forked from facebookresearch/maskrcnn-benchmark
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
69 changed files
with
7,922 additions
and
231 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,217 @@ | ||
| ### Paper | ||
| 1 [mask-rcnn](https://arxiv.org/pdf/1703.06870.pdf) | ||
|
|
||
|
|
||
| ### dataset | ||
| 1 [cityscapesScripts](https://github.com/mcordts/cityscapesScripts) | ||
|
|
||
|
|
||
| ### 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 | | ||
|
|
||
|
|
||
| ### 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. | ||
|
|
||
|
|
||
| ### 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))) | ||
| ) | ||
|
|
||
| checkpoint = torch.load(f) | ||
| m = checkpoint['model'] | ||
|
|
||
| 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", | ||
| } | ||
|
|
||
| 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) | ||
|
|
||
| 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) | ||
|
|
||
| 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" | ||
| ) | ||
|
|
||
| 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 | ||
|
|
||
| def _copy_bias(src_bias, dst_bias, class_agnostic=False): | ||
| if class_agnostic: | ||
| return dst_bias | ||
| return _copy_weight(src_bias, dst_bias) | ||
|
|
||
| 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 | ||
| ) | ||
|
|
||
| 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 | ||
| ) | ||
|
|
||
| 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. | ||
|
|
Oops, something went wrong.