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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from maskrcnn_benchmark.structures.bounding_box import BoxList
def convert_mask_grounding_to_od_logits(logits, positive_map_label_to_token, num_classes):
od_logits = torch.zeros(logits.shape[0], num_classes + 1, logits.shape[2], logits.shape[3]).to(logits.device)
for label_j in positive_map_label_to_token:
od_logits[:, label_j, :, :] = logits[:, torch.LongTensor(positive_map_label_to_token[label_j]), :, :].mean(1)
mask_prob = od_logits.sigmoid()
return mask_prob
# TODO check if want to return a single BoxList or a composite
# object
class MaskPostProcessor(nn.Module):
"""
From the results of the CNN, post process the masks
by taking the mask corresponding to the class with max
probability (which are of fixed size and directly output
by the CNN) and return the masks in the mask field of the BoxList.
If a masker object is passed, it will additionally
project the masks in the image according to the locations in boxes,
"""
def __init__(self, masker=None, mdetr_style_aggregate_class_num=None, vl_version=None):
super(MaskPostProcessor, self).__init__()
self.masker = masker
self.mdetr_style_aggregate_class_num = mdetr_style_aggregate_class_num
self.vl_version = vl_version
def forward(self, x, boxes, positive_map_label_to_token=None):
"""
Arguments:
x (Tensor): the mask logits
boxes (list[BoxList]): bounding boxes that are used as
reference, one for ech image
Returns:
results (list[BoxList]): one BoxList for each image, containing
the extra field mask
"""
if self.vl_version:
mask_prob = convert_mask_grounding_to_od_logits(
x, positive_map_label_to_token, self.mdetr_style_aggregate_class_num
)
else:
mask_prob = x.sigmoid()
# select masks coresponding to the predicted classes
num_masks = x.shape[0]
labels = [bbox.get_field("labels") for bbox in boxes]
labels = torch.cat(labels)
if not self.vl_version:
# TODO: a hack for binary mask head
labels = (labels > 0).to(dtype=torch.int64)
index = torch.arange(num_masks, device=labels.device)
mask_prob = mask_prob[index, labels][:, None]
boxes_per_image = [len(box) for box in boxes]
mask_prob = mask_prob.split(boxes_per_image, dim=0)
if self.masker:
mask_prob = self.masker(mask_prob, boxes)
results = []
for prob, box in zip(mask_prob, boxes):
bbox = BoxList(box.bbox, box.size, mode="xyxy")
for field in box.fields():
bbox.add_field(field, box.get_field(field))
bbox.add_field("mask", prob)
results.append(bbox)
return results
class MaskPostProcessorCOCOFormat(MaskPostProcessor):
"""
From the results of the CNN, post process the results
so that the masks are pasted in the image, and
additionally convert the results to COCO format.
"""
def forward(self, x, boxes, positive_map_label_to_token=None, vl_version=None):
import pycocotools.mask as mask_util
import numpy as np
results = super(MaskPostProcessorCOCOFormat, self).forward(x, boxes)
for result in results:
masks = result.get_field("mask").cpu()
rles = [mask_util.encode(np.array(mask[0, :, :, np.newaxis], order="F"))[0] for mask in masks]
for rle in rles:
rle["counts"] = rle["counts"].decode("utf-8")
result.add_field("mask", rles)
return results
# the next two functions should be merged inside Masker
# but are kept here for the moment while we need them
# temporarily gor paste_mask_in_image
def expand_boxes(boxes, scale):
w_half = (boxes[:, 2] - boxes[:, 0]) * 0.5
h_half = (boxes[:, 3] - boxes[:, 1]) * 0.5
x_c = (boxes[:, 2] + boxes[:, 0]) * 0.5
y_c = (boxes[:, 3] + boxes[:, 1]) * 0.5
w_half *= scale
h_half *= scale
boxes_exp = torch.zeros_like(boxes)
boxes_exp[:, 0] = x_c - w_half
boxes_exp[:, 2] = x_c + w_half
boxes_exp[:, 1] = y_c - h_half
boxes_exp[:, 3] = y_c + h_half
return boxes_exp
def expand_masks(mask, padding):
N = mask.shape[0]
M = mask.shape[-1]
pad2 = 2 * padding
scale = float(M + pad2) / M
padded_mask = mask.new_zeros((N, 1, M + pad2, M + pad2))
padded_mask[:, :, padding:-padding, padding:-padding] = mask
return padded_mask, scale
def paste_mask_in_image(mask, box, im_h, im_w, thresh=0.5, padding=1):
padded_mask, scale = expand_masks(mask[None], padding=padding)
mask = padded_mask[0, 0]
box = expand_boxes(box[None], scale)[0]
box = box.to(dtype=torch.int32)
TO_REMOVE = 1
w = int(box[2] - box[0] + TO_REMOVE)
h = int(box[3] - box[1] + TO_REMOVE)
w = max(w, 1)
h = max(h, 1)
# Set shape to [batchxCxHxW]
mask = mask.expand((1, 1, -1, -1))
# Resize mask
mask = mask.to(torch.float32)
mask = F.interpolate(mask, size=(h, w), mode="bilinear", align_corners=False)
mask = mask[0][0]
if thresh >= 0:
mask = mask > thresh
else:
# for visualization and debugging, we also
# allow it to return an unmodified mask
mask = (mask * 255).to(torch.bool)
im_mask = torch.zeros((im_h, im_w), dtype=torch.bool)
x_0 = max(box[0], 0)
x_1 = min(box[2] + 1, im_w)
y_0 = max(box[1], 0)
y_1 = min(box[3] + 1, im_h)
im_mask[y_0:y_1, x_0:x_1] = mask[(y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0])]
return im_mask
class Masker(object):
"""
Projects a set of masks in an image on the locations
specified by the bounding boxes
"""
def __init__(self, threshold=0.5, padding=1):
self.threshold = threshold
self.padding = padding
def forward_single_image(self, masks, boxes):
boxes = boxes.convert("xyxy")
im_w, im_h = boxes.size
res = [
paste_mask_in_image(mask[0], box, im_h, im_w, self.threshold, self.padding)
for mask, box in zip(masks, boxes.bbox)
]
if len(res) > 0:
res = torch.stack(res, dim=0)[:, None]
else:
res = masks.new_empty((0, 1, masks.shape[-2], masks.shape[-1]))
return res
def __call__(self, masks, boxes):
if isinstance(boxes, BoxList):
boxes = [boxes]
# Make some sanity check
assert len(boxes) == len(masks), "Masks and boxes should have the same length."
# TODO: Is this JIT compatible?
# If not we should make it compatible.
results = []
for mask, box in zip(masks, boxes):
assert mask.shape[0] == len(box), "Number of objects should be the same."
result = self.forward_single_image(mask, box)
results.append(result)
return results
def make_roi_mask_post_processor(cfg):
if cfg.MODEL.ROI_MASK_HEAD.POSTPROCESS_MASKS:
mask_threshold = cfg.MODEL.ROI_MASK_HEAD.POSTPROCESS_MASKS_THRESHOLD
masker = Masker(threshold=mask_threshold, padding=1)
else:
masker = None
mdetr_style_aggregate_class_num = cfg.TEST.MDETR_STYLE_AGGREGATE_CLASS_NUM
mask_post_processor = MaskPostProcessor(
masker, mdetr_style_aggregate_class_num, vl_version=cfg.MODEL.ROI_MASK_HEAD.PREDICTOR.startswith("VL")
)
return mask_post_processor
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