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| ### Paper | ||
| 1 [mask-rcnn](https://arxiv.org/pdf/1703.06870.pdf) | ||
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| ### dataset | ||
| 1 [cityscapesScripts](https://github.com/mcordts/cityscapesScripts) | ||
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| ### Performance (from paper) | ||
| | case | training data | im/gpu | mask AP[val] | mask AP [test] | mask AP50 [test] | | ||
| |--------------|:-------------:|:------:|:------------:|:--------------:|-----------------:| | ||
| | R-50-FPN | fine | 8/8 | 31.5 | 26.2 | 49.9 | | ||
| | R-50-FPN | fine + COCO | 8/8 | 36.4 | 32.0 | 58.1 | | ||
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| ### Note (from paper) | ||
| We apply our Mask R-CNN models with the ResNet-FPN-50 backbone; we found the 101-layer counterpart performs similarly due to the small dataset size. We train with image scale (shorter side) randomly sampled from [800, 1024], which reduces overfitting; inference is on a single scale of 1024 pixels. We use a mini-batch size of 1 image per GPU (so 8 on 8 GPUs) and train the model for 24k iterations, starting from a learning rate of 0.01 and reducing it to 0.001 at 18k iterations. It takes ∼4 hours of training on a single 8-GPU machine under this setting. | ||
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| ### Implemetation (for finetuning from coco trained model) | ||
| Step 1: download trained model on coco dataset from [model zoo](https://download.pytorch.org/models/maskrcnn/e2e_mask_rcnn_R_50_FPN_1x.pth) | ||
| Step 2: do the model surgery on the trained model as below and use it as `pretrained model` for finetuning: | ||
| ```python | ||
| def clip_weights_from_pretrain_of_coco_to_cityscapes(f, out_file): | ||
| """""" | ||
| # COCO categories for pretty print | ||
| COCO_CATEGORIES = [ | ||
| "__background__", | ||
| "person", | ||
| "bicycle", | ||
| "car", | ||
| "motorcycle", | ||
| "airplane", | ||
| "bus", | ||
| "train", | ||
| "truck", | ||
| "boat", | ||
| "traffic light", | ||
| "fire hydrant", | ||
| "stop sign", | ||
| "parking meter", | ||
| "bench", | ||
| "bird", | ||
| "cat", | ||
| "dog", | ||
| "horse", | ||
| "sheep", | ||
| "cow", | ||
| "elephant", | ||
| "bear", | ||
| "zebra", | ||
| "giraffe", | ||
| "backpack", | ||
| "umbrella", | ||
| "handbag", | ||
| "tie", | ||
| "suitcase", | ||
| "frisbee", | ||
| "skis", | ||
| "snowboard", | ||
| "sports ball", | ||
| "kite", | ||
| "baseball bat", | ||
| "baseball glove", | ||
| "skateboard", | ||
| "surfboard", | ||
| "tennis racket", | ||
| "bottle", | ||
| "wine glass", | ||
| "cup", | ||
| "fork", | ||
| "knife", | ||
| "spoon", | ||
| "bowl", | ||
| "banana", | ||
| "apple", | ||
| "sandwich", | ||
| "orange", | ||
| "broccoli", | ||
| "carrot", | ||
| "hot dog", | ||
| "pizza", | ||
| "donut", | ||
| "cake", | ||
| "chair", | ||
| "couch", | ||
| "potted plant", | ||
| "bed", | ||
| "dining table", | ||
| "toilet", | ||
| "tv", | ||
| "laptop", | ||
| "mouse", | ||
| "remote", | ||
| "keyboard", | ||
| "cell phone", | ||
| "microwave", | ||
| "oven", | ||
| "toaster", | ||
| "sink", | ||
| "refrigerator", | ||
| "book", | ||
| "clock", | ||
| "vase", | ||
| "scissors", | ||
| "teddy bear", | ||
| "hair drier", | ||
| "toothbrush", | ||
| ] | ||
| # Cityscapes of fine categories for pretty print | ||
| CITYSCAPES_FINE_CATEGORIES = [ | ||
| "__background__", | ||
| "person", | ||
| "rider", | ||
| "car", | ||
| "truck", | ||
| "bus", | ||
| "train", | ||
| "motorcycle", | ||
| "bicycle", | ||
| ] | ||
| coco_cats = COCO_CATEGORIES | ||
| cityscapes_cats = CITYSCAPES_FINE_CATEGORIES | ||
| coco_cats_to_inds = dict(zip(coco_cats, range(len(coco_cats)))) | ||
| cityscapes_cats_to_inds = dict( | ||
| zip(cityscapes_cats, range(len(cityscapes_cats))) | ||
