.016933,"end_time":"2022-09-22T09:21:36.059461","exception":false,"start_time":"2022-09-22T09:21:36.042528","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class Resblock(nn.Module):\n \n def __init__(self, in_features, use_dropout=False, dropout_ratio=0.5):\n super().__init__()\n \n layers = list()\n \n # Padding\n layers.append(nn.ReflectionPad2d(1))\n \n # Convolution layer\n layers.append(Convlayer(in_features, in_features, 3, 1, use_leaky=False, use_pad=False))\n \n # Dropout\n if use_dropout:\n layers.append(nn.Dropout(dropout_ratio))\n \n # Padding\n layers.append(nn.ReflectionPad2d(1))\n \n # Convolution\n layers.append(nn.Conv2d(in_features, in_features, 3, 1, padding=0, bias=True))\n \n # Normalization\n layers.append(nn.InstanceNorm2d(in_features))\n \n \n self.res = nn.Sequential(*layers)\n \n \n def forward(self, x):\n \n return x + self.res(x)","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:06.931592Z","start_time":"2022-09-04T09:58:06.92351Z"},"hidden":true,"papermill":{"duration":0.028341,"end_time":"2022-09-22T09:21:36.104338","exception":false,"start_time":"2022-09-22T09:21:36.075997","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-11T05:37:22.575908Z","iopub.execute_input":"2023-04-11T05:37:22.576650Z","iopub.status.idle":"2023-04-11T05:37:22.587139Z","shell.execute_reply.started":"2023-04-11T05:37:22.576613Z","shell.execute_reply":"2023-04-11T05:37:22.586205Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Generator","metadata":{"heading_collapsed":true,"papermill":{"duration":0.016184,"end_time":"2022-09-22T09:21:36.136869","exception":false,"start_time":"2022-09-22T09:21:36.120685","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class Generator(nn.Module):\n \n def __init__(self, in_ch, out_ch, num_res_blocks=6):\n \n super().__init__()\n \n model = list()\n \n # Padding layer\n model.append(nn.ReflectionPad2d(3))\n \n \n # Convolution input_channels -> 64\n model.append(Convlayer(in_ch=in_ch, out_ch=64, \n kernel_size=7, stride=1, \n use_leaky=False, \n use_inst_norm=True, \n use_pad=False))\n \n # Convolution 64 -> 128\n model.append(Convlayer(in_ch=64, out_ch=128, \n kernel_size=3, stride=2, \n use_leaky=False, \n use_inst_norm=True, \n use_pad=True))\n \n # Convolution 128 -> 256\n model.append(Convlayer(in_ch=128, out_ch=256, \n kernel_size=3, stride=2, \n use_leaky=False, \n use_inst_norm=True, \n use_pad=True))\n \n # Residual Block\n for _ in range(num_res_blocks):\n model.append(Resblock(in_features=256))\n\n \n # Transposed convolution 256 -> 128\n model.append(Upsample(in_ch=256, out_ch=128)) \n \n # Transposed convolution 128 -> 256\n model.append(Upsample(in_ch=128, out_ch=64)) \n \n # Padding Layer\n model.append(nn.ReflectionPad2d(3))\n \n # Convolutional layer\n model.append(nn.Conv2d(in_channels=64, out_channels=out_ch, \n kernel_size=7, padding=0))\n \n # Activation function Tanh\n model.append(nn.Tanh())\n \n \n self.gen = nn.Sequential(*model)\n \n \n def forward(self, x):\n \n return self.gen(x)","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:06.939138Z","start_time":"2022-09-04T09:58:06.933163Z"},"hidden":true,"papermill":{"duration":0.030768,"end_time":"2022-09-22T09:21:36.184","exception":false,"start_time":"2022-09-22T09:21:36.153232","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-11T05:37:22.588850Z","iopub.execute_input":"2023-04-11T05:37:22.589570Z","iopub.status.idle":"2023-04-11T05:37:22.600445Z","shell.execute_reply.started":"2023-04-11T05:37:22.589534Z","shell.execute_reply":"2023-04-11T05:37:22.599687Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Discriminator","metadata":{"heading_collapsed":true,"papermill":{"duration":0.016517,"end_time":"2022-09-22T09:21:36.217472","exception":false,"start_time":"2022-09-22T09:21:36.200955","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class Discriminator(nn.Module):\n \n def __init__(self, in_ch, num_layers=4):\n \n super().