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sam.py
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sam.py
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import gc
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
from models import torch_device
from transformers import SamModel, SamProcessor
import utils
from utils import vis
import cv2
from scipy import ndimage
def load_sam():
sam_model = SamModel.from_pretrained("facebook/sam-vit-huge").to(torch_device)
sam_processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")
sam_model_dict = dict(sam_model=sam_model, sam_processor=sam_processor)
return sam_model_dict
# Not fully backward compatible with the previous implementation
# Reference: lmdv2/notebooks/gen_masked_latents_multi_object_ref_ca_loss_modular.ipynb
def sam(
sam_model_dict,
image,
input_points=None,
input_boxes=None,
target_mask_shape=None,
return_numpy=True,
):
"""target_mask_shape: (h, w)"""
sam_model, sam_processor = (
sam_model_dict["sam_model"],
sam_model_dict["sam_processor"],
)
if not isinstance(input_boxes, torch.Tensor):
if input_boxes and isinstance(input_boxes[0], tuple):
# Convert tuple to list
input_boxes = [list(input_box) for input_box in input_boxes]
if input_boxes and input_boxes[0] and isinstance(input_boxes[0][0], tuple):
# Convert tuple to list
input_boxes = [
[list(input_box) for input_box in input_boxes_item]
for input_boxes_item in input_boxes
]
with torch.no_grad():
with torch.autocast(torch_device):
inputs = sam_processor(
image,
input_points=input_points,
input_boxes=input_boxes,
return_tensors="pt",
).to(torch_device)
outputs = sam_model(**inputs)
masks = sam_processor.image_processor.post_process_masks(
outputs.pred_masks.cpu().float(),
inputs["original_sizes"].cpu(),
inputs["reshaped_input_sizes"].cpu(),
)
conf_scores = outputs.iou_scores.cpu().numpy()[0, 0]
del inputs, outputs
# Uncomment if experiencing out-of-memory error:
utils.free_memory()
if return_numpy:
masks = [
F.interpolate(
masks_item.type(torch.float), target_mask_shape, mode="bilinear"
)
.type(torch.bool)
.numpy()
for masks_item in masks
]
else:
masks = [
F.interpolate(
masks_item.type(torch.float), target_mask_shape, mode="bilinear"
).type(torch.bool)
for masks_item in masks
]
return masks, conf_scores
def sam_point_input(sam_model_dict, image, input_points, **kwargs):
return sam(sam_model_dict, image, input_points=input_points, **kwargs)
def sam_box_input(sam_model_dict, image, input_boxes, **kwargs):
return sam(sam_model_dict, image, input_boxes=input_boxes, **kwargs)
def get_iou_with_resize(mask, masks, masks_shape):
masks = np.array(
[
cv2.resize(
mask.astype(np.uint8) * 255, masks_shape[::-1], cv2.INTER_LINEAR
).astype(bool)
for mask in masks
]
)
return utils.iou(mask, masks)
def select_mask(
masks,
conf_scores,
coarse_ious=None,
rule="largest_over_conf",
discourage_mask_below_confidence=0.85,
discourage_mask_below_coarse_iou=0.2,
verbose=False,
):
"""masks: numpy bool array"""
mask_sizes = masks.sum(axis=(1, 2))
# Another possible rule: iou with the attention mask
if rule == "largest_over_conf":
# Use the largest segmentation
# Discourage selecting masks with conf too low or coarse iou is too low
max_mask_size = np.max(mask_sizes)
if coarse_ious is not None:
scores = (
mask_sizes
- (conf_scores < discourage_mask_below_confidence) * max_mask_size
- (coarse_ious < discourage_mask_below_coarse_iou) * max_mask_size
)
else:
scores = (
mask_sizes
- (conf_scores < discourage_mask_below_confidence) * max_mask_size
)
if verbose:
print(f"mask_sizes: {mask_sizes}, scores: {scores}")
else:
raise ValueError(f"Unknown rule: {rule}")
mask_id = np.argmax(scores)
mask = masks[mask_id]
selection_conf = conf_scores[mask_id]
if coarse_ious is not None:
selection_coarse_iou = coarse_ious[mask_id]
else:
selection_coarse_iou = None
if verbose:
# print(f"Confidences: {conf_scores}")
print(
f"Selected a mask with confidence: {selection_conf}, coarse_iou: {selection_coarse_iou}"
)
if verbose >= 2:
plt.figure(figsize=(10, 8))
# plt.suptitle("After SAM")
for ind in range(3):
plt.subplot(1, 3, ind + 1)
