{"cells":[{"metadata":{"_uuid":"ba84c7dc-4a29-429b-b509-82faf97da0f7","_cell_guid":"77107d2d-4de3-4282-a2b8-ca4f54085d39","trusted":true},"cell_type":"markdown","source":"Problem statement: to detect wheatheads present in images of wheats","execution_count":null},{"metadata":{"_uuid":"8b38a85c-95c9-48f5-bfd3-ef4d291b2dc4","_cell_guid":"d4c27afc-de5e-4895-9909-b08a350ab26f","trusted":true},"cell_type":"markdown","source":"# Outline\n\n**0. References **\n\n**1. Pipeline **\n\n**2. Importing necessary packages**\n\n**3. Basic EDA **\n\n**4. Data Preparation **\n\n**5. Model **\n\n**6. Training**\n\n**7. Inference**","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"## References","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"Below notebooks helped in a great manner to construct the below code:\n\n1. https://www.kaggle.com/pestipeti/pytorch-starter-fasterrcnn-train by Peter\n2. https://www.kaggle.com/pestipeti/pytorch-starter-fasterrcnn-inference by Peter\n3. https://www.kaggle.com/aryaprince/getting-started-with-object-detection-with-pytorch by @PA\n4. https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html PyTorch tutorial\n5. https://pytorch.org/docs/stable/torchvision/models.html ","execution_count":null},{"metadata":{"_uuid":"869f71df-592c-4efc-b2e4-76ee91d5347d","_cell_guid":"cc8fed90-26e9-485d-a7ae-851d59f284a3","trusted":true},"cell_type":"markdown","source":"## 1. Pipeline","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"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"}},"execution_count":null},{"metadata":{},"cell_type":"markdown","source":"![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"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"}},"execution_count":null},{"metadata":{"_uuid":"5cbeb61a-cc1f-4b48-8e8f-5711a3abd06f","_cell_guid":"d61ecc08-ed93-4f18-bcd9-5c4d91e29459","trusted":true},"cell_type":"markdown","source":"## 2. Importing Packages","execution_count":null},{"metadata":{"_uuid":"e7134710-7dbd-4b1f-8fcd-29cfddafeb9a","_cell_guid":"928c232e-6769-4162-89b4-67d5db43f0c3","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nimport matplotlib.pyplot as plt\nimport PIL\nfrom PIL import Image\n\nimport cv2\n\nimport torch\nimport torchvision\nfrom torchvision.models.detection import FasterRCNN\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\nfrom torchvision import transforms\n\nimport torch.utils.data\nfrom torch.utils.data import Dataset, DataLoader\nimport random","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d957279e-be2a-4287-be2f-65960076e6aa","_cell_guid":"16bd7f20-6128-4851-a1dd-c3dd1ecfd121","trusted":true},"cell_type":"markdown","source":"## 3. Basic EDA","execution_count":null},{"metadata":{"_uuid":"0e3fb9ba-32ac-4842-a795-07819fc80325","_cell_guid":"31621c88-b652-443b-b686-18ae911c7dd0","trusted":true},"cell_type":"code","source":"os.listdir(\"/kaggle/input/global-wheat-detection\")\n\ntrain_dir = \"/kaggle/input/global-wheat-detection/train\"\ntest_dir = \"/kaggle/input/global-wheat-detection/test\"\n\ndf_train=pd.read_csv(\"/kaggle/input/global-wheat-detection/train.csv\")","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"ca64a11a-1e3f-4b03-90c8-08f1f732a023","_cell_guid":"05348c66-ee95-499b-8aea-2187480137be","trusted":true},"cell_type":"code","source":"print(df_train.head())\nprint(df_train.shape)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"4426c3e4-3ee7-4dd5-b084-352eed097f5b","_cell_guid":"44a44028-a7df-43d7-86f1-bea3da4d6aaa","trusted":true},"cell_type":"markdown","source":"So we are looking at 147793 entries in the training excel -> corresponding to the total number of bounding boxes overall\nThis may/may not be equal to the number of images -- lets check this\nIt will be equal only if each image has one and only one bounding box","execution_count":null},{"metadata":{"_uuid":"ead0e7f6-0cd2-4304-ac20-58d23066b3ad","_cell_guid":"a1f88aa8-e890-45f5-bc3f-b192cea8411e","trusted":true},"cell_type":"code","source":"print(len(df_train['image_id'].unique()))\nprint((df_train.shape[0])/len(df_train['image_id'].unique()))","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"781ec0f3-1f35-484d-a722-e9516e4f7a70","_cell_guid":"b462466a-9460-4b2c-8cff-9cb584d91ded","trusted":true},"cell_type":"markdown","source":"Thus we have 3373 images. And we are looking at an average of approximately 44 bounding boxes in the image\n=> Thus approximately 44 wheat heads are present in an image\n\nLets also have a look at