{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Hyperparameter Search for FasterRCNN and Visualizing Predictions\n\nIn this notebook, I'll demonstrate the [Weights and Biases library](https://docs.wandb.com/)'s automated hyperparameter optimization functionality on FasterRCNN. We'll use it to measure our model's performance quickly, find optimal hyperparameters, monitor system stats like GPU utilization and log the bounding boxes predicted by our FasterRCNN models.\n\n## [Explore results in a live dashboard →](https://app.wandb.ai/kshen/GWD-fasterRCNN?workspace=user-lavanyashukla)\n![](https://i.imgur.com/Jt8EKf0.png)\n\nOnly a small subset of the data will be used to demonstrate training and validation to avoid GPU out of memory error due to Kaggle's memory limit. This is only to demonstrate how to integrate W&B and the code can be modified to suit your purpose or run on a local machine.\n\nIn this example, we are trying to find the best combination of epochs, batch size, and learning rate to minimize the training loss. You can set what metric to optimize for, we will just use training loss as an example. Other metrics/scores such as the Average Precision Score will also be logged for the validation set (technically the Mean Average Precision Score if evaluated on entire set, see [this kernel](https://www.kaggle.com/pestipeti/competition-metric-details-script)).\n\n### If you like this kernel please give it an upvote :)\n\n- [Docs for W&B hyperparameter sweeps](https://docs.wandb.com/sweeps)\n\n- [W&B for Kaggle](https://www.wandb.com/kaggle)\n\n- Original FasterRCNN notebooks (@pestipeti):\n\n    - [Kernel 1](https://www.kaggle.com/pestipeti/pytorch-starter-fasterrcnn-train)\n\n    - [Kernel 2](https://www.kaggle.com/pestipeti/pytorch-starter-fasterrcnn-inference)","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"# Setup","execution_count":null},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport cv2\nimport os\nimport re\nimport gc\n\nfrom PIL import Image\n\nimport albumentations as A\nfrom albumentations.pytorch.transforms import ToTensorV2\n\nimport torch\nimport torchvision\n\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\nfrom torchvision.models.detection import FasterRCNN\nfrom torchvision.models.detection.rpn import AnchorGenerator\n\nfrom torch.utils.data import DataLoader, Dataset\nfrom torch.utils.data.sampler import SequentialSampler\n\nfrom matplotlib import pyplot as plt\n\nDIR_INPUT = '/kaggle/input/global-wheat-detection'\nDIR_TRAIN = f'{DIR_INPUT}/train'\nDIR_TEST = f'{DIR_INPUT}/test'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Install + import Weights and Biases\n!pip install --upgrade wandb\nimport wandb","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df = pd.read_csv(f'{DIR_INPUT}/train.csv')\ntrain_df.shape\ntrain_df.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Extract the bbox column and explode it into x,y,w,h\ntrain_df['x'] = -1\ntrain_df['y'] = -1\ntrain_df['w'] = -1\ntrain_df['h'] = -1\n\ndef expand_bbox(x):\n    r = np.array(re.findall(\"([0-9]+[.]?[0-9]*)\", x))\n    if len(r) == 0:\n        r = [-1, -1, -1, -1]\n    return r\n\ntrain_df[['x', 'y', 'w', 'h']] = np.stack(train_df['bbox'].apply(lambda x: expand_bbox(x)))\ntrain_df.drop(columns=['bbox'], inplace=True)\ntrain_df['x'] = train_df['x'].astype(np.float)\ntrain_df['y'] = train_df['y'].astype(np.float)\ntrain_df['w'] = train_df['w'].astype(np.float)\ntrain_df['h'] = train_df['h'].astype(np.float)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Select a small subset for demo purposes.","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"image_ids = train_df['image_id'].unique()\n#Original: 3373 total images divided into 2708 for training, 665 for validation\n#valid_ids = image_ids[-665:]\n#train_ids = image_ids[:-665]\n\n#Select smaller set: 677 for training, 166 for validation (1/4 size original) to avoid any CUDA out of memory issues\ntrain_ids = image_ids[:677]\nvalid_ids = image_ids[677:677+166]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(image_ids.shape)\nprint(valid_ids.shape)\nprint(train_ids.