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# coding=utf-8
# Copyright 2024 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Post processing."""
import torch
import torch.nn.functional as F
# pylint: disable=g-bad-import-order
# pylint: disable=g-importing-member
from modeling.post_process.object_discovery import get_instances
from utils.metrics import IoM
# This should be a abstract function to generate masks for the input image.
# However, we first hack it due to the time limit.
def generate_masks_from_sam(
image_path, save_path, pipeline, img_sam=None, visualize=True
):
"""Generate masks from SAM."""
masks, _, mask_list = pipeline.segment_automask(
image_path=image_path,
visualize=visualize,
save_path=save_path,
image=img_sam,
)
mask_tensor = torch.from_numpy(masks)
mask_tensor = mask_tensor.float()
return mask_tensor, mask_list
def match_masks(
mask_tensor, attn_map, mask_list, iom_thres=0.0, min_pred_threshold=0.2
):
"""Match masks with the attention map according to the IoU.
Args:
mask_tensor: A torch.Tensor for the masks with shape [num_masks, height,
width].
attn_map: A torch.Tensor for the attention map with shape [1, 1, height,
width].
mask_list: A list of masks with shape [num_masks, height, width]
iom_thres: A float for the threshold to apply to the attention map.
min_pred_threshold: The prediction score threshold.
Returns:
A list of matched_masks with shape [num_masks, height, width],
len(matched_masks) = number of captions
"""
predictions = attn_map.squeeze(1).detach()
iom = IoM(predictions, mask_tensor, min_pred_threshold=min_pred_threshold)
keep_mask = iom > iom_thres
# mask_tensor = mask_tensor[keep_mask]
new_list = []
for mid, m_dict in enumerate(mask_list):
if keep_mask[mid]:
new_list.append(m_dict)
# if not len(new_list):
if not new_list:
max_id = torch.argmax(iom)
new_list.append(mask_list[max_id])
return new_list
def post_process_mask(attn_masks, pad=None, min_area_ratio=0.15):
"""Post process attention masks."""
if pad is not None:
left, top, width, height = pad
attn_masks = attn_masks[Ellipsis, top : top + height, left : left + width]
else:
height = None
width = None
mask_area = attn_masks.sum(dim=(1, 2))
total_area = mask_area.sum()
keep_mask = mask_area / total_area > min_area_ratio
if torch.sum(keep_mask) == 0:
if keep_mask.shape[0] == 0:
return torch.zeros(
(1, height, width), device=attn_masks.device, dtype=attn_masks.dtype
)
keep_mask[torch.argmax(mask_area)] = True
attn_masks = attn_masks[keep_mask]
return attn_masks
def filter_masks(
attn_masks,
pad=None,
mask_threshold=0.3,
min_area_ratio=0.15,
return_largest=False,
device=None,
return_instances=False,
):
"""Filter attention mask below the threshold."""
attn_masks[attn_masks < mask_threshold] = 0
# get_instances will be operated on cpu
ins_masks = get_instances(attn_masks, return_largest=return_largest)
ins_masks = [post_process_mask(m, pad, min_area_ratio) for m in ins_masks]
ins_masks = list(filter(lambda x: x is not None, ins_masks))
ins_masks = [m.to(device) for m in ins_masks]
if not return_instances:
return [torch.any(m, dim=0, keepdim=True).to(m.dtype) for m in ins_masks]
return ins_masks
def post_process(
input_array,
attn_masks,
pad=None,
mask_threshold=0.3,
return_largest=False,
min_area_ratio=0.15,
return_instances=False,
):
"""post process the input tensor with the attention masks.
Args:
input_array: A np.ndarray input array to be post processed with shape
[width, height, 3, batch_size]
attn_masks: A torch.Tensor for the attention masks with shape [1,
num_texts, width, height]
pad: A list of padding: [pad_left, pad_top, width, height], where
pad_left, pad_top and width, height are int values.
mask_threshold: The threshold to binarize the mask.
return_largest: If true, return the largest connected component.
min_area_ratio: Keep the mask if its area is larger than this threshold.
return_instances: Whether to return instances or not.
Returns:
attn_masks: A list of tensors with shape [num_instances, height, width]
x num_texts, where len(attn_masks) = num_texts.
NOTE: the number_instances for each text (class) may vary.
The output is a binary tensor.
"""
if len(attn_masks.shape) == 3:
attn_masks = attn_masks[None]
img_width, img_height = input_array.shape[:2]
attn_masks = F.interpolate(
attn_masks, size=(img_height, img_width), mode='bicubic'
).squeeze(0)
device = attn_masks.device
output_masks = filter_masks(
attn_masks,
pad=pad,
mask_threshold=mask_threshold,
min_area_ratio=min_area_ratio,
return_largest=return_largest,
device=device,
return_instances=return_instances,
)
if pad is not None:
left, top, width, height = pad
input_array = input_array[top : top + height, left : left + width]
return input_array, output_masks
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