import itertools
import os
import random
import shutil
from math import ceil
from typing import Dict, List
import numpy as np
import prettytable as pt
import torch
import torch.nn as nn
from fast_pytorch_kmeans import KMeans
from pathlib import Path
from scipy.stats import hmean
from sklearn import metrics
from termcolor import cprint
from torchvision.utils import draw_segmentation_masks, make_grid, save_image
import utils.misc as misc
from losses import get_spixel_tgt_map, get_volume_seg_map
from utils.convcrf import convcrf
from utils.crf import DenseCRF
def train(
model: nn.Module,
dataloader,
dataset_title: str,
optimizer_dict: Dict,
criterion,
epoch: int,
writer,
suffix: str,
opt,
):
metric_logger = misc.MetricLogger(writer=writer, suffix=suffix)
cprint("{}-th epoch training on {}".format(epoch, dataset_title), "blue")
model.train()
roc_auc_elements = {
modality: {"map_scores": [], "vol_scores": []}
for modality in itertools.chain(opt.modality, ["ensemble"])
}
roc_auc_elements["labels"] = []
for i, data in metric_logger.log_every(
dataloader, print_freq=opt.print_freq, header=f"[{suffix} {epoch}]"
):
if (opt.debug or opt.wholetest) and i > 50:
break
for modality, optimizer in optimizer_dict.items():
optimizer.zero_grad()
image = data["image"].to(opt.device)
unnormalized_image = data["unnormalized_image"].to(opt.device)
label = data["label"].to(opt.device)
mask = data["mask"].to(opt.device)
spixel = data["spixel"].to(opt.device) if opt.mvc_spixel else None
outputs = model(
image,
seg_size=None
if opt.loss_on_mid_map
else [image.shape[-2], image.shape[-1]],
)
losses = criterion(
outputs,
label,
mask,
epoch=epoch,
max_epoch=opt.epochs,
spixel=spixel,
raw_image=unnormalized_image,
)
total_loss = losses["total_loss"]
total_loss.backward()
for modality in opt.modality:
if opt.grad_clip > 0.0:
grad_norm = nn.utils.clip_grad_norm_(
model.sub_models[modality].parameters(), opt.grad_clip
)
metric_logger.update(**{f"grad_norm/{modality}": grad_norm})
optimizer_dict[modality].step()
# image-level metrices logger
roc_auc_elements["labels"].extend(label.tolist())
for modality in itertools.chain(opt.modality, ["ensemble"]):
roc_auc_elements[modality]["map_scores"].extend(
outputs[modality]["map_pred"].tolist()
)
roc_auc_elements[modality]["vol_scores"].extend(
(outputs[modality]["vol_pred"]).tolist()
)
metric_logger.update(**losses)
image_metrics = update_image_roc_auc_metric(
opt.modality + ["ensemble"], roc_auc_elements, None
)
metric_logger.update(**image_metrics)
metric_logger.write_tensorboard(epoch)
print("Average status:")
print(metric_logger.stat_table())
def bundled_evaluate(
model: nn.Module, dataloaders: Dict, criterion, epoch, writer, suffix, opt
):
metric_logger = misc.MetricLogger(writer=writer, suffix=suffix + "_avg")
for dataset, dataloader in dataloaders.items():
outputs = evaluate(
model,
dataloader,
criterion,
dataset,
epoch,
writer,
suffix + f"_{dataset}",
opt,
)
old_keys = list(outputs.keys())
for k in old_keys:
outputs[k.replace(dataset.upper(), "AVG")] = outputs[k]
for k in old_keys:
del outputs[k]
metric_logger.update(**outputs)
metric_logger.write_tensorboard(epoch)
print("Average status:")
print(metric_logger.stat_table())
return metric_logger.get_meters()
def evaluate(
model: nn.Module,
dataloader,
criterion,
dataset_title: str,
epoch: int,
writer,
suffix: str,
opt,
):
metric_logger = misc.MetricLogger(writer=writer, suffix=suffix)
cprint("{}-th epoch evaluation on {}".format(epoch, dataset_title.upper()), "blue")
model.eval()
if opt.crf_postproc:
postprocess = DenseCRF(
iter_max=opt.crf_iter_max,
pos_w=opt.crf_pos_w,
pos_xy_std=opt.crf_pos_xy_std,
bi_w=opt.crf_bi_w,
bi_xy_std=opt.crf_bi_xy_std,
bi_rgb_std=opt.crf_bi_rgb_std,
)
elif opt.convcrf_postproc:
convcrf_config = convcrf.default_conf
convcrf_config["skip_init_softmax"] = True
convcrf_config["final_softmax"] = True
shape = [opt.convcrf_shape, opt.convcrf_shape]