the distribution in terms of max & min","execution_count":null},{"metadata":{"_uuid":"8e00217d-ba81-40e3-8ddd-6be717834d19","_cell_guid":"8a880dce-605f-4cf5-9059-baaf2738364b","trusted":true},"cell_type":"code","source":"print(\"Image_id v/s # of bounding boxes\")\nprint(df_train['image_id'].value_counts())","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"04b1841f-a01e-4705-a19e-1e473004b144","_cell_guid":"bf682440-b2d8-4cd0-8907-836291b9bdfe","trusted":true},"cell_type":"markdown","source":"Checking  image sizes","execution_count":null},{"metadata":{"_uuid":"7bb4bc18-f11c-41cc-a4ee-342ab28e0ea6","_cell_guid":"7edc1074-b67e-402e-846a-4862299c4e30","trusted":true},"cell_type":"code","source":"print(\"Height\")\nprint(df_train['height'].value_counts())\nprint(\"Width\")\ndf_train['width'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"fc7cad97-5034-4e11-8cc3-7c90b4e145ba","_cell_guid":"6c558364-f7e6-4895-ab46-a4e7e777e91b","trusted":true},"cell_type":"markdown","source":"All images have height,width = 1024,1024","execution_count":null},{"metadata":{"_uuid":"d604d93f-5e74-4586-877c-77456cfdc693","_cell_guid":"9854b0de-d9b9-4f99-b2a7-60c6cfc2f9f7","trusted":true},"cell_type":"markdown","source":"Lets also check whether image_ids in df_train csv and list of images in training folder are same","execution_count":null},{"metadata":{"_uuid":"c1255d5e-f3a8-4b97-a082-d65b5a73fd6d","_cell_guid":"63678dec-27c6-4901-970b-b2b8b39dd65f","trusted":true},"cell_type":"code","source":"list_image_ids_df = list(df_train['image_id'].unique())\nlist_image_ids_dir = os.listdir(\"/kaggle/input/global-wheat-detection/train\")\n#list_image_ids_df.sort() == list_image_ids_dir.sort()\n#sorted(list_image_ids_df,key = lambda x: x,reverse=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(list_image_ids_dir)- len(list_image_ids_df)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#There is a difference of 49 images i.e. there are 49 images in training directory for which there are no bounding boxes indicating no wheatheads detected","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"33e36099-4d3a-4774-b55a-b3ff5436e2c6","_cell_guid":"23ac43b6-9d41-425f-9f7e-ba12cccd7d53","trusted":true},"cell_type":"markdown","source":"Just to be sure, lets check for any null values in dataframe","execution_count":null},{"metadata":{"_uuid":"493077f6-11cd-48bb-ac10-fc566c540068","_cell_guid":"87560a38-9c02-4de3-bc21-9c3db6801522","trusted":true},"cell_type":"code","source":"for col in df_train.columns:\n    if sum(df_train[col].isnull())==1:\n        print(col+\" has null values\")\n    else:\n        print(col+\" no null values\")","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"f38bd0c0-afd0-4106-8835-e455b5719cbc","_cell_guid":"0ceec63d-d2d8-4e7f-9efb-0eb119341edc","trusted":true},"cell_type":"markdown","source":"Now, that the different sets of checks are done lets start with data preparation","execution_count":null},{"metadata":{"_uuid":"6480bfa3-7ba5-4948-b671-eecb38ad6a0c","_cell_guid":"5b001a7f-465a-4733-a56a-a6098e24ee46","trusted":true},"cell_type":"markdown","source":"## 4. Data Preparation","execution_count":null},{"metadata":{"_uuid":"2fd93bf5-fa60-4031-ae8b-cad7c9198b6b","_cell_guid":"904c7cd7-4014-4067-9644-7fe07dcaf519","trusted":true},"cell_type":"markdown","source":"We know the structure df_train from the given data description:\n1. image_id - the unique image ID\n2. width, height - the width and height of the images\n3. bbox - a bounding box, formatted as a Python-style list of [xmin, ymin, width, height]\n\nbbox is something of high interest to us. We need to structure bbox in a way that can be used further","execution_count":null},{"metadata":{"_uuid":"d807a667-bbcb-41c5-a0ad-4e510ed02ac0","_cell_guid":"82be4a7e-cf65-4dfe-8dea-4a9987232487","trusted":true},"cell_type":"code","source":"df_train['x0'] = df_train['bbox'].map(lambda x: x[1:-1].split(\",\")[0]).astype(float)\ndf_train['y0'] = df_train['bbox'].map(lambda x: x[1:-1].split(\",\")[1]).astype(float)\ndf_train['w'] = df_train['bbox'].map(lambda x: x[1:-1].split(\",\")[2]).astype(float)\ndf_train['h'] = df_train['bbox'].map(lambda x: x[1:-1].split(\",\")[3]).astype(float)\ndf_train['x1'] = df_train['x0'] + df_train['w']\ndf_train['y1'] = df_train['y0'] + df_train['h']","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"6ace1a5d-03e5-4a0f-bed9-d17eb30eec57","_cell_guid":"1f69a26b-0ac9-483c-9ef6-8e8eb9fb809a","trusted":true},"cell_type":"code","source":"df_train.head()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"05c68918-d022-474a-bf18-08edfa1727f6","_cell_guid":"453b030d-8180-429e-8411-50c916669873","trusted":true},"cell_type":"code","source":"for