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#take the bounding boxes and put them into their respective category (train, valid) by id \nvalid_df = train_df[train_df['image_id'].isin(valid_ids)]\ntrain_df = train_df[train_df['image_id'].isin(train_ids)]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"valid_df.shape, train_df.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class WheatDataset(Dataset):\n\n    def __init__(self, dataframe, image_dir, transforms=None):\n        super().__init__()\n\n        self.image_ids = dataframe['image_id'].unique() #all images\n        self.df = dataframe\n        self.image_dir = image_dir\n        self.transforms = transforms\n\n    def __getitem__(self, index: int):\n\n        image_id = self.image_ids[index]\n        records = self.df[self.df['image_id'] == image_id]\n        \n        #load image and normalize image pixels to 0-1\n        image = cv2.imread(f'{self.image_dir}/{image_id}.jpg', cv2.IMREAD_COLOR)\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32)\n        image /= 255.0\n        \n        boxes = records[['x', 'y', 'w', 'h']].values\n        #turn each bounding box into format [x_start, y_start, x_end, y_end]\n        boxes[:, 2] = boxes[:, 0] + boxes[:, 2]\n        boxes[:, 3] = boxes[:, 1] + boxes[:, 3]\n        \n        #calculate areas of each bounding box\n        area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])\n        area = torch.as_tensor(area, dtype=torch.float32)\n\n        # there is only one class\n        labels = torch.ones((records.shape[0],), dtype=torch.int64)\n        \n        # suppose all instances are not crowd\n        iscrowd = torch.zeros((records.shape[0],), dtype=torch.int64)\n        \n        target = {}\n        target['boxes'] = boxes\n        target['labels'] = labels #all boxes are labelled as 1 (wheat head)\n        # target['masks'] = None\n        target['image_id'] = torch.tensor([index])\n        target['area'] = area\n        target['iscrowd'] = iscrowd\n        \n        #apply transformations to this image\n        if self.transforms:\n            sample = {\n                'image': image,\n                'bboxes': target['boxes'],\n                'labels': labels\n            }\n            sample = self.transforms(**sample)\n            image = sample['image']\n            \n            target['boxes'] = torch.stack(tuple(map(torch.tensor, zip(*sample['bboxes'])))).permute(1, 0)\n\n        return image, target, image_id\n\n    def __len__(self) -> int:\n        return self.image_ids.shape[0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Albumentations (pretty much image data augmentation)\ndef get_train_transform():\n    return A.Compose([\n        A.Flip(0.5),\n        ToTensorV2(p=1.0)\n    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})\n\ndef get_valid_transform():\n    return A.Compose([\n        ToTensorV2(p=1.0)\n    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})\n\n#Collate function\ndef collate_fn(batch):\n    return tuple(zip(*batch))\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#IOU Metric Calculation\nfrom collections import namedtuple\nfrom typing import List, Union\n\nBox = namedtuple('Box', 'xmin ymin xmax ymax')\n\n\ndef calculate_iou(gt: List[Union[int, float]],\n                  pred: List[Union[int, float]],\n                  form: str = 'pascal_voc') -> float:\n    \"\"\"Calculates the IoU.\n    \n    Args:\n        gt: List[Union[int, float]] coordinates of the ground-truth box\n        pred: List[Union[int, float]] coordinates of the prdected box\n        form: str gt/pred coordinates format\n            - pascal_voc: [xmin, ymin, xmax, ymax]\n            - coco: [xmin, ymin, w, h]\n    Returns:\n        IoU: float Intersection over union (0.0 <= iou <= 1.0)\n    \"\"\"\n    if form == 'coco':\n        bgt = Box(gt[0], gt[1], gt[0] + gt[2], gt[1] + gt[3])\n        bpr = Box(pred[0], pred[1], pred[0] + pred[2], pred[1] + pred[3])\n    else:\n        bgt = Box(gt[0], gt[1], gt[2], gt[3])\n        bpr = Box(pred[0], pred[1], pred[2], pred[3])\n        \n\n    overlap_area = 0.0\n    union_area = 0.0\n\n    # Calculate overlap area\n    dx = min(bgt.xmax, bpr.xmax) - max(bgt.xmin, bpr.xmin)\n    