__init__()\n \n model = list()\n \n # Convolution in_channels -> 64\n model.append(nn.Conv2d(in_channels=in_ch, out_channels=64, \n kernel_size=4, stride=2, padding=1))\n \n \n # Convolutions i=1: 64 -> 64 \n # i=2: 64 -> 128\n # i=3: 128 -> 256 \n # i=4: 256 -> 512 \n \n for i in range(1, num_layers):\n in_chs = 64 * 2**(i-1)\n out_chs = in_chs * 2\n \n if i == num_layers - 1:\n model.append(Convlayer(in_chs, out_chs, \n kernel_size=4, stride=1))\n \n else:\n model.append(Convlayer(in_chs, out_chs, \n kernel_size=4, stride=2))\n \n # Convolution 512 -> 1\n model.append(nn.Conv2d(in_channels=512, out_channels=1, \n kernel_size=4, stride=1, padding=1))\n \n self.disc = nn.Sequential(*model)\n \n \n def forward(self, x):\n \n return self.disc(x)\n ","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:06.946676Z","start_time":"2022-09-04T09:58:06.940743Z"},"hidden":true,"papermill":{"duration":0.028749,"end_time":"2022-09-22T09:21:36.263043","exception":false,"start_time":"2022-09-22T09:21:36.234294","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-11T05:37:22.602028Z","iopub.execute_input":"2023-04-11T05:37:22.602738Z","iopub.status.idle":"2023-04-11T05:37:22.614380Z","shell.execute_reply.started":"2023-04-11T05:37:22.602701Z","shell.execute_reply":"2023-04-11T05:37:22.613668Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### CycleGAN","metadata":{"heading_collapsed":true,"papermill":{"duration":0.016845,"end_time":"2022-09-22T09:21:36.296411","exception":false,"start_time":"2022-09-22T09:21:36.279566","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class CycleGAN(object):\n \n def __init__(self, in_ch, out_ch, epochs, device, start_lr=0.01, lmbda=10, idt_coef=0.5, decay_epoch=0):\n \n\n \n # Regularization coefficients\n self.lmbda = lmbda\n self.idt_coef = idt_coef\n \n # Set device\n self.device = device\n \n # Generator Monet -> Photo\n self.gen_mtp = Generator(in_ch, out_ch)\n \n # Generator Photo -> Monet\n self.gen_ptm = Generator(in_ch, out_ch)\n \n # discriminator for Monet-generated images\n self.disc_m = Discriminator(in_ch)\n \n # discriminator for Photo-generated images\n self.disc_p = Discriminator(in_ch)\n \n # Initialize model weights\n self.init_models()\n \n # Optimizator for generators\n self.adam_gen = torch.optim.Adam(itertools.chain(self.gen_mtp.parameters(), \n self.gen_ptm.parameters()), \n lr=start_lr, betas=(0.5, 0.999))\n \n # Optimizator for discriminator\n self.adam_disc = torch.optim.Adam(itertools.chain(self.disc_m.parameters(), \n self.disc_p.parameters()), \n lr=start_lr, betas=(0.5, 0.999))\n \n # Set number of epochs and start of learning rate decay \n self.epochs = epochs\n \n # Set decay epoch \n self.decay_epoch = decay_epoch if decay_epoch > 0 else int(self.epochs/2)\n \n # Set rule for learning step decay\n lambda_decay = lambda epoch: start_lr/(epoch-self.decay_epoch) if epoch > self.decay_epoch else start_lr\n\n \n # Define scheduler for generator and discriminator\n self.scheduler_gen = torch.optim.lr_scheduler.LambdaLR(self.adam_gen, lr_lambda=lambda_decay)\n self.scheduler_disc = torch.optim.lr_scheduler.LambdaLR(self.adam_disc, lr_lambda=lambda_decay)\n\n \n # Initialize weights\n def init_weights(self, net, gain=0.02):\n\n def init_func(m):\n\n # Name of the class\n classname = m.