col in df_train.columns:\n    if sum(df_train[col].isnull())==1:\n        print(col+\" has null values\")\n    else:\n        print(col+\" no null values\")","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"f1791307-8556-4164-9591-bd744dc3af91","_cell_guid":"251cbf89-f858-499f-adb5-e72eb1e502a0","trusted":true},"cell_type":"code","source":"df_train.dtypes","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"fb85e18e-facd-4649-864e-e631fdbfbf3e","_cell_guid":"7f67f964-b2df-4532-946e-f463678b5d68","trusted":true},"cell_type":"code","source":"cols_to_be_selected = ['image_id','x0','y0','x1','y1']\ndf1_train = df_train[cols_to_be_selected]","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"4bc17beb-4356-4e4c-aac5-bd62fd3551a8","_cell_guid":"ed90e214-c2d9-4927-bd71-83bf09e576d4","trusted":true},"cell_type":"code","source":"val_percentage = 0.2\nnum_val_images = int(len(df1_train['image_id'].unique())*val_percentage)\nnum_train_images = len(df1_train['image_id'].unique()) - num_val_images\nlist_val_imageid = list(df1_train['image_id'].unique())[(-1)*num_val_images:]\nlist_train_imageid = list(df1_train['image_id'].unique())[:num_train_images]","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"1bd671ea-b5c4-49a3-81e9-9cd11344cac5","_cell_guid":"e1f2bcf4-c746-4e05-b83f-2a0c0232d30f","trusted":true},"cell_type":"code","source":"print(\"Number of validation images: \",num_val_images)\nprint(\"Number of training images: \",num_train_images)\nprint(num_val_images + num_train_images)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"631ebb4d-affa-4785-a6a8-a5f83d9dea65","_cell_guid":"22cea642-2111-4b23-8224-71ad630d2e48","trusted":true},"cell_type":"markdown","source":"Now lets create corresponding dataframes","execution_count":null},{"metadata":{"_uuid":"201605ef-9169-42b3-8590-225217ff62e1","_cell_guid":"cc616b3d-3fd8-4b11-9798-4fec5fa3a08e","trusted":true},"cell_type":"code","source":"df2_val = df1_train.loc[df1_train['image_id'].isin(list_val_imageid),:]\ndf2_train = df1_train.loc[df1_train['image_id'].isin(list_train_imageid),:]","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"685d7f6b-5a58-4e70-8f27-6c4dbea43470","_cell_guid":"ab1456f0-76e1-4805-9886-88177189d7e6","trusted":true},"cell_type":"markdown","source":"With this we have the dataframes in place. Now lets start preparing the data which we need for FastRCNN Pytorch model\nFor this lets refer to the documentation:\nhttps://pytorch.org/docs/stable/torchvision/models.html","execution_count":null},{"metadata":{"_uuid":"65ac0def-88b6-424b-8bab-aafe058d0e91","_cell_guid":"9c5dc4c5-9ae4-47af-9f25-1bfd9d715627","trusted":true},"cell_type":"code","source":"def get_transform():\n    list_transforms = []\n    # converts the input image, which should be a PIL image, into a PyTorch Tensor\n    list_transforms.append(transforms.ToTensor())\n    \n    #keeping space for augmentations in future\n    \n    return transforms.Compose(list_transforms)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8185fdb1-17e3-49e3-b5ae-88ec4782f7e6","_cell_guid":"3c90a218-6334-47d7-a707-79c5d7f9ed7d","trusted":true},"cell_type":"markdown","source":"Taking the below excerpt from the model finetuning pytorch tutorial available","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"Lets now define the dataset","execution_count":null},{"metadata":{"_uuid":"d80ce93e-bdbb-4942-9702-2f0b55e4c838","_cell_guid":"eaba9e73-553a-48d4-8c45-c39dafef63a7","trusted":true},"cell_type":"markdown","source":"\nThe [torchvision reference scripts for training object detection, instance segmentation and person keypoint detection](https://github.com/pytorch/vision/tree/v0.3.0/references/detection) allows for easily supporting adding new custom datasets.\nThe dataset should inherit from the standard `torch.utils.data.Dataset` class, and implement `__len__` and `__getitem__`.\n\nThe only specificity that we require is that the dataset `__getitem__` should return:\n\n* image: a PIL Image of size (H, W)\n* target: a dict containing the following fields\n    * `boxes` (`FloatTensor[N, 4]`): the coordinates of the `N` bounding boxes in `[x0, y0, x1, y1]` format, ranging from `0` to `W` and `0` to `H`\n    * `labels` (`Int64Tensor[N]`): the label for each bounding box\n    * `image_id` (`Int64Tensor[1]`): an image identifier. It should be unique between all the images in the dataset, and is used during evaluation\n    * `area` (`Tensor[N]`): The area of the bounding box. This is used during evaluation with the COCO metric, to separate the metric scores between small, medium and large boxes.