postprocess = convcrf.GaussCRF(
conf=convcrf_config, shape=shape, nclasses=2, use_gpu=True
).to(opt.device)
figure_path = opt.figure_path + f"_{dataset_title.upper()}"
if opt.save_figure:
if os.path.exists(figure_path):
shutil.rmtree(figure_path)
os.mkdir(figure_path)
cprint("Saving figures to {}".format(figure_path), "blue")
if opt.max_pool_postproc > 1:
max_pool = nn.MaxPool2d(
kernel_size=opt.max_pool_postproc,
stride=1,
padding=(opt.max_pool_postproc - 1) // 2,
).to(opt.device)
else:
max_pool = nn.Identity().to(opt.device)
# used_sliding_prediction = False
roc_auc_elements = {
modality: {"map_scores": [], "vol_scores": []}
for modality in itertools.chain(opt.modality, ["ensemble"])
}
roc_auc_elements["labels"] = []
with torch.no_grad():
for i, data in metric_logger.log_every(
dataloader, print_freq=opt.print_freq, header=f"[{suffix} {epoch}]"
):
if (opt.debug or opt.wholetest) and i > 50:
break
image_size = data["image"].shape[-2:]
label = data["label"]
mask = data["mask"]
if opt.crf_postproc or opt.spixel_postproc or opt.convcrf_postproc:
spixel = data["spixel"].to(opt.device)
if max(image_size) > opt.tile_size and opt.large_image_strategy == "slide":
outputs = sliding_predict(
model, data, opt.tile_size, opt.tile_overlap, opt
)
else:
image = data["image"].to(opt.device)
outputs = model(image, seg_size=image.shape[-2:])
if opt.max_pool_postproc > 1:
for modality in itertools.chain(opt.modality, ["ensemble"]):
outputs[modality]["out_map"] = max_pool(
outputs[modality]["out_map"]
)
# CRF
if opt.crf_postproc:
raw_prob = outputs["ensemble"]["out_map"]
image = data["unnormalized_image"] * 255.0
if opt.crf_downsample > 1:
image = (
torch.nn.functional.interpolate(
image,
size=(
image_size[0] // opt.crf_downsample,
image_size[1] // opt.crf_downsample,
),
mode="bilinear",
align_corners=False,
)
.clamp(0, 255)
.int()
)
image = image.squeeze(0).numpy().astype(np.uint8).transpose(1, 2, 0)
for modality in itertools.chain(opt.modality, ["ensemble"]):
prob = outputs[modality]["out_map"].squeeze(1)
if opt.crf_downsample > 1:
prob = (
torch.nn.functional.interpolate(
prob,
size=(
image_size[0] // opt.crf_downsample,
image_size[1] // opt.crf_downsample,
),
mode="bilinear",
align_corners=False,
)
.clamp(0, 1)
.squeeze(0)
)
prob = torch.cat([prob, 1 - prob], dim=0).detach().cpu().numpy()
prob = postprocess(image, prob)
prob = prob[None, 0, ...]
prob = torch.tensor(prob, device=opt.device).unsqueeze(0)
if opt.crf_downsample > 1:
prob = torch.nn.functional.interpolate(
prob, size=image_size, mode="bilinear", align_corners=False
).clamp(0, 1)
outputs[modality]["out_map"] = prob
outputs[modality]["map_pred"] = (
outputs[modality]["out_map"].max().unsqueeze(0)
)
elif opt.convcrf_postproc:
raw_prob = outputs["ensemble"]["out_map"]
image = data["unnormalized_image"].to(opt.device) * 255.0
image = (
torch.nn.functional.interpolate(
image,
size=(opt.convcrf_shape, opt.convcrf_shape),
mode="bilinear",
align_corners=False,
)
.clamp(0, 255)
.int()
)
for modality in itertools.chain(opt.modality, ["ensemble"]):
prob = outputs[modality]["out_map"]
prob = torch.cat([prob, 1 - prob], dim=1)
prob = torch.nn.functional.interpolate(
prob,
size=(opt.convcrf_shape, opt.convcrf_shape),
mode="bilinear",
align_corners=False,
).clamp(0, 1)
prob = postprocess(unary=prob, img=image)
prob = torch.nn.functional.interpolate(
prob, size=image_size, mode="bilinear", align_corners=False
).clamp(0, 1)
outputs[modality]["out_map"] = prob[:, 0, None, ...]
outputs[modality]["map_pred"] = (
outputs[modality]["out_map"].max().unsqueeze(0)
)
elif opt.spixel_postproc:
raw_prob = outputs["ensemble"]["out_map"]
for modality in itertools.chain(opt.modality, ["ensemble"]):
outputs[modality]["out_map"] = get_spixel_tgt_map(
outputs[modality]["out_map"], spixel
)
# image-level metrices logger
roc_auc_elements["labels"].extend(label.detach().cpu().tolist())
for modality in itertools.chain(opt.modality, ["ensemble"]):