__class__.__name__\n\n # If class has attribute \"weight\" (to initialize) and \n # has either convolutional layer or linear \n if hasattr(m, 'weight') and \\\n (classname.find('Conv') != -1 or classname.find('Linear') != -1):\n\n # Initialize weights with values drawn from normal distribution N(mean, std)\n init.normal_(m.weight.data, mean=0.0, std=gain)\n\n # Set bias value with constant val\n if hasattr(m, 'bias') and m.bias is not None:\n init.constant_(m.bias.data, val=0.0)\n\n # Initialize BatchNorm weights\n elif classname.find('BatchNorm2d') != -1:\n\n init.normal_(m.weight.data, mean=1.0, std=gain)\n init.constant_(m.bias.data, val=0.0)\n\n # Apply weight initialization to every submodule of model\n net.apply(init_func)\n \n \n # Initialize models\n def init_models(self):\n \n # Initialize weights\n self.init_weights(self.gen_mtp)\n self.init_weights(self.gen_ptm)\n self.init_weights(self.disc_m)\n self.init_weights(self.disc_p)\n \n # Set device for models\n self.gen_mtp = self.gen_mtp.to(self.device)\n self.gen_ptm = self.gen_ptm.to(self.device)\n self.disc_m = self.disc_m.to(self.device)\n self.disc_p = self.disc_p.to(self.device) \n \n \n \n # Enable/ disable gradients for model parameters\n def param_require_grad(self, models, requires_grad=True):\n for model in models:\n for param in model.parameters():\n param.requires_grad = requires_grad\n\n \n # Cycle generation: x -> y_gen -> x_cycle\n def cycle_gen(self, x, G_x_to_y, G_y_to_x):\n\n y_gen = G_x_to_y(x)\n x_cycle = G_y_to_x(y_gen)\n\n return y_gen, x_cycle \n \n \n # Define BCE logistic loss\n def mse_loss(self, x, target):\n \n if target == 1:\n return nn.MSELoss()(x, torch.ones(x.size()).to(self.device))\n else:\n return nn.MSELoss()(x, torch.zeros(x.size()).to(self.device))\n \n \n # Define Generator Loss\n def loss_gen(self, idt, real, cycle, disc):\n \n \n # Identity Losses:\n loss_idt = nn.L1Loss()(idt, real) * self.lmbda * self.idt_coef\n \n # Cycle Losses:\n loss_cycle = nn.L1Loss()(cycle, real) * self.lmbda\n \n # Adversarial Losses: \n loss_adv = self.mse_loss(disc, target=1)\n \n # Total Generator loss:\n loss_gen = loss_cycle + loss_adv + loss_idt\n \n return loss_gen\n \n \n # Discriminator Loss\n # Ideal Discriminator will classify real image as 1 and fake as 0\n def loss_disc(self, real, gen):\n\n loss_real = self.mse_loss(real, target=1)\n loss_gen = self.mse_loss(gen, target=0)\n\n return (loss_real + loss_gen)/2\n \n \n # Train\n def train(self, img_dl):\n \n history = []\n dataset_len = img_dl.__len__()\n print_header = True\n \n for epoch in range(self.epochs):\n \n # Start measuring time for epoch\n start_time = time.time()\n\n \n # Nulify average losses for an epoch\n loss_gen_photo_avg = 0.0\n loss_gen_monet_avg = 0.0\n loss_gen_total_avg = 0.0\n \n loss_disc_photo_avg = 0.0\n loss_disc_monet_avg = 0.0\n loss_disc_total_avg = 0.0\n \n \n # Iterate through dataloader with images\n for i, (photo_real, monet_real) in enumerate(img_dl):\n\n photo_real, monet_real = photo_real.to(device), monet_real.to(device)\n\n \n # Disable gradients