\n    * `iscrowd` (`UInt8Tensor[N]`): instances with `iscrowd=True` will be ignored during evaluation.\n    * (optionally) `masks` (`UInt8Tensor[N, H, W]`): The segmentation masks for each one of the objects\n    * (optionally) `keypoints` (`FloatTensor[N, K, 3]`): For each one of the `N` objects, it contains the `K` keypoints in `[x, y, visibility]` format, defining the object. `visibility=0` means that the keypoint is not visible. Note that for data augmentation, the notion of flipping a keypoint is dependent on the data representation, and you should probably adapt `references/detection/transforms.py` for your new keypoint representation\n\nIf your model returns the above methods, they will make it work for both training and evaluation, and will use the evaluation scripts from pycocotools.\n\nAdditionally, if you want to use aspect ratio grouping during training (so that each batch only contains images with similar aspect ratio), then it is recommended to also implement a `get_height_and_width` method, which returns the height and the width of the image. If this method is not provided, we query all elements of the dataset via `__getitem__` , which loads the image in memory and is slower than if a custom method is provided.","execution_count":null},{"metadata":{"_uuid":"5a22f84c-1468-42cf-a19c-bb8016f84aa6","_cell_guid":"36734249-6811-453d-98f3-9d9d4752722e","trusted":true},"cell_type":"markdown","source":"With this in mind lets build the class with three methods in place:\n1. __init__\n2. __getitem__\n3. __len__","execution_count":null},{"metadata":{"_uuid":"b949f74d-682d-4156-9824-7b0125a0d921","_cell_guid":"e5b3aaca-a745-49e6-82ff-ed76a01a16a3","trusted":true},"cell_type":"code","source":"class GlobalWheatDetectionDataset(torch.utils.data.Dataset):\n    # first lets start with __init__ and initialize any objects\n    def __init__(self,input_df,input_dir,transforms=None):\n        \n        self.df=input_df\n        \n        self.list_images = list(self.df['image_id'].unique())\n        \n        self.image_dir=input_dir\n        \n        self.transforms = transforms\n    \n    # next lets define __getitem__\n    # very important to note what it returns for EACH image:\n    # I. image - a PIL image of size (H,W) for ResNet50 FPN image should be scaled\n    # II. target - a dictionary containing the following fields\n    # A. boxes as FloatTensor of dimensions - N,4 where N = # of bounding boxs within an image \n    # and 4 columns include [x0,y0,x1,y1]\n    # B. labels as Int64Tensor of dimension - N\n    # C. area as Int64Tensor of dimension - N\n    # D. iscrowd as UInt8Tensor of dimension - N\n    # III. image_id \n    \n    def __getitem__(self,idx):\n        \n        # II. target\n        # Preparation for (A) boxes\n        # FloatTensor of dimensions - N,4 where N = # of bounding boxs within an image and 4 columns include [x0,y0,x1,y1]\n        \n        cols_to_be_selected =['x0','y0','x1','y1']\n        img_id = self.list_images[idx]\n        bboxes_array = np.array(self.df.loc[self.df['image_id']==img_id,cols_to_be_selected])\n        boxes = torch.tensor(bboxes_array, dtype=torch.float32)\n        \n        # Preparation for (B) labels\n        # Int64Tensor of dimension - N\n        num_boxes = self.df.loc[self.df['image_id']==img_id].shape[0]\n        labels = torch.ones(num_boxes, dtype=torch.int64)\n        \n        # Preparation for (C) area\n        # dimension - N, int64tensor\n        area = torch.tensor(np.array((self.df['x1']-self.df['x0'])*(self.df['y1']-self.df['y0'])), dtype=torch.int64)\n        \n        # Preparation for (D) iscrowd\n        # dimension - N, Uint8tensor\n        iscrowd = torch.zeros(num_boxes, dtype=torch.uint8)\n        \n        # Combining everything\n        target = {}\n        target['boxes'] = boxes\n        target['labels'] = labels\n        target['area'] = area\n        target['iscrowd'] = iscrowd\n        \n        # I. Input image\n        # Specifications: A.RGB format B. scaled (0,1) C. size (H,W) D. PIL format\n        \n        img = cv2.imread(self.image_dir+\"/\"+img_id+\".jpg\")\n        img_RGB = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n        img_scaled = img_RGB/255.0\n        img_final = img_scaled\n        \n        if self.transforms is not None:\n            img_final = self.transforms(img_final)\n        \n        # III. image_id\n        \n        \n        return img_final, target, img_id\n    \n    # next lets define __len__    \n    def __len__(self):\n        \n        return len(self.df['image_id'].unique())","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"a7766d83-ba80-40d1-8eed-9652ffb97b5d","_cell_guid":"359564aa-4b1e-4363-a12b-beb817fde238","trusted":true},"cell_type":"markdown","source":"Thats everthing for the dataset","execution_count":null},{"metadata":{"_uuid":"addcf199-1488-44ef-8964-627c6049f11b","_cell_guid":"2fd70092-0a40-43eb-8b59-e80a170fa4fe","trusted":true},"cell_type":"code","source":"train_dataset = GlobalWheatDetectionDataset(df2_train,train_dir,get_transform())\nval_dataset = GlobalWheatDetectionDataset(df2_val,train_dir,get_transform())","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"7bce9337-e31b-428c-bfa4-f6e0b54ed401","_cell_guid":"9300dbef-0d4b-41e6-8d12-cbdb541d407c","trusted":true},"cell_type":"code","source":"def collate_fn(batch):\n    return tuple(zip(*batch))","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"73813a25-861c-4e5c-be42-e4ed81df4eda","_cell_guid":"19a4ac30-10d6-4bd6-8f79-49ee390a2c26","trusted":true},"cell_type":"code","source":"train_dataloader = DataLoader(train_dataset, batch_size=16,shuffle=False, num_workers=4,collate_fn=collate_fn)\n#val_dataloader = DataLoader(val_dataset, batch_size=8,shuffle=False, num_workers=4,collate_fn=collate_fn)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"cc2af9d5-eb67-46f4-b0f7-5f19c168d290","_cell_guid":"940e7ef4-874e-414d-bf53-1ab51f7feabd","trusted":true},"cell_type":"code","source":"device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\nprint(device)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8d499c9c-7c93-4151-a9bc-2138ce216e16","_cell_guid":"820db4e2-a66f-4f5d-916c-54ab3daddfa2","trusted":true},"cell_type":"code","source":"#Showing a sample\nimage,targets,image_id = train_dataset[0]\n# Converting the A.)images to cuda or device \nimage = image.to(device)\n# To see why this is needed especially in a GPU environment, try running [print(img.device) for img in images]\n\n# Converting the B.)images to cuda or device","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"523a09a6-56d6-482d-98c1-b6813a98c7d4","_cell_guid":"ede0fe07-7502-4810-ac8f-2feb826278db","trusted":true},"cell_type":"code","source":"boxes = targets['boxes'].cpu().numpy().astype(np.int32)\nsample = image.permute(1,2,0).cpu().numpy().astype(np.float32)\nfig, ax = plt.subplots(1, 1, figsize=(16, 8))\n\nfor box in boxes:\n    cv2.rectangle(sample,\n                  (box[0], box[1]),\n                  (box[2], box[3]),\n                  (255, 0, 0), 3)\n    \nax.set_axis_off()\nax.imshow(sample)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## 5. Model Building","execution_count":null},{"metadata":{"_uuid":"89ea6cad-7fe2-4dae-b23e-f1cb3af9079f","_cell_guid":"5638e1e3-dd77-4991-8024-062df85d4f47","trusted":true},"cell_type":"code","source":"# load a model; pre-trained on COCO\n#model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d7899680-8190-4b9a-bcb3-3a9b1aa608a7","_cell_guid":"57ed07d3-70ce-46c2-a50b-f5a6e157c0ce","trusted":true},"cell_type":"code","source":"def get_instance_objectdetection_model(num_classes,path_weight):\n    # load an instance segmentation model pre-trained on COCO\n    create_model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False,pretrained_backbone=False)\n\n    # get the number of input features for the classifier\n    in_features = create_model.roi_heads.box_predictor.cls_score.in_features\n    # replace the pre-trained head with a new one\n    create_model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n    create_model.load_state_dict(torch.load(path_weight,map_location=torch.device('cpu')))\n\n    return create_model","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## 6. Training","execution_count":null},{"metadata":{"_uuid":"932512cf-8d1d-4b82-8755-fc5c84dc56a0","_cell_guid":"c5a2d9c1-6bb3-4f32-a0b6-ac5b37448147","trusted":true},"cell_type":"code","source":"path_weight= \"/kaggle/input/fasterrcnn/fasterrcnn_resnet50_fpn_best.pth\"","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d48284b2-dcfd-47e5-80df-9a118d210c4b","_cell_guid":"8516a820-76eb-4839-a6fa-125df93e6cba","trusted":true},"cell_type":"code","source":"num_classes = 2\n# Why 2 classes - background and wheat-heads\nmodel = get_instance_objectdetection_model(num_classes,path_weight)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"30152b2b-0eea-4013-9fdf-0c09e397efb7","_cell_guid":"f6c67780-7f13-48c9-985a-045b42303833","trusted":true},"cell_type":"code","source":"torch.cuda.empty_cache()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"e30f3079-d1cb-4eaa-b72f-a98a8153f608","_cell_guid":"19634ddc-024c-454a-84c8-4fbe994d6a99","trusted":true},"cell_type":"code","source":"# move model to the right device\nmodel.to(device)\n\n# construct an optimizer\nparams = [p for p in model.parameters() if p.requires_grad]\noptimizer = torch.optim.SGD(params, lr=0.005,\n                            momentum=0.9, weight_decay=0.0005)\n\n# and a learning rate scheduler which decreases the learning rate by\n# 10x every 3 epochs\nlr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,\n                                               step_size=3,\n                                               gamma=0.1)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"ef27c760-e16c-4409-ab98-4b3edfd0353d","_cell_guid":"65915e7f-f8f7-4c6b-bf99-d05c5b49ccad","trusted":true},"cell_type":"code","source":"mode=\"validation\"","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"2904ab51-6c6f-418e-b0e2-a4703f6d8671","_cell_guid":"38d6b21c-c498-47a8-8c18-697db94ccb87","trusted":true},"cell_type":"code","source":"if mode==\"training\":\n    model.train()\n    model.to(device)\n\n    num_epochs = 5\n\n    itr = 1\n\n    for epoch in range(num_epochs):\n        #loss_hist.reset()\n        loss_sum = 0\n        num_iterations = 0\n        for images, targets, image_ids in train_dataloader:\n\n            images = list(image.to(device,dtype=torch.float) for image in images)\n            targets = [{k: v.to(device) for k, v in t.items()} for t in targets]\n\n            loss_dict = model(images, targets)   ##Return the loss\n\n            losses = sum(loss for loss in loss_dict.values())\n            loss_value = losses.item()\n\n            #loss_hist.send(loss_value)  #Average out the loss\n            loss_sum = loss_sum + loss_value\n            num_iterations = num_iterations + 1\n\n            optimizer.zero_grad()\n            losses.backward()\n            optimizer.step()\n\n            if itr % 5 == 0:\n                print(f\"Iteration #{itr} loss: {loss_value}\")\n\n            itr += 1\n\n        # update the learning rate\n        if lr_scheduler is not None:\n            lr_scheduler.step()\n\n        if num_iterations>0:\n            loss_avg_value = loss_sum/num_iterations\n\n        print(\"Epoch\"+ \"#\"+str(epoch)+\" loss: \"+str(loss_avg_value))","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"7ec59027-6fa5-4e2e-b00c-6cdcb0fcfd46","_cell_guid":"ae78e589-6703-44c7-b857-801b774a54f1","trusted":true},"cell_type":"code","source":"os.listdir(\"/kaggle/input/gwd-customtrained-fasterrcnn-resnet-50-fpn-01\")","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"546ffd4e-5224-41b4-912c-1160e44bd177","_cell_guid":"8e4e218c-60cf-4a2c-82c1-4d0a0ca0b594","trusted":true},"cell_type":"code","source":"#torch.save(model.state_dict(), '/kaggle/working/customtrained_fasterrcnn_resnet50_fpn.pth')","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"01cfb978-c694-418a-89a5-307c6a4387d0","_cell_guid":"7fddfb7d-0c6c-4e6a-b64c-c1c49f3feb8e","trusted":true},"cell_type":"code","source":"path_trained_weight = \"/kaggle/input/gwd-customtrained-fasterrcnn-resnet-50-fpn-01/customtrained_fasterrcnn_resnet50_fpn.pth\"\nnum_classes=2\ntrained_model = get_instance_objectdetection_model(num_classes,path_trained_weight)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"1cf331d3-9007-47c4-a1c8-9c8096cd7d95","_cell_guid":"178fc547-8cf1-434c-8379-ae2c5a1dfc2f","trusted":true},"cell_type":"code","source":"# os.chdir(\"/kaggle/working/\")","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"e45f0bcf-372f-4cb5-8586-dddd4aca4bd4","_cell_guid":"b0d65eb9-33f4-4544-8f9b-a15c0a151e62","trusted":true},"cell_type":"code","source":"# from IPython.display import