| ) | ||
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| checkpoint = torch.load(f) | ||
| m = checkpoint['model'] | ||
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| weight_names = { | ||
| "cls_score": "module.roi_heads.box.predictor.cls_score.weight", | ||
| "bbox_pred": "module.roi_heads.box.predictor.bbox_pred.weight", | ||
| "mask_fcn_logits": "module.roi_heads.mask.predictor.mask_fcn_logits.weight", | ||
| } | ||
| bias_names = { | ||
| "cls_score": "module.roi_heads.box.predictor.cls_score.bias", | ||
| "bbox_pred": "module.roi_heads.box.predictor.bbox_pred.bias", | ||
| "mask_fcn_logits": "module.roi_heads.mask.predictor.mask_fcn_logits.bias", | ||
| } | ||
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| representation_size = m[weight_names["cls_score"]].size(1) | ||
| cls_score = nn.Linear(representation_size, len(cityscapes_cats)) | ||
| nn.init.normal_(cls_score.weight, std=0.01) | ||
| nn.init.constant_(cls_score.bias, 0) | ||
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| representation_size = m[weight_names["bbox_pred"]].size(1) | ||
| class_agnostic = m[weight_names["bbox_pred"]].size(0) != len(coco_cats) * 4 | ||
| num_bbox_reg_classes = 2 if class_agnostic else len(cityscapes_cats) | ||
| bbox_pred = nn.Linear(representation_size, num_bbox_reg_classes * 4) | ||
| nn.init.normal_(bbox_pred.weight, std=0.001) | ||
| nn.init.constant_(bbox_pred.bias, 0) | ||
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| dim_reduced = m[weight_names["mask_fcn_logits"]].size(1) | ||
| mask_fcn_logits = Conv2d(dim_reduced, len(cityscapes_cats), 1, 1, 0) | ||
| nn.init.constant_(mask_fcn_logits.bias, 0) | ||
| nn.init.kaiming_normal_( | ||
| mask_fcn_logits.weight, mode="fan_out", nonlinearity="relu" | ||
| ) | ||
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| def _copy_weight(src_weight, dst_weight): | ||
| for ix, cat in enumerate(cityscapes_cats): | ||
| if cat not in coco_cats: | ||
| continue | ||
| jx = coco_cats_to_inds[cat] | ||
| dst_weight[ix] = src_weight[jx] | ||
| return dst_weight | ||
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| def _copy_bias(src_bias, dst_bias, class_agnostic=False): | ||
| if class_agnostic: | ||
| return dst_bias | ||
| return _copy_weight(src_bias, dst_bias) | ||
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| m[weight_names["cls_score"]] = _copy_weight( | ||
| m[weight_names["cls_score"]], cls_score.weight | ||
| ) | ||
| m[weight_names["bbox_pred"]] = _copy_weight( | ||
| m[weight_names["bbox_pred"]], bbox_pred.weight | ||
| ) | ||
| m[weight_names["mask_fcn_logits"]] = _copy_weight( | ||
| m[weight_names["mask_fcn_logits"]], mask_fcn_logits.weight | ||
| ) | ||
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| m[bias_names["cls_score"]] = _copy_bias( | ||
| m[bias_names["cls_score"]], cls_score.bias | ||
| ) | ||
| m[bias_names["bbox_pred"]] = _copy_bias( | ||
| m[bias_names["bbox_pred"]], bbox_pred.bias, class_agnostic | ||
| ) | ||
| m[bias_names["mask_fcn_logits"]] = _copy_bias( | ||
| m[bias_names["mask_fcn_logits"]], mask_fcn_logits.bias | ||
| ) | ||
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| print("f: {}\nout_file: {}".format(f, out_file)) | ||
| torch.save(m, out_file) | ||
| ``` | ||
| Step 3: modify the `input&weight&solver` configuration in the `yaml` file, like this: | ||
| ``` | ||
| MODEL: | ||
| WEIGHT: "xxx.pth" # the model u save from above code | ||
| INPUT: | ||
| MIN_SIZE_TRAIN: (800, 832, 864, 896, 928, 960, 992, 1024, 1024) | ||
| MAX_SIZE_TRAIN: 2048 | ||
| MIN_SIZE_TEST: 1024 | ||
| MAX_SIZE_TEST: 2048 | ||
| SOLVER: | ||
| BASE_LR: 0.01 | ||
| IMS_PER_BATCH: 8 | ||
| WEIGHT_DECAY: 0.0001 | ||
| STEPS: (3000,) | ||
| MAX_ITER: 4000 | ||
| ``` | ||
| Step 4: train the model. | ||
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