for discriminators during generator training \n self.param_require_grad([self.disc_m, self.disc_p], requires_grad=False) \n\n # Set gradients for generators to zero at the start of the training pass \n self.adam_gen.zero_grad()\n\n\n # =======================================\n\n # FORWARD PASS THROUGH GENERATOR\n\n # ----------- Cycle photo ---------------\n\n monet_gen, photo_cycle = self.cycle_gen(photo_real, \n self.gen_ptm, \n self.gen_mtp)\n \n # ----------- Cycle Monet ---------------\n\n photo_gen, monet_cycle = self.cycle_gen(monet_real, \n self.gen_mtp, \n self.gen_ptm)\n\n\n\n # ----------- Generate itself ---------------\n\n \n # Real Monet -> Identical Monet\n monet_idt = self.gen_ptm(monet_real) \n \n # Real photo -> Identical photo\n photo_idt = self.gen_mtp(photo_real)\n\n \n # =======================================\n\n # DISCRIMINATOR PRECTION ON GENERATED IMAGES\n\n\n # Discriminator M: Check generated Monet \n monet_disc = self.disc_m(monet_gen)\n\n # Discriminator P: Check generated photo\n photo_disc = self.disc_p(photo_gen)\n\n\n # =======================================\n\n # CALCULATE LOSSES FOR GENERATORS\n\n # Generator Losses\n loss_gen_photo = self.loss_gen(photo_idt, photo_real, photo_cycle, photo_disc)\n loss_gen_monet = self.loss_gen(monet_idt, monet_real, monet_cycle, monet_disc)\n \n \n # Total Generator loss:\n loss_gen_total = loss_gen_photo + loss_gen_monet\n \n \n # Update average Generator loss:\n loss_gen_photo_avg += loss_gen_photo.item()\n loss_gen_monet_avg += loss_gen_monet.item()\n loss_gen_total_avg += loss_gen_total.item()\n\n \n # =======================================\n\n # GENERATOR BACKWARD PASS \n\n\n # Propagate loss backward\n loss_gen_total.backward()\n\n # Make step with optimizer\n self.adam_gen.step()\n\n \n # =======================================\n\n # FORWARD PASS THROUGH DISCRIMINATORS\n\n \n # Enable gradients for discriminators during discriminator trainig\n self.param_require_grad([self.disc_m, self.disc_p], requires_grad=True) \n \n # Set zero gradients\n self.adam_disc.zero_grad()\n \n \n # discriminator M: Predictions on real and generated Monet:\n monet_disc_real = self.disc_m(monet_real)\n monet_disc_gen = self.disc_m(monet_gen.detach())\n\n # discriminator P: Predictions on real and generated photo:\n photo_disc_real = self.disc_p(photo_real)\n photo_disc_gen = self.disc_p(photo_gen.detach())\n\n \n # =======================================\n\n # CALCULATE LOSSES FOR DISCRIMINATORS\n\n # Discriminator losses\n loss_disc_photo = self.loss_disc(photo_disc_real, photo_disc_gen)\n loss_disc_monet = self.loss_disc(monet_disc_real, monet_disc_gen)\n\n\n # Total discriminator loss: \n loss_disc_total = loss_disc_photo + loss_disc_monet\n\n \n # =======================================\n\n # DISCRIMINATOR BACKWARD PASS \n\n\n # Propagate losses backward\n loss_disc_total.backward()\n \n # Make step with optimizer\n self.adam_disc.step()\n \n\n # =======================================\n\n # Update average Discriminator loss\n loss_disc_photo_avg += loss_disc_photo.item()\n loss_disc_monet_avg += loss_disc_monet.item()\n loss_disc_total_avg += loss_disc_total.item()\n \n \n # Calculate average losses per epoch \n loss_gen_photo_avg /= dataset_len\n loss_gen_monet_avg /= dataset_len\n loss_gen_total_avg /= dataset_len\n \n loss_disc_photo_avg /= dataset_len\n loss_disc_monet_avg /= dataset_len\n loss_disc_total_avg /= dataset_len\n \n \n # Estimate training time per epoch\n time_req = time.time() - start_time\n \n \n # Expand training history\n history.append([epoch, loss_gen_photo_avg, \n loss_gen_monet_avg, \n loss_gen_total_avg,\n loss_disc_photo_avg, \n loss_disc_monet_avg, \n loss_disc_total_avg ])\n \n \n # Print statistics\n if print_header:\n print('EPOCH | LOSS: Gen photo | Gen Monet | Disc photo | Disc Monet | TIME MIN')\n print_header = False\n \n print('------+-----------------+-----------+------------+------------+----------')\n print(' {:3} | {:>8.2f} | {:>8.2f} | {:>8.2f} | {:>8.2f} | {:4.0f}'.format(epoch+1, \n loss_gen_photo_avg, \n loss_gen_monet_avg, \n loss_disc_photo_avg, \n loss_disc_monet_avg, \n time_req//60))\n # Step learning rate scheduler\n self.scheduler_gen.step()\n self.scheduler_disc.step()\n\n # Save training history\n history = pd.DataFrame(history, columns=['epoch', \n 'loss_gen_photo_avg', \n 'loss_gen_monet_avg', \n 'loss_gen_total_avg',\n 'loss_disc_photo_avg', \n 'loss_disc_monet_avg', \n 'loss_disc_total_avg' ])\n \n history.to_csv('history.csv', index=False)\n \n \n def predict(self, image):\n \n with torch.no_grad():\n \n self.gen_ptm.eval()\n \n image = image.to(self.device)\n\n monet_gen = self.gen_ptm(image)\n\n \n return monet_gen","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:06.965581Z","start_time":"2022-09-04T09:58:06.948177Z"},"hidden":true,"papermill":{"duration":0.054903,"end_time":"2022-09-22T09:21:36.367823","exception":false,"start_time":"2022-09-22T09:21:36.31292","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-11T05:37:22.616053Z","iopub.execute_input":"2023-04-11T05:37:22.616779Z","iopub.status.idle":"2023-04-11T05:37:22.653806Z","shell.execute_reply.started":"2023-04-11T05:37:22.616743Z","shell.execute_reply":"2023-04-11T05:37:22.652796Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## \n

\n


Train

","metadata":{}},{"cell_type":"markdown","source":"### Initialize model","metadata":{"papermill":{"duration":0.016367,"end_time":"2022-09-22T09:21:36.433808","exception":false,"start_time":"2022-09-22T09:21:36.417441","status":"completed"},"tags":[]}},{"cell_type":"code","source":"gan = CycleGAN(3, 3, epochs=30, device=device)","metadata":{"ExecuteTime":{"end_time":"2022-09-04T10:01:21.534356Z","start_time":"2022-09-04T10:01:21.265346Z"},"papermill":{"duration":0.42362,"end_time":"2022-09-22T09:21:36.874109","exception":false,"start_time":"2022-09-22T09:21:36.450489","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-11T05:37:22.656540Z","iopub.execute_input":"2023-04-11T05:37:22.657317Z","iopub.status.idle":"2023-04-11T05:37:23.038036Z","shell.execute_reply.started":"2023-04-11T05:37:22.657282Z","shell.execute_reply":"2023-04-11T05:37:23.037087Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Train model","metadata":{"papermill":{"duration":0.016477,"end_time":"2022-09-22T09:21:36.907419","exception":false,"start_time":"2022-09-22T09:21:36.890942","status":"completed"},"tags":[]}},{"cell_type":"code","source":"gan.train(img_dataloader)","metadata":{"ExecuteTime":{"end_time":"2022-09-04T10:09:02.563358Z","start_time":"2022-09-04T10:01:22.788722Z"},"papermill":{"duration":7357.170172,"end_time":"2022-09-22T11:24:14.093958","exception":false,"start_time":"2022-09-22T09:21:36.923786","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-11T05:37:23.041442Z","iopub.execute_input":"2023-04-11T05:37:23.041720Z","iopub.status.idle":"2023-04-11T06:14:25.982438Z","shell.execute_reply.started":"2023-04-11T05:37:23.041696Z","shell.execute_reply":"2023-04-11T06:14:25.981316Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## \n

