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attention_visualization.py
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attention_visualization.py
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import os
import tqdm
import wandb
import hydra
import torch
import random
import datetime
import matplotlib
import numpy as np
import pandas as pd
from PIL import Image
from pathlib import Path
from omegaconf import DictConfig
from collections import defaultdict
from source.dataset import SlideFilepathsDataset
from source.utils import initialize_wandb, is_main_process
from source.attention_visualization_utils import (
cmap_map,
get_patch_model,
get_region_model,
get_slide_model,
generate_masks,
create_patch_heatmaps_indiv,
create_patch_heatmaps_concat,
create_region_heatmaps_indiv,
create_region_heatmaps_concat,
get_slide_heatmaps_patch_level,
get_slide_heatmaps_region_level,
get_slide_heatmaps_slide_level,
get_slide_hierarchical_heatmaps_region,
get_slide_blended_heatmaps,
stitch_slide_heatmaps,
display_stitched_heatmaps,
)
@hydra.main(version_base="1.2.0", config_path="config/heatmaps", config_name="ecp_masked_attn")
def main(cfg: DictConfig):
distributed = torch.cuda.device_count() > 1
if distributed:
torch.distributed.init_process_group(backend="nccl")
gpu_id = int(os.environ["LOCAL_RANK"])
if gpu_id == 0:
print(f"Distributed session successfully initialized")
else:
gpu_id = -1
if is_main_process():
print(f"torch.cuda.device_count(): {torch.cuda.device_count()}")
run_id = datetime.datetime.now().strftime("%Y-%m-%d_%H_%M")
# set up wandb
if cfg.wandb.enable:
key = os.environ.get("WANDB_API_KEY")
wandb_run = initialize_wandb(cfg, key=key)
wandb_run.define_metric("processed", summary="max")
run_id = wandb_run.id
else:
run_id = ""
if distributed:
obj = [run_id]
torch.distributed.broadcast_object_list(
obj, 0, device=torch.device(f"cuda:{gpu_id}")
)
run_id = obj[0]
output_dir = Path(cfg.output_dir, cfg.experiment_name, run_id)
if is_main_process():
output_dir.mkdir(parents=True, exist_ok=True)
mask_attention = (cfg.mask_attn_patch is True) or (mask_attn_region is True)
# seed everything to ensure reproducible heatmaps
seed = 0
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if gpu_id == -1:
device = torch.device(f"cuda")
else:
device = torch.device(f"cuda:{gpu_id}")
patch_weights = Path(cfg.patch_weights)
patch_model = get_patch_model(
pretrained_weights=patch_weights,
mask_attn=cfg.mask_attn_patch,
device=device,
)
region_weights = Path(cfg.region_weights)
region_model = get_region_model(
pretrained_weights=region_weights,
region_size=cfg.region_size,
mask_attn=cfg.mask_attn_region,
img_size_pretrained=cfg.img_size_pretrained,
device=device,
)
if cfg.slide_weights:
slide_weights = Path(cfg.slide_weights)
sd = torch.load(slide_weights, map_location="cpu")
start_idx = list(sd.keys()).index("global_phi.0.weight")
end_idx = list(sd.keys()).index("global_rho.0.bias")
sd = {
k: v
for i, (k, v) in enumerate(sd.items())
if i >= start_idx and i <= end_idx
}
print(f"Pretrained weights found at {slide_weights}")
slide_model = get_slide_model(sd, device=device)
light_jet = cmap_map(lambda x: x / 2 + 0.5, matplotlib.cm.jet)
if cfg.patch_fp and is_main_process():
patch = Image.open(cfg.patch_fp)
print(f"Computing indiviudal patch-level attention heatmaps")
output_dir_patch = Path(output_dir, "patch_indiv")
output_dir_patch.mkdir(exist_ok=True, parents=True)
create_patch_heatmaps_indiv(
patch,
patch_model,
output_dir_patch,
threshold=0.5,
alpha=0.5,
cmap=light_jet,
granular=cfg.smoothing.patch,
offset=cfg.smoothing.offset.patch,
downscale=cfg.downscale,
patch_device=device,
)
print("done!")
print(f"Computing concatenated patch-level attention heatmaps")
output_dir_patch_concat = Path(output_dir, "patch_concat")
output_dir_patch_concat.mkdir(exist_ok=True, parents=True)
create_patch_heatmaps_concat(
patch,
patch_model,
output_dir_patch_concat,
threshold=0.5,
alpha=0.5,
cmap=light_jet,
granular=cfg.smoothing.patch,
offset=cfg.smoothing.offset.patch,
downscale=cfg.downscale,
patch_device=device,
)
print("done!")