dy = min(bgt.ymax, bpr.ymax) - max(bgt.ymin, bpr.ymin)\n\n    if (dx > 0) and (dy > 0):\n        overlap_area = dx * dy\n\n    # Calculate union area\n    union_area = (\n            (bgt.xmax - bgt.xmin) * (bgt.ymax - bgt.ymin) +\n            (bpr.xmax - bpr.xmin) * (bpr.ymax - bpr.ymin) -\n            overlap_area\n    )\n\n    return overlap_area / union_area","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#MAP Calculation\ndef find_best_match(gts, predd, threshold=0.5, form='pascal_voc'):\n    \"\"\"Returns the index of the 'best match' between the\n    ground-truth boxes and the prediction. The 'best match'\n    is the highest IoU. (0.0 IoUs are ignored).\n    \n    Args:\n        gts: Coordinates of the available ground-truth boxes\n        pred: Coordinates of the predicted box\n        threshold: Threshold\n        form: Format of the coordinates\n        \n    Return:\n        Index of the best match GT box (-1 if no match above threshold)\n    \"\"\"\n    best_match_iou = -np.inf\n    best_match_idx = -1\n    \n    for gt_idx, ggt in enumerate(gts):\n        iou = calculate_iou(ggt, predd, form=form)\n        \n        if iou < threshold:\n            continue\n        \n        if iou > best_match_iou:\n            best_match_iou = iou\n            best_match_idx = gt_idx\n\n    return best_match_idx\n\n\ndef calculate_precision(preds_sorted, gt_boxes, threshold=0.5, form='coco'):\n    \"\"\"Calculates precision per at one threshold.\n    \n    Args:\n        preds_sorted: \n    \"\"\"\n    tp = 0\n    fp = 0\n    fn = 0\n\n    fn_boxes = []\n\n    for pred_idx, pred in enumerate(preds_sorted):\n        best_match_gt_idx = find_best_match(gt_boxes, pred, threshold=threshold, form=form)\n\n        if best_match_gt_idx >= 0:\n            # True positive: The predicted box matches a gt box with an IoU above the threshold.\n            tp += 1\n\n            # Remove the matched GT box\n            gt_boxes = np.delete(gt_boxes, best_match_gt_idx, axis=0)\n\n        else:\n            # No match\n            # False positive: indicates a predicted box had no associated gt box.\n            fn += 1\n            fn_boxes.append(pred)\n\n    # False negative: indicates a gt box had no associated predicted box.\n    fp = len(gt_boxes)\n    precision = tp / (tp + fp + fn)\n    return precision, fn_boxes, gt_boxes\n\n\ndef calculate_image_precision(preds_sorted, gt_boxes, thresholds=(0.5), form='coco', debug=False):\n    \n    n_threshold = len(thresholds)\n    image_precision = 0.0\n    \n    for threshold in thresholds:\n        precision_at_threshold, _, _ = calculate_precision(preds_sorted,\n                                                           gt_boxes,\n                                                           threshold=threshold,\n                                                           form=form\n                                                          )\n        if debug:\n            print(\"@{0:.2f} = {1:.4f}\".format(threshold, precision_at_threshold))\n\n        image_precision += precision_at_threshold / n_threshold\n    \n    return image_precision","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Logging metrics and predicted bounding boxes\nHere we define train and test functions that will use the W&B logging features as well as setting up the hyperparameter sweep configuration.","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"\n\ndef train(args, model, device, train_data_loader, optimizer, epoch, iteration):\n    print(\"Epoch: \", epoch)\n    \n    #Train Loop\n    for batch_idx, (images, targets, image_ids) in enumerate(train_data_loader):\n        \n        #Train and calculate loss\n        model.train()\n        images = list(image.to(device) 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)\n        \n        losses = sum(loss for loss in loss_dict.values())\n        loss_value = losses.item()\n\n        optimizer.zero_grad()\n        losses.backward()\n        optimizer.step()\n        if batch_idx % args.log_interval == 0:\n            #Log the train loss and iteration\n            wandb.log({\"train_loss\": loss_value, \"iteration_train\": iteration})\n        \n        iteration += 1 #iteration increases for every batch\n        \n    \n    return iteration #returns the updated new iteration\n            ","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"In the test function we will also log the bounding boxes so we can see visualizations.","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"#[xmin, ymin, xmax, ymax] => [x, y, width, height]\ndef convert_boxes_format(boxes):\n    boxes[:, 2] = boxes[:, 2] - boxes[:, 0]\n    boxes[:, 3] = boxes[:, 3] - boxes[:, 1]\n    return boxes\n\nclass_id_to_label = {\n    0: \"target\",\n    1: \"wheat\"\n}\n\n#Test will run as validation and log bounding boxes with confidence/prediction scores\ndef test(args, model, device, test_data_loader, epoch, iteration):\n    model.eval()\n    \n    epoch_precision_score = []\n    for batch_idx, (images, targets, image_ids) in enumerate(test_data_loader):\n        images = list(image.to(device) for image in images)\n        targets = [{k: v.to(device) for k, v in t.items()} for t in targets]\n\n        outputs = model(images)\n        \n        #Calculate Precision Score Per Batch\n        batch_precision_score = 0\n        for i, image in enumerate(images):\n            \n            #predicted bounding boxes\n            boxes = outputs[i]['boxes'].data.cpu().numpy()\n            boxes = convert_boxes_format(boxes)\n            \n            scores = outputs[i]['scores'].data.cpu().numpy()\n            \n            #ground truth bounding boxes\n            gt_boxes = targets[i][\"boxes\"].cpu().numpy()\n            gt_boxes = convert_boxes_format(gt_boxes)\n\n            image_id = image_ids[i]\n\n            # Sort highest confidence -> lowest confidence\n            preds_sorted_idx = np.argsort(scores)[::-1]\n            preds_sorted = boxes[preds_sorted_idx]\n\n            iou_thresholds = [x for x in np.arange(0.5, 0.76, 0.05)]\n            image_precision = calculate_image_precision(preds_sorted, gt_boxes,\n                                            thresholds=iou_thresholds,\n                                            form='coco', debug=True)\n            \n            print(\"Average Precision of image: {0:.4f}\".format(image_precision))\n            batch_precision_score +=  image_precision\n        \n        batch_precision_score = batch_precision_score / len(images) #average precision score for the batch\n        epoch_precision_score.append(batch_precision_score)\n        \n        if batch_idx % args.log_interval == 0:\n            #logs the precision score per batch and also the iteration\n            wandb.log({\"batch_score_validation\": batch_precision_score, \"iteration_validation\": iteration})\n        \n        #Log bounding boxes\n        if batch_idx % args.image_log_interval == 0:\n            \n            #Log 1 image with bounding boxes for this batch\n            for i, image in enumerate(images[:1]): \n\n                image_id = image_ids[i]\n                scores = outputs[i]['scores'].data.cpu().numpy().astype(np.float64)\n\n                #predicted bounding boxes\n                boxes = outputs[i]['boxes'].data.cpu().numpy().astype(np.float64)\n                predicted_boxes = []\n                for b_i, box in enumerate(boxes):\n    \n                    box_data = {\"position\" : {\n                          \"minX\" : box[0],\n                          \"maxX\" : box[2],\n                          \"minY\" : box[1],\n                          \"maxY\" : box[3] \n                        },\n                      \"class_id\" : 1,\n                      \"box_caption\" : \"wheat: (%.3f)\" % (scores[b_i]),\n                      \"domain\": \"pixel\",\n                      \"scores\" : { \"score\" : scores[b_i] }\n                    }\n                    predicted_boxes.append(box_data)\n                    \n\n                #ground truth bounding boxes\n                gt_boxes = targets[i][\"boxes\"].cpu().numpy().astype(np.float64)\n                target_boxes = [] \n                for b_i, box in enumerate(gt_boxes):\n  \n                    box_data = {\"position\" : {\n                          \"minX\" : box[0],\n                          \"maxX\" : box[2],\n                          \"minY\" : box[1],\n                          \"maxY\" : box[3] \n                        },\n                      \"class_id\" : 0,\n                      \"domain\": \"pixel\",\n                      \"box_caption\" : \"ground_truth\"\n                    }\n                    target_boxes.append(box_data)\n                \n                \n                image = image.permute(1,2,0).cpu().numpy().astype(np.float64)\n                \n                #create image object and log\n                img = wandb.Image(image, boxes = \n                                  {\"predictions\": \n                                   {\"box_data\": predicted_boxes, \n                                    \"class_labels\" : class_id_to_label},\"ground_truth\": {\"box_data\": target_boxes}})\n                \n                wandb.log({\"bounding_boxes\": img})\n            \n        iteration += 1\n    \n    epoch_precision_score = sum(epoch_precision_score) / len(epoch_precision_score) \n    wandb.log({\"epoch_score_validation\": batch_precision_score, \"epoch\": epoch})\n    return iteration\n        \n    ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Load dataset\ntrain_dataset = WheatDataset(train_df, DIR_TRAIN, get_train_transform())\nvalid_dataset = WheatDataset(valid_df, DIR_TRAIN, get_valid_transform())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Define Sweep Configuration and Run\nA sweep configuration defines all the values that W&B will try and the metric that it will be using to judge which set of values do the best.\n","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"#Hyperparameter sweep configuration (more info the the W&B docs)\nsweep_config = {\n    'method': 'grid', #grid, random (random will continue running until you terminate it or specify a target field for metric)\n    'metric': {\n      'name': 'train_loss',\n      'goal': 'minimize'   \n    },\n    'parameters': {\n        'epochs': {\n            'values': [2, 4]\n        },\n        'batch_size': {\n            'values': [4, 8]\n        },\n        'lr': {\n            'values': [1e-3, 5e-4] \n        }\n    }\n}\n\n#initialize the sweep\n#Running this line will ask you to log into your W&B account\nsweep_id = wandb.sweep(sweep_config, project=\"GWD-fasterRCNN\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The run function is the main function.\n\nNote that we are keeping track of the iterations separately for train and test. \n\nThis will allow for easier plotting of values in your W&B dashboard.\n\nYou can set the X axis for charts to use iteration_train for displaying training loss/accuracy, or iteration_test for displaying values such as validation/test loss/accuracy.\n\nThe wandb.watch() function only needs to be called once, so in subsequent runs we can comment it out.","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"def run():\n    \n    #Default Hyperparameter values if no sweep is defined\n    config_default = {         \n    \"batch_size\": 8,          # input batch size for training (default: 64)\n    \"test_batch_size\": 8,    # input batch size for testing (default: 1000)\n    \"epochs\": 2,             # number of epochs to train (default: 10)\n    \"lr\": 0.005,               # learning rate (default: 0.01)\n    \"momentum\": 0.9,          # SGD momentum (default: 0.5) \n    \"no_cuda\": False,         # whether to disable CUDA training\n    \"seed\": 42,               # random seed (default: 42)\n    \"log_interval\": 1,      #how many batches to wait before logging in train/test loops\n    \"image_log_interval\": 10,\n    \"decay\": 0.0005\n    }\n    wandb.init(config=config_default, project=\"GWD-fasterRCNN\")\n    config = wandb.config\n    \n    # load a model; pre-trained on COCO\n    model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)\n    num_classes = 2  # wheat (1)....or not wheat (0)\n    # get number of input features for the classifier\n    in_features = model.roi_heads.box_predictor.cls_score.in_features\n    # replace the pre-trained head with a new one\n    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n\n    