FileLink\n# FileLink(r'customtrained_fasterrcnn_resnet50_fpn.pth')","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"a7b71b73-3188-40de-a3f1-a562b335e2b3","_cell_guid":"ff143584-a530-4144-890e-0ee45a550c66","trusted":true},"cell_type":"markdown","source":"Validation","execution_count":null},{"metadata":{"_uuid":"b7c45041-cf7e-4237-8eef-f2e2b0c5d5f7","_cell_guid":"5d5c5b2d-631c-4518-b3df-ab024800dbfc","trusted":true},"cell_type":"code","source":"torch.cuda.empty_cache()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"9c6982ed-09ca-44a8-bbcf-383ccd5e6bd7","_cell_guid":"8792e452-50a2-4acf-8c36-8ea8df6bbfa1","trusted":true},"cell_type":"code","source":"val_dataloader = DataLoader(val_dataset, batch_size=8,shuffle=False, num_workers=2,collate_fn=collate_fn)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"32149bde-6437-4ba0-8ea9-947848ec7a2d","_cell_guid":"428bf6de-a295-4cde-93e1-17715dbfb231","trusted":true},"cell_type":"code","source":"trained_model.eval()\ntrained_model.to(device)\n\n\nimages, targets, image_ids = next(iter(val_dataloader))\n\nimages = list(image.to(device,dtype=torch.float) for image in images)\ntargets = [{k: v.to(device) for k, v in t.items()} for t in targets]\n\nboxes = targets[2]['boxes'].cpu().numpy().astype(np.int32)\nsample = images[2].permute(1,2,0).cpu().numpy()\n\nfig,ax = plt.subplots(1,1,figsize=(16,8))\n\nfor box in boxes:\n    cv2.rectangle(sample, (box[0],box[1]),(box[2],box[3]),(255,0,0),3)\n    ax.set_axis_off()\n    ax.imshow(sample)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"591ad0a6-76b8-44a5-a0fa-564dd03e1179","_cell_guid":"df9ba7ee-118c-4764-be0f-56a6c4781c4b","trusted":true},"cell_type":"markdown","source":"## 7. Inference","execution_count":null},{"metadata":{"_uuid":"c85cfe3e-bfcb-4a0d-b3c7-ac3730e56087","_cell_guid":"bf81c922-89e6-4915-b73e-8ca5ee2b6581","trusted":true},"cell_type":"code","source":"class GlobalWheatDetectionTestDataset(torch.utils.data.Dataset):\n    # first lets start with __init__ and initialize any objects\n    def __init__(self,input_df,input_dir,transforms=None):\n        \n        self.df=input_df\n        \n        self.list_images = list(self.df['image_id'].unique())\n        \n        self.image_dir=input_dir\n        \n        self.transforms = transforms\n    \n    # next lets define __getitem__\n    # very important to note what it returns for EACH image:\n    # I. image - a PIL image of size (H,W) for ResNet50 FPN image should be scaled\n    \n    # II. image_id \n    \n    def __getitem__(self,idx):\n        \n        # II. image_id\n        img_id = self.list_images[idx]\n        # I. Input image\n        # Specifications: A.RGB format B. scaled (0,1) C. size (H,W) D. PIL format\n        \n        img = cv2.imread(self.image_dir+\"/\"+img_id+\".jpg\")\n        img_RGB = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n        img_scaled = img_RGB/255.0\n        img_final = img_scaled\n        \n        if self.transforms is not None:\n            img_final = self.transforms(img_final)\n        \n        \n        \n        \n        return img_final, img_id\n    \n    # next lets define __len__    \n    def __len__(self):\n        \n        return len(self.df['image_id'].unique())","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"868889ff-e3b3-4727-b2c9-df141602f3f3","_cell_guid":"2f3438f4-7d63-4c95-81e2-d81c6a4d9a8e","trusted":true},"cell_type":"code","source":"df_test=pd.read_csv(\"/kaggle/input/global-wheat-detection/sample_submission.csv\")","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"085713cd-ae01-4ffb-8b4c-4ee398a5910e","_cell_guid":"dcbd3734-6f82-45ab-b9a5-047c78d49b88","trusted":true},"cell_type":"code","source":"df_test.head()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"027ba58b-09f3-4e4f-a7b9-4d7494494d4d","_cell_guid":"e4f86237-eccb-463e-a85b-34d63c263bdb","trusted":true},"cell_type":"code","source":"# df_test['x0'] = df_test['bbox'].map(lambda x: x[1:-1].split(\",\")[0]).astype(float)\n# df_test['y0'] = df_test['bbox'].map(lambda x: x[1:-1].split(\",\")[1]).astype(float)\n# df_test['w'] = df_test['bbox'].map(lambda x: x[1:-1].split(\",\")[2]).astype(float)\n# df_test['h'] = df_test['bbox'].map(lambda x: x[1:-1].split(\",\")[3]).astype(float)\n# df_test['x1'] = df_test['x0'] + df_test['w']\n# df_test['y1'] = df_test['y0'] + df_test['h']","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"40c958ac-2b13-43f1-a623-6df4dd8c22e5","_cell_guid":"51e152d1-f318-4985-bcb6-fc6607f23a8c","trusted":true},"cell_type":"code","source":"test_dataset = GlobalWheatDetectionTestDataset(df_test,test_dir,get_transform())","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"0d04b959-bcfc-4c96-a84c-8ed12a854d92","_cell_guid":"c76db00f-8f1d-41fa-8226-17727a923a35","trusted":true},"cell_type":"code","source":"test_dataloader = DataLoader(test_dataset, batch_size=8,shuffle=False, num_workers=2,collate_fn=collate_fn)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"5220b5fe-0652-49d1-9285-94c4cbc8fc0d","_cell_guid":"92e338ae-5065-4f4c-9994-c6b1900a4557","trusted":true},"cell_type":"code","source":"detection_threshold = 0.45","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"43984e34-72be-4dc3-9bcb-c2f30dc9a591","_cell_guid":"933e1b00-e3cb-41a4-97ce-2e3097175080","trusted":true},"cell_type":"code","source":"def format_prediction_string(boxes, scores): ## Define the formate for storing prediction results\n    pred_strings = []\n    for j in zip(scores, boxes):\n        pred_strings.append(\"{0:.4f} {1} {2} {3} {4}\".format(j[0], j[1][0], j[1][1], j[1][2], j[1][3]))\n\n    return \" \".join(pred_strings)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"5062dbd4-0883-4674-a0b4-4f126f1d1159","_cell_guid":"bc6ac1dc-2bf7-482d-8574-52369f2dce40","trusted":true},"cell_type":"code","source":"device = torch.device('cuda')","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"51d98aba-42e4-4727-bc06-9b780f2d5daa","_cell_guid":"645e88dc-0dde-49da-a904-401af768854f","trusted":true},"cell_type":"code","source":"## Lets make the prediction\nresults=[]\ntrained_model.eval()\nimages = []\noutputs =[]\nfor images_, image_ids in test_dataloader:    \n\n    images = list(image.to(device,dtype=torch.float) for image in images_)\n    outputs = trained_model(images)\n\n    for i, image in enumerate(images):\n\n        boxes = outputs[i]['boxes'].data.cpu().numpy()    ##Formate of the output's box is [Xmin,Ymin,Xmax,Ymax]\n        scores = outputs[i]['scores'].data.cpu().numpy()\n        \n        boxes = boxes[scores >= detection_threshold].astype(np.int32) #Compare the score of output with the threshold and\n        scores = scores[scores >= detection_threshold]                    #slelect only those boxes whose score is greater\n                                                                          # than threshold value\n        image_id = image_ids[i]\n        \n        boxes[:, 2] = boxes[:, 2] - boxes[:, 0]         \n        boxes[:, 3] = boxes[:, 3] - boxes[:, 1]         #Convert the box formate to [Xmin,Ymin,W,H]\n        \n        \n            \n        result = {                                     #Store the image id and boxes and scores in result dict.\n            'image_id': image_id,\n            'PredictionString': format_prediction_string(boxes, scores)\n        }\n\n        \n        results.append(result)              #Append the result dict to Results list\n\ntest_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])\ntest_df.head()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"22983043-1e45-42ba-a04a-2a80d993f5ac","_cell_guid":"5bb33cd6-0e4c-4864-94af-281e60358727","trusted":true},"cell_type":"code","source":"#os.chdir(\"/kaggle/working\")","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"593b3316-290b-4d3c-af77-74e4f39ff816","_cell_guid":"69e3e27f-96de-49d7-8434-7eb56df936af","trusted":true},"cell_type":"code","source":"test_df.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"7c66e501-3635-4570-a679-a9a6e92efb6c","_cell_guid":"84a5c8e1-fdf7-42e1-8b87-5d2886ee4f73","trusted":true},"cell_type":"code","source":"# from IPython.display import FileLink\n# FileLink(r'submission_1.csv')","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"b42abf65-efc5-46f0-aa4c-daf9fe727d0f","_cell_guid":"2645cea8-8786-428c-91bf-a8a343e35fe0","trusted":true},"cell_type":"code","source":"sample = images[1].permute(1,2,0).cpu().numpy()\nboxes = outputs[1]['boxes'].data.cpu().numpy()\nscores = outputs[1]['scores'].data.cpu().numpy()\n\nboxes = boxes[scores >= detection_threshold].astype(np.int32)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"f1e15387-a3b0-4913-a6f9-3d6e8bec23a3","_cell_guid":"9979f889-0e0f-4ef7-8548-88e59d3c54fb","trusted":true},"cell_type":"code","source":"fig, ax = plt.subplots(1, 1, figsize=(16, 8))\n\nfor box in boxes:\n    cv2.rectangle(sample,\n                  (box[0], box[1]),\n                  (box[2], box[3]),\n                  (220, 0, 0), 2)\n    \nax.set_axis_off()\nax.imshow(sample)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"484821d6-daeb-4e4b-aa17-d5d4d0b133db","_cell_guid":"05e5ccba-682b-473e-87f4-84f13e993c8e","trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}