if cfg.region_fp and is_main_process():
region = Image.open(cfg.region_fp)
print(f"Computing individual region-level attention heatmaps")
output_dir_region = Path(output_dir, "region_indiv")
output_dir_region.mkdir(exist_ok=True, parents=True)
create_region_heatmaps_indiv(
region,
patch_model,
region_model,
output_dir_region,
downscale=cfg.downscale,
threshold=0.5,
alpha=0.5,
cmap=light_jet,
granular=cfg.smoothing.region,
offset=cfg.smoothing.offset.region,
patch_device=device,
region_device=device,
)
print("done!")
print(f"Computing concatenated region-level attention heatmaps")
output_dir_region_concat = Path(output_dir, "region_concat")
output_dir_region_concat.mkdir(exist_ok=True, parents=True)
create_region_heatmaps_concat(
region,
patch_model,
region_model,
output_dir_region_concat,
downscale=cfg.downscale,
alpha=0.5,
cmap=light_jet,
granular=cfg.smoothing.region,
offset=cfg.smoothing.offset.region,
patch_device=device,
region_device=device,
)
print("done!")
if cfg.slide_fp and is_main_process():
slide_path = Path(cfg.slide_fp)
slide_id = slide_path.stem
region_dir = Path(cfg.region_dir)
output_dir_slide = Path(output_dir, slide_id)
mask_p, mask_mp = None, None
if mask_attention:
mask_p, mask_mp = generate_masks(
slide_id,
slide_path,
cfg.segmentation_mask_fp,
region_dir,
region_size=cfg.region_size,
spacing=cfg.spacing,
downsample=1,
tissue_pixel_value=cfg.tissue_pixel_value,
)
########################
# PATCH-LEVEL HEATMAPS #
########################
hms, hms_thresh, hms_highlight, coords = get_slide_heatmaps_patch_level(
slide_id,
region_dir,
patch_model,
region_model,
output_dir_slide,
downscale=cfg.downscale,
cmap=light_jet,
threshold=cfg.threshold,
highlight=cfg.highlight,
save_to_disk=False,
granular=cfg.smoothing.patch,
offset=cfg.smoothing.offset.patch,
slide_path=slide_path,
segmentation_mask_path=cfg.segmentation_mask_fp,
spacing=cfg.spacing,
downsample=1,
background_pixel_value=cfg.background_pixel_value,
tissue_pixel_value=cfg.tissue_pixel_value,
patch_attn_mask=mask_p,
mini_patch_attn_mask=mask_mp,
patch_device=device,
region_device=device,
)
stitched_hms = {}
for head_num, heatmaps in hms.items():
stitched_hm = stitch_slide_heatmaps(
slide_path,
heatmaps,
coords[head_num],
output_dir_slide,
fname=f"patch_head_{head_num}",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=True,
restrict_to_tissue=cfg.restrict_to_tissue,
segmentation_mask_path=cfg.segmentation_mask_fp,
tissue_pixel_value=cfg.tissue_pixel_value,
)
stitched_hms[f"Head {head_num}"] = stitched_hm
stitched_hms_thresh = {}
if len(hms_thresh) > 0:
for head_num, heatmaps in hms_thresh.items():
stitched_hm = stitch_slide_heatmaps(
slide_path,
heatmaps,
coords[head_num],
output_dir_slide,
fname=f"patch_head_{head_num}_thresh",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=True,
)
stitched_hms_thresh[f"Head {head_num}"] = stitched_hm
stitched_hms_highlight = {}
if len(hms_highlight) > 0:
for head_num, heatmaps in hms_highlight.items():
stitched_hm = stitch_slide_heatmaps(
slide_path,
heatmaps,
coords[head_num],
output_dir_slide,
fname=f"patch_head_{head_num}_highlight",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=True,
highlight=(cfg.highlight != None),
opacity=cfg.opacity,
restrict_to_tissue=cfg.restrict_to_tissue,
seg_params=cfg.seg_params,
)
stitched_hms_highlight[f"Head {head_num}"] = stitched_hm
if cfg.display:
display_stitched_heatmaps(
slide_path,
stitched_hms,
output_dir_slide,
fname=f"patch",
display_patching=True,
cmap=light_jet,
region_dir=region_dir,
region_size=cfg.region_size,
downsample=cfg.downsample,
font_fp=cfg.font_fp,
run_id=run_id,
)
if len(stitched_hms_thresh) > 0:
display_stitched_heatmaps(
slide_path,
stitched_hms_thresh,
output_dir_slide,
fname=f"patch_thresh",
display_patching=True,
cmap=light_jet,
region_dir=region_dir,
region_size=cfg.region_size,
downsample=cfg.downsample,
font_fp=cfg.font_fp,
run_id=run_id,
)
if len(stitched_hms_highlight) > 0:
display_stitched_heatmaps(
slide_path,
stitched_hms_highlight,
output_dir_slide,
fname=f"patch_highlight",
display_patching=True,
cmap=light_jet,
region_dir=region_dir,
region_size=cfg.region_size,
downsample=cfg.downsample,
font_fp=cfg.font_fp,
run_id=run_id,
)
#########################
# REGION-LEVEL HEATMAPS #
#########################
hms, hms_thresh, hms_highlight, coords = get_slide_heatmaps_region_level(
slide_id,
region_dir,
patch_model,
region_model,
output_dir_slide,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=False,
threshold=cfg.threshold,
highlight=cfg.highlight,
granular=cfg.smoothing.region,
offset=cfg.smoothing.offset.region,
gaussian_smoothing=cfg.gaussian_smoothing,
slide_path=slide_path,
segmentation_mask_path=cfg.segmentation_mask_fp,
spacing=cfg.spacing,
downsample=1,
background_pixel_value=cfg.background_pixel_value,
tissue_pixel_value=cfg.tissue_pixel_value,
patch_attn_mask=mask_p,
mini_patch_attn_mask=mask_mp,
patch_device=device,
region_device=device,
)
stitched_hms = {}
for head_num, heatmaps in hms.items():
stitched_hm = stitch_slide_heatmaps(
slide_path,
heatmaps,
coords[head_num],
output_dir_slide,
fname=f"region_head_{head_num}",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=True,
restrict_to_tissue=cfg.restrict_to_tissue,
segmentation_mask_path=cfg.segmentation_mask_fp,
tissue_pixel_value=cfg.tissue_pixel_value,
)
stitched_hms[f"Head {head_num}"] = stitched_hm
stitched_hms_thresh = {}
if len(hms_thresh) > 0:
for head_num, heatmaps in hms_thresh.items():
stitched_hm = stitch_slide_heatmaps(
slide_path,
heatmaps,
coords[head_num],
output_dir_slide,
fname=f"region_head_{head_num}_thresh",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=True,
)
stitched_hms_thresh[f"Head {head_num}"] = stitched_hm
stitched_hms_highlight = {}
if len(hms_highlight) > 0:
for head_num, heatmaps in hms_highlight.items():
stitched_hm = stitch_slide_heatmaps(
slide_path,
heatmaps,
coords[head_num],
output_dir_slide,
fname=f"region_head_{head_num}_highlight",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=True,
highlight=(cfg.highlight != None),
opacity=cfg.opacity,
restrict_to_tissue=cfg.restrict_to_tissue,
seg_params=cfg.seg_params,
)
stitched_hms_highlight[f"Head {head_num}"] = stitched_hm
if cfg.display:
display_stitched_heatmaps(
slide_path,
stitched_hms,
output_dir_slide,
fname=f"region",
display_patching=True,
cmap=light_jet,
region_dir=region_dir,
region_size=cfg.region_size,
downsample=cfg.downsample,
font_fp=cfg.font_fp,
run_id=run_id,
)
if len(stitched_hms_thresh) > 0:
display_stitched_heatmaps(
slide_path,
stitched_hms_thresh,
output_dir_slide,
fname=f"region_thresh",
display_patching=True,
cmap=light_jet,
region_dir=region_dir,
region_size=cfg.region_size,
downsample=cfg.downsample,
font_fp=cfg.font_fp,
run_id=run_id,
)
if len(stitched_hms_highlight) > 0:
display_stitched_heatmaps(
slide_path,
stitched_hms_highlight,
output_dir_slide,
fname=f"region_highlight",
display_patching=True,
cmap=light_jet,
region_dir=region_dir,
region_size=cfg.region_size,
downsample=cfg.downsample,
font_fp=cfg.font_fp,
run_id=run_id,
)
##################################
# HIERARCHICAL HEATMAPS (REGION) #
##################################