use_cuda = not config[\"no_cuda\"] and torch.cuda.is_available()\n    device = torch.device('cuda') if use_cuda else torch.device('cpu')\n\n    # Set random seeds and deterministic pytorch for reproducibility\n    # random.seed(config.seed)       # python random seed\n    torch.manual_seed(config[\"seed\"]) # pytorch random seed\n    np.random.seed(config[\"seed\"]) # numpy random seed\n    torch.backends.cudnn.deterministic = True\n\n\n    #Create dataloaders\n    train_data_loader = DataLoader(\n        train_dataset,\n        batch_size=config[\"batch_size\"],\n        shuffle=False,\n        num_workers=4,\n        collate_fn=collate_fn\n    )\n\n    valid_data_loader = DataLoader(\n        valid_dataset,\n        batch_size=config[\"test_batch_size\"],\n        shuffle=False,\n        num_workers=4,\n        collate_fn=collate_fn\n    )\n\n\n    model.to(device)\n    params = [p for p in model.parameters() if p.requires_grad]\n    optimizer = torch.optim.SGD(params, lr=config[\"lr\"], momentum=config[\"momentum\"], weight_decay=config[\"decay\"])\n    \n    ####\n    wandb.watch(model, log=\"all\") #this line only needs to be run once to hook W&B to your model (comment out for future runs)\n    ####\n    \n    #Keep track of train and test's iterations seperatedly (this allows for easier plotting because you can use iteration_train or iteration_test for your X axis)\n    iteration_train = 0\n    iteration_test = 0\n    for epoch in range(1, config[\"epochs\"] + 1):\n        #Runs training and returns the new training iteration counter\n        iteration_train = train(config, model, device, train_data_loader, optimizer, epoch, iteration_train)\n        #Runs testing/valiadtion and returns the new test iteration counter\n        iteration_test = test(config, model, device, valid_data_loader, epoch, iteration_test)\n        print(\"Iteration Train: \", iteration_train)\n        print(\"Iteration Test: \", iteration_test)\n\n    torch.save(model.state_dict(), 'fasterrcnn_resnet50_fpn.pth')\n    wandb.save('fasterrcnn_resnet50_fpn.pth')\n    \n    #Clear memory\n    del model\n    del train_data_loader\n    del valid_data_loader\n    gc.collect()\n    torch.cuda.empty_cache()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Run sweep\nwandb.agent(sweep_id, run)\nprint(\"FINISHED\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Hyperparameter sweep results\nThe HP sweep will produce 2x2x2=8 runs (because in the sweep config, there are 2 values being tested for each of epochs,batch size,and lr)\n\nhttps://app.wandb.ai/kshen/GWD-fasterRCNN/sweeps/uuo3vref\n","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"Weights and Biases provides visualizations to compare different HP configurations. Each run can be plotted on a parallel chart. \n\nHere we can see that the run highlighted in purple has a hyperparameter config that results in the smallest train loss.\n\n![Screenshot_99.png](attachment:Screenshot_99.png)","attachments":{"Screenshot_99.png":{"image/png":"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"}},"execution_count":null},{"metadata":{},"cell_type":"markdown","source":"\n\nBy metrics batch_score_validation or train_loss we can see that certain runs/configurations can produce better scores:\n\n![Screenshot_98.png](attachment:Screenshot_98.png)","attachments":{"Screenshot_98.png":{"image/png":"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"}},"execution_count":null},{"metadata":{},"cell_type":"markdown","source":"### Bounding Boxes\n\nWe can also see the bounding boxes that we logged for our runs (a sample image from a run is shown below). Visualizing bounding boxes can help you see what your model is getting right and wrong by visualizing the ground truth boxes compared with our predictions. \n\nIn the sample below, we can see that one major source of error is that leaves can be detected as wheat heads. However, as mentioned at the beginning, since this is only trained on a small subset for demo purposes, the conclusions made here shouldn't be taken at face value.\n\n![](https://i.imgur.com/yP2sF1k.png)","execution_count":null}],"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}