\n


Train results

","metadata":{}},{"cell_type":"markdown","source":"### Plot losses","metadata":{"papermill":{"duration":0.022408,"end_time":"2022-09-22T11:24:14.184496","exception":false,"start_time":"2022-09-22T11:24:14.162088","status":"completed"},"tags":[]}},{"cell_type":"code","source":"plot_train_loss(log='history.csv')","metadata":{"ExecuteTime":{"end_time":"2022-09-04T10:21:30.115051Z","start_time":"2022-09-04T10:21:29.865353Z"},"papermill":{"duration":0.542435,"end_time":"2022-09-22T11:24:14.803723","exception":false,"start_time":"2022-09-22T11:24:14.261288","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-11T06:14:25.984131Z","iopub.execute_input":"2023-04-11T06:14:25.984535Z","iopub.status.idle":"2023-04-11T06:14:26.445783Z","shell.execute_reply.started":"2023-04-11T06:14:25.984495Z","shell.execute_reply":"2023-04-11T06:14:26.444866Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Model predictions","metadata":{"papermill":{"duration":0.0238,"end_time":"2022-09-22T11:24:14.851963","exception":false,"start_time":"2022-09-22T11:24:14.828163","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"- Define dataset and dataloader for photos only","metadata":{"papermill":{"duration":0.022865,"end_time":"2022-09-22T11:24:14.898851","exception":false,"start_time":"2022-09-22T11:24:14.875986","status":"completed"},"tags":[]}},{"cell_type":"code","source":"photo_dataset = ImageDatasetCV(monet_dir=None, photo_dir=PHOTO_DIR)\n\nphoto_dataloader = DataLoader(photo_dataset, batch_size=BATCH_SIZE)","metadata":{"ExecuteTime":{"end_time":"2022-08-05T10:34:01.158951Z","start_time":"2022-08-05T10:34:01.143398Z"},"papermill":{"duration":0.038948,"end_time":"2022-09-22T11:24:14.961408","exception":false,"start_time":"2022-09-22T11:24:14.92246","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-11T06:14:26.447334Z","iopub.execute_input":"2023-04-11T06:14:26.447674Z","iopub.status.idle":"2023-04-11T06:14:26.456855Z","shell.execute_reply.started":"2023-04-11T06:14:26.447640Z","shell.execute_reply":"2023-04-11T06:14:26.455854Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- View predicted results","metadata":{"ExecuteTime":{"end_time":"2022-08-05T10:34:05.699762Z","start_time":"2022-08-05T10:34:05.695726Z"},"papermill":{"duration":0.024769,"end_time":"2022-09-22T11:24:15.0101","exception":false,"start_time":"2022-09-22T11:24:14.985331","status":"completed"},"tags":[]}},{"cell_type":"code","source":"with torch.no_grad():\n \n j = 0\n for photo in photo_dataloader:\n\n photo = photo.to(device)\n \n monet_gen = gan.gen_ptm(photo)\n \n fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n \n ax[0].imshow(unnormalize(photo[0].cpu()).permute(1,2,0))\n ax[1].imshow(unnormalize(monet_gen[0].cpu().detach()).permute(1,2,0))\n \n ax[0].set_title('Photo')\n ax[1].set_title('Monetesque photo')\n \n plt.show()\n j += 1\n if j > 3:\n break\n \n","metadata":{"ExecuteTime":{"end_time":"2022-08-05T13:33:31.074983Z","start_time":"2022-08-05T13:33:28.082636Z"},"papermill":{"duration":1.587583,"end_time":"2022-09-22T11:24:16.621904","exception":false,"start_time":"2022-09-22T11:24:15.034321","status":"completed"},"scrolled":true,"tags":[],"execution":{"iopub.status.busy":"2023-04-11T06:14:26.458509Z","iopub.execute_input":"2023-04-11T06:14:26.458865Z","iopub.status.idle":"2023-04-11T06:14:27.887026Z","shell.execute_reply.started":"2023-04-11T06:14:26.458831Z","shell.execute_reply":"2023-04-11T06:14:27.886051Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"photo_dataset = ImageDatasetCV(monet_dir=None, photo_dir=test)\n\nphoto_dataloader = DataLoader(photo_dataset, batch_size=BATCH_SIZE)","metadata":{"execution":{"iopub.status.busy":"2023-04-11T06:14:27.888561Z","iopub.execute_input":"2023-04-11T06:14:27.889253Z","iopub.status.idle":"2023-04-11T06:14:27.908572Z","shell.execute_reply.started":"2023-04-11T06:14:27.889202Z","shell.execute_reply":"2023-04-11T06:14:27.907714Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with torch.no_grad():\n \n j = 0\n for photo in photo_dataloader:\n\n photo = photo.to(device)\n \n monet_gen = gan.gen_ptm(photo)\n \n fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n \n ax[0].imshow(unnormalize(photo[0].cpu()).permute(1,2,0))\n ax[1].imshow(unnormalize(monet_gen[0].cpu().detach()).permute(1,2,0))\n \n ax[0].set_title('Photo')\n ax[1].set_title('Monetesque photo')\n \n plt.show()\n j += 1\n if j > 3:\n break","metadata":{"execution":{"iopub.status.busy":"2023-04-11T06:14:27.911552Z","iopub.execute_input":"2023-04-11T06:14:27.911821Z","iopub.status.idle":"2023-04-11T06:14:28.960101Z","shell.execute_reply.started":"2023-04-11T06:14:27.911796Z","shell.execute_reply":"2023-04-11T06:14:28.959180Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"SAVE_PATH = '/kaggle/working/result2-5'\n\ntest = '../input/ca3-test/CA3/Part 2-5'\n\nphoto_dataset = ImageDatasetCV(monet_dir=None, photo_dir=test)\n\nphoto_dataloader = DataLoader(photo_dataset, batch_size=BATCH_SIZE)\n\nwith torch.no_grad():\n \n j = 0\n for photo in photo_dataloader:\n\n photo = photo.to(device)\n \n monet_gen = gan.gen_ptm(photo)\n \n fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n \n ax[0].imshow(unnormalize(photo[0].cpu()).permute(1,2,0))\n ax[1].imshow(unnormalize(monet_gen[0].cpu().detach()).permute(1,2,0))\n \n ax[0].set_title('Photo')\n ax[1].set_title('Monetesque photo')\n \n plt.show()\n j += 1\n if j > 3:\n break","metadata":{"execution":{"iopub.status.busy":"2023-04-11T06:14:28.961536Z","iopub.execute_input":"2023-04-11T06:14:28.962596Z","iopub.status.idle":"2023-04-11T06:14:30.024217Z","shell.execute_reply.started":"2023-04-11T06:14:28.962557Z","shell.execute_reply":"2023-04-11T06:14:30.023283Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Save results","metadata":{"papermill":{"duration":0.040083,"end_time":"2022-09-22T11:24:16.700762","exception":false,"start_time":"2022-09-22T11:24:16.660679","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"### Save model","metadata":{"papermill":{"duration":0.041414,"end_time":"2022-09-22T11:24:16.781299","exception":false,"start_time":"2022-09-22T11:24:16.739885","status":"completed"},"tags":[]}},{"cell_type":"code","source":"save_dict = {\n 'epoch': 0,\n 'gen_mtp': gan.gen_mtp.state_dict(),\n 'gen_ptm': gan.gen_ptm.state_dict(),\n 'desc_m': gan.disc_m.state_dict(),\n 'desc_p': gan.disc_p.state_dict(),\n 'optimizer_gen': gan.adam_gen.state_dict(),\n 'optimizer_desc': gan.adam_disc.state_dict()\n }","metadata":{"papermill":{"duration":0.050364,"end_time":"2022-09-22T11:24:16.869779","exception":false,"start_time":"2022-09-22T11:24:16.819415","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-11T06:14:30.025574Z","iopub.execute_input":"2023-04-11T06:14:30.026525Z","iopub.status.idle":"2023-04-11T06:14:30.034338Z","shell.execute_reply.started":"2023-04-11T06:14:30.026487Z","shell.execute_reply":"2023-04-11T06:14:30.033477Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"save_checkpoint(save_dict, 'model.ckpt')","metadata":{"papermill":{"duration":0.428035,"end_time":"2022-09-22T11:24:17.335421","exception":false,"start_time":"2022-09-22T11:24:16.907386","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-11T06:14:30.039671Z","iopub.execute_input":"2023-04-11T06:14:30.039959Z","iopub.status.idle":"2023-04-11T06:14:30.416445Z","shell.execute_reply.started":"2023-04-11T06:14:30.039934Z","shell.execute_reply":"2023-04-11T06:14:30.415473Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## \n

\n


Save results

","metadata":{}},{"cell_type":"markdown","source":"- Get prediction for all photos","metadata":{"papermill":{"duration":0.042631,"end_time":"2022-09-22T11:24:17.492999","exception":false,"start_time":"2022-09-22T11:24:17.450368","status":"completed"},"tags":[]}},{"cell_type":"code","source":"!mkdir ../images","metadata":{"papermill":{"duration":1.126057,"end_time":"2022-09-22T11:24:18.658335","exception":false,"start_time":"2022-09-22T11:24:17.532278","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-11T06:14:30.417939Z","iopub.execute_input":"2023-04-11T06:14:30.418614Z","iopub.status.idle":"2023-04-11T06:14:31.397534Z","shell.execute_reply.started":"2023-04-11T06:14:30.418577Z","shell.execute_reply":"2023-04-11T06:14:31.396202Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if not os.path.exists(SAVE_PATH):\n os.makedirs(SAVE_PATH)\n \n\nwith torch.no_grad():\n \n \n for i, photo in enumerate(photo_dataloader):\n \n monet_gen = gan.predict(photo)\n\n monet_gen = T.ToPILImage()(unnormalize(monet_gen[0].cpu().detach())).convert('RGB')\n \n monet_gen.save(os.path.join(SAVE_PATH, str(i+1) + \".jpg\"))\n \n ","metadata":{"ExecuteTime":{"end_time":"2022-08-05T13:41:34.450823Z","start_time":"2022-08-05T13:41:34.111492Z"},"papermill":{"duration":179.375339,"end_time":"2022-09-22T11:27:18.092107","exception":false,"start_time":"2022-09-22T11:24:18.716768","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-11T06:14:31.400513Z","iopub.execute_input":"2023-04-11T06:14:31.401584Z","iopub.status.idle":"2023-04-11T06:14:31.547769Z","shell.execute_reply.started":"2023-04-11T06:14:31.401542Z","shell.execute_reply":"2023-04-11T06:14:31.546776Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- Create ZIP archive","metadata":{"papermill":{"duration":0.036608,"end_time":"2022-09-22T11:27:18.166327","exception":false,"start_time":"2022-09-22T11:27:18.129719","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# shutil.make_archive(\"/kaggle/working/images\", 'zip', \"/kaggle/images\")\nimage1 = Image.open(os.path.join(SAVE_PATH, '1.jpg'))\nimage1.show()\n\n","metadata":{"papermill":{"duration":4.191949,"end_time":"2022-09-22T11:27:22.394669","exception":false,"start_time":"2022-09-22T11:27:18.20272","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-04-11T06:14:31.549472Z","iopub.execute_input":"2023-04-11T06:14:31.549869Z","iopub.status.idle":"2023-04-11T06:14:31.615995Z","shell.execute_reply.started":"2023-04-11T06:14:31.549834Z","shell.execute_reply":"2023-04-11T06:14:31.614369Z"},"trusted":true},"execution_count":null,"outputs":[]}]}