# hms, hms_thresh, coords = get_slide_hierarchical_heatmaps_region(
# slide_id,
# patch_model,
# region_model,
# region_dir,
# output_dir_slide,
# downscale=cfg.downscale,
# cmap=light_jet,
# threshold=None,
# save_to_disk=True,
# granular=cfg.smoothing.region,
# offset=cfg.smoothing.offset.region,
# patch_device=device,
# region_device=device,
# )
# stitched_hms = defaultdict(list)
# for rhead_num, hm_dict in hms.items():
# coords_dict = coords[rhead_num]
# for phead_num, heatmaps in hm_dict.items():
# coordinates = coords_dict[phead_num]
# stitched_hm = stitch_slide_heatmaps(
# slide_path,
# heatmaps,
# coordinates,
# output_dir_slide,
# fname=f"rhead_{rhead_num}_phead_{phead_num}",
# spacing=cfg.spacing,
# downsample=cfg.downsample,
# downscale=cfg.downscale,
# save_to_disk=True,
# )
# stitched_hms[rhead_num].append(stitched_hm)
# stitched_hms_thresh = defaultdict(list)
# for rhead_num, hm_dict in hms_thresh.items():
# coords_dict = coords[rhead_num]
# for phead_num, heatmaps in hm_dict.items():
# coordinates = coords_dict[phead_num]
# stitched_hm = stitch_slide_heatmaps(
# slide_path,
# heatmaps,
# coordinates,
# output_dir_slide,
# fname=f"rhead_{rhead_num}_phead_{phead_num}_thresh",
# spacing=cfg.spacing,
# downsample=cfg.downsample,
# downscale=cfg.downscale,
# save_to_disk=True,
# )
# stitched_hms_thresh[rhead_num].append(stitched_hm)
# if cfg.display:
# for rhead_num, hms in stitched_hms.items():
# d = {
# f"Region Head {rhead_num} & Patch Head {phead}": hm
# for phead, hm in enumerate(hms)
# }
# display_stitched_heatmaps(
# slide_path,
# d,
# output_dir_slide,
# fname=f"rhead_{rhead_num}",
# display_patching=True,
# cmap=light_jet,
# region_dir=region_dir,
# region_size=cfg.region_size,
# downsample=cfg.downsample,
# font_fp=cfg.font_fp,
# run_id=run_id,
# )
if cfg.slide_weights:
########################
# SLIDE-LEVEL HEATMAPS #
########################
hms, thresh_hms, highlight_hms, coords = get_slide_heatmaps_slide_level(
slide_id,
region_dir,
patch_model,
region_model,
slide_model,
patch_size=256,
downscale=cfg.downscale,
threshold=cfg.threshold,
highlight=cfg.highlight,
cmap=light_jet,
region_fmt="jpg",
granular=cfg.smoothing.slide,
offset=cfg.smoothing.offset.slide,
slide_path=slide_path,
spacing=cfg.spacing,
gaussian_smoothing=cfg.gaussian_smoothing,
gaussian_offset=cfg.gaussian_offset,
patch_attn_mask=mask_p,
mini_patch_attn_mask=mask_mp,
patch_device=device,
region_device=device,
slide_device=device,
)
stitched_hms = {}
stitched_hm = stitch_slide_heatmaps(
slide_path,
hms,
coords,
output_dir_slide,
fname=f"wsi",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=True,
restrict_to_tissue=False,
segmentation_mask_path=cfg.segmentation_mask_fp,
tissue_pixel_value=cfg.tissue_pixel_value,
)
stitched_hms[f"slide-level"] = stitched_hm
if len(thresh_hms) > 0:
stitched_hm = stitch_slide_heatmaps(
slide_path,
thresh_hms,
coords,
output_dir_slide,
fname=f"wsi_thresh",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
save_to_disk=True,
)
stitched_hms[f"thresholded"] = stitched_hm
if len(highlight_hms) > 0:
stitched_hm = stitch_slide_heatmaps(
slide_path,
highlight_hms,
coords,
output_dir_slide,
fname=f"wsi_highlight",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
save_to_disk=True,
highlight=(cfg.highlight != None),
opacity=cfg.opacity,
restrict_to_tissue=cfg.restrict_to_tissue,
seg_params=cfg.seg_params,
)
stitched_hms[f"highlight"] = stitched_hm
if cfg.display:
display_stitched_heatmaps(
slide_path,
stitched_hms,
output_dir_slide,
fname=f"wsi",
display_patching=True,
draw_grid=False,
cmap=light_jet,
region_dir=region_dir,
region_size=cfg.region_size,
downsample=cfg.downsample,
font_fp=cfg.font_fp,
run_id=run_id,
)
####################
# BLENDED HEATMAPS #
####################
hms, thresh_hms, coords = get_slide_blended_heatmaps(