# This is obtained before resize.
plt.title(
f"Mask {ind}, score {scores[ind]}, conf {conf_scores[ind]:.2f}, iou {coarse_ious[ind] if coarse_ious is not None else None:.2f}"
)
plt.imshow(masks[ind])
plt.tight_layout()
plt.show()
plt.close()
return mask, selection_conf
def preprocess_mask(token_attn_np_smooth, mask_th, n_erode_dilate_mask=0):
token_attn_np_smooth_normalized = token_attn_np_smooth - token_attn_np_smooth.min()
token_attn_np_smooth_normalized /= token_attn_np_smooth_normalized.max()
mask_thresholded = token_attn_np_smooth_normalized > mask_th
if n_erode_dilate_mask:
mask_thresholded = ndimage.binary_erosion(
mask_thresholded, iterations=n_erode_dilate_mask
)
mask_thresholded = ndimage.binary_dilation(
mask_thresholded, iterations=n_erode_dilate_mask
)
return mask_thresholded
# The overall pipeline to refine the attention mask
def sam_refine_attn(
sam_input_image,
token_attn_np,
model_dict,
height,
width,
H,
W,
use_box_input,
gaussian_sigma,
mask_th_for_box,
n_erode_dilate_mask_for_box,
mask_th_for_point,
discourage_mask_below_confidence,
discourage_mask_below_coarse_iou,
verbose,
):
# token_attn_np is for visualizations
token_attn_np_smooth = ndimage.gaussian_filter(
token_attn_np.astype(float), sigma=gaussian_sigma
)
if verbose >= 2:
# Visualize one token only
vis.visualize_arrays(
[
(token_attn_np, f"token_attn_np"),
(token_attn_np_smooth, f"token_attn_np_smooth"),
],
colorbar_index=1,
)
# (w, h)
mask_size_scale = (
height // token_attn_np_smooth.shape[1],
width // token_attn_np_smooth.shape[0],
)
if use_box_input:
# box input
mask_binary = preprocess_mask(
token_attn_np_smooth,
mask_th_for_box,
n_erode_dilate_mask=n_erode_dilate_mask_for_box,
)
input_boxes = utils.binary_mask_to_box(
mask_binary, w_scale=mask_size_scale[0], h_scale=mask_size_scale[1]
)
input_boxes = [input_boxes]
masks, conf_scores = sam_box_input(
model_dict,
image=sam_input_image,
input_boxes=input_boxes,
target_mask_shape=(H, W),
)
else:
# point input
mask_binary = preprocess_mask(
token_attn_np_smooth, mask_th_for_point, n_erode_dilate_mask=0
)
# Uses the max coordinate only
max_coord = np.unravel_index(
token_attn_np_smooth.argmax(), token_attn_np_smooth.shape
)
# print("max_coord:", max_coord)
input_points = [
[[max_coord[1] * mask_size_scale[1], max_coord[0] * mask_size_scale[0]]]
]
masks, conf_scores = sam_point_input(
model_dict,
image=sam_input_image,
input_points=input_points,
target_mask_shape=(H, W),
)
if verbose >= 2:
plt.title("Coarse binary mask (for getting the box with box input and for iou)")
plt.imshow(mask_binary)
plt.show()
# Assuming one image, one three-masks per image (so we have indexing twice)
three_masks = masks[0][0]
coarse_ious = get_iou_with_resize(
mask_binary, three_masks, masks_shape=mask_binary.shape
)
mask_selected, conf_score_selected = select_mask(
three_masks,
conf_scores,
coarse_ious=coarse_ious,
rule="largest_over_conf",
discourage_mask_below_confidence=discourage_mask_below_confidence,
discourage_mask_below_coarse_iou=discourage_mask_below_coarse_iou,
verbose=True,
)
return mask_selected, conf_score_selected
def sam_refine_box(sam_input_image, box, *args, **kwargs):
# One image with one box
sam_input_images, boxes = [sam_input_image], [[box]]
mask_selected_batched_list, conf_score_selected_batched_list = sam_refine_boxes(
sam_input_images, boxes, *args, **kwargs
)
return mask_selected_batched_list[0][0], conf_score_selected_batched_list[0][0]
def sam_refine_boxes(
sam_input_images,
boxes,
model_dict,
height,
width,
H,
W,
discourage_mask_below_confidence,
discourage_mask_below_coarse_iou,
verbose,
):
# (w, h)
input_boxes = [
[utils.scale_proportion(box, H=height, W=width) for box in boxes_item]
for boxes_item in boxes
]
masks, conf_scores = sam_box_input(
model_dict,
image=sam_input_images,
input_boxes=input_boxes,
target_mask_shape=(H, W),
)
mask_selected_batched_list, conf_score_selected_batched_list = [], []
for boxes_item, masks_item in zip(boxes, masks):
mask_selected_list, conf_score_selected_list = [], []
for box, three_masks in zip(boxes_item, masks_item):
mask_binary = utils.proportion_to_mask(box, H, W, return_np=True)
if verbose >= 2:
# Also the box is the input for SAM
plt.title("Binary mask from input box (for iou)")
plt.imshow(mask_binary)
plt.show()
coarse_ious = get_iou_with_resize(
mask_binary, three_masks, masks_shape=mask_binary.shape
)
mask_selected, conf_score_selected = select_mask(
three_masks,
conf_scores,
coarse_ious=coarse_ious,
rule="largest_over_conf",
discourage_mask_below_confidence=discourage_mask_below_confidence,
discourage_mask_below_coarse_iou=discourage_mask_below_coarse_iou,
verbose=False,
)
mask_selected_list.append(mask_selected)
conf_score_selected_list.append(conf_score_selected)
mask_selected_batched_list.append(mask_selected_list)
conf_score_selected_batched_list.append(conf_score_selected_list)
return mask_selected_batched_list, conf_score_selected_batched_list