slide_id,
region_dir,
patch_model,
region_model,
slide_model,
cfg.level,
output_dir_slide,
gamma=cfg.gamma,
patch_size=256,
mini_patch_size=16,
downscale=cfg.downscale,
threshold=cfg.threshold,
cmap=light_jet,
region_fmt="jpg",
save_to_disk=False,
smoothing=cfg.smoothing,
slide_path=slide_path,
segmentation_mask_path=cfg.segmentation_mask_fp,
spacing=cfg.spacing,
downsample=1,
background_pixel_value=cfg.background_pixel_value,
tissue_pixel_value=cfg.tissue_pixel_value,
patch_attn_mask=mask_p,
mini_patch_attn_mask=mask_mp,
patch_device=device,
region_device=device,
slide_device=device,
)
stitched_hms = defaultdict(list)
for rhead_num in range(region_model.num_heads):
for phead_num in range(patch_model.num_heads):
heatmaps = hms[(rhead_num,phead_num)]
coordinates = coords[(rhead_num,phead_num)]
stitched_hm = stitch_slide_heatmaps(
slide_path,
heatmaps,
coordinates,
output_dir_slide,
fname=f"rhead_{rhead_num}_phead_{phead_num}",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=True,
restrict_to_tissue=cfg.restrict_to_tissue,
segmentation_mask_path=cfg.segmentation_mask_fp,
tissue_pixel_value=cfg.tissue_pixel_value,
)
stitched_hms[rhead_num].append(stitched_hm)
if len(thresh_hms) > 0:
stitched_hms_thresh = defaultdict(list)
for rhead_num in range(region_model.num_heads):
for phead_num in range(patch_model.num_heads):
heatmaps = thresh_hms[(rhead_num,phead_num)]
coordinates = coords[(rhead_num,phead_num)]
stitched_hm = stitch_slide_heatmaps(
slide_path,
heatmaps,
coordinates,
output_dir_slide,
fname=f"rhead_{rhead_num}_phead_{phead_num}_thresh",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=True,
)
stitched_hms_thresh[rhead_num].append(stitched_hm)
if cfg.display:
for rhead_num, hms in stitched_hms.items():
d = {
f"Region Head {rhead_num} & Patch Head {phead}": hm
for phead, hm in enumerate(hms)
}
display_stitched_heatmaps(
slide_path,
d,
output_dir_slide,
fname=f"rhead_{rhead_num}",
display_patching=True,
cmap=light_jet,
region_dir=region_dir,
region_size=cfg.region_size,
downsample=cfg.downsample,
font_fp=cfg.font_fp,
run_id=run_id,
)
if cfg.slide_csv:
df = pd.read_csv(cfg.slide_csv)
dataset = SlideFilepathsDataset(df)
if distributed:
sampler = torch.utils.data.DistributedSampler(dataset, shuffle=True)
else:
sampler = torch.utils.data.RandomSampler(dataset)
loader = torch.utils.data.DataLoader(
dataset,
sampler=sampler,
batch_size=1,
num_workers=cfg.num_workers,
shuffle=False,
drop_last=False,
)
with tqdm.tqdm(
loader,
desc="Attention Heatmap Generation",
unit=" slide",
ncols=80,
position=0,
leave=True,
disable=not is_main_process(),
) as t1:
for i, batch in enumerate(t1):
_, fp = batch
slide_path = Path(fp[0])
slide_id = slide_path.stem
region_dir = Path(cfg.region_dir)
output_dir_slide = Path(output_dir, slide_id)
########################
# PATCH-LEVEL HEATMAPS #
########################
hms, hms_thresh, hms_highlight, coords = get_slide_heatmaps_patch_level(
slide_id,
region_dir,
patch_model,
region_model,
output_dir_slide,
downscale=cfg.downscale,
cmap=light_jet,
threshold=cfg.threshold,
highlight=cfg.highlight,
save_to_disk=False,
granular=cfg.smoothing.patch,
offset=cfg.smoothing.offset.patch,
patch_device=device,
region_device=device,
main_process=is_main_process(),
)
stitched_hms = {}
for head_num, heatmaps in hms.items():
stitched_hm = stitch_slide_heatmaps(
slide_path,
heatmaps,
coords[head_num],
output_dir_slide,
fname=f"patch_head_{head_num}",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=True,
)
stitched_hms[f"Head {head_num}"] = stitched_hm
stitched_hms_thresh = {}
if len(hms_thresh) > 0:
for head_num, heatmaps in hms_thresh.items():
stitched_hm = stitch_slide_heatmaps(
slide_path,
heatmaps,
coords[head_num],
output_dir_slide,
fname=f"region_head_{head_num}_thresh",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=True,
)
stitched_hms_thresh[f"Head {head_num}"] = stitched_hm
stitched_hms_highlight = {}
if len(hms_highlight) > 0:
for head_num, heatmaps in hms_highlight.items():
stitched_hm = stitch_slide_heatmaps(
slide_path,
heatmaps,
coords[head_num],
output_dir_slide,
fname=f"patch_head_{head_num}_highlight",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=True,
highlight=(cfg.highlight != None),
opacity=cfg.opacity,
restrict_to_tissue=cfg.restrict_to_tissue,
seg_params=cfg.seg_params,
)
stitched_hms_highlight[f"Head {head_num}"] = stitched_hm
if cfg.display:
display_stitched_heatmaps(
slide_path,
stitched_hms,
output_dir_slide,
fname=f"patch",
display_patching=True,
cmap=light_jet,
region_dir=region_dir,
region_size=cfg.region_size,
downsample=cfg.downsample,
font_fp=cfg.font_fp,
)
if len(stitched_hms_thresh) > 0:
display_stitched_heatmaps(
slide_path,
stitched_hms_thresh,
output_dir_slide,
fname=f"patch_thresh",
display_patching=True,
cmap=light_jet,
region_dir=region_dir,
region_size=cfg.region_size,
downsample=cfg.downsample,
font_fp=cfg.font_fp,
)
if len(stitched_hms_highlight) > 0:
display_stitched_heatmaps(
slide_path,
stitched_hms_highlight,
output_dir_slide,
fname=f"patch_highlight",
display_patching=True,
cmap=light_jet,
region_dir=region_dir,
region_size=cfg.region_size,
downsample=cfg.downsample,
font_fp=cfg.font_fp,
)
#########################
# REGION-LEVEL HEATMAPS #
#########################
hms, hms_thresh, hms_highlight, coords = get_slide_heatmaps_region_level(
slide_id,
region_dir,
patch_model,
region_model,
output_dir_slide,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=False,
threshold=cfg.threshold,
highlight=cfg.highlight,
granular=cfg.smoothing.region,
offset=cfg.smoothing.offset.region,
gaussian_smoothing=cfg.gaussian_smoothing,
patch_device=device,
region_device=device,
main_process=is_main_process(),
)
stitched_hms = {}
for head_num, heatmaps in hms.items():
stitched_hm = stitch_slide_heatmaps(
slide_path,
heatmaps,
coords[head_num],
output_dir_slide,
fname=f"region_head_{head_num}",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=True,
)
stitched_hms[f"Head {head_num}"] = stitched_hm
stitched_hms_thresh = {}
if len(hms_thresh) > 0:
for head_num, heatmaps in hms_thresh.items():
stitched_hm = stitch_slide_heatmaps(
slide_path,
heatmaps,
coords[head_num],
output_dir_slide,
fname=f"region_head_{head_num}_thresh",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=True,
)
stitched_hms_thresh[f"Head {head_num}"] = stitched_hm
stitched_hms_highlight = {}
if len(hms_highlight) > 0:
for head_num, heatmaps in hms_highlight.items():
stitched_hm = stitch_slide_heatmaps(
slide_path,
heatmaps,
coords[head_num],
output_dir_slide,
fname=f"region_head_{head_num}_highlight",
spacing=cfg.spacing,
downsample=cfg.downsample,
downscale=cfg.downscale,
cmap=light_jet,
save_to_disk=True,
highlight=(cfg.highlight != None),
opacity=cfg.opacity,
restrict_to_tissue=cfg.restrict_to_tissue,
seg_params=cfg.seg_params,
)
stitched_hms_highlight[f"Head {head_num}"] = stitched_hm
if cfg.display:
display_stitched_heatmaps(
slide_path,
stitched_hms,
output_dir_slide,
fname=f"region",
display_patching=True,
cmap=light_jet,
region_dir=region_dir,
region_size=cfg.region_size,
downsample=cfg.downsample,
font_fp=cfg.font_fp,
)
if len(stitched_hms_thresh) > 0:
display_stitched_heatmaps(
slide_path,
stitched_hms_thresh,
output_dir_slide,
fname=f"region_thresh",
display_patching=True,
cmap=light_jet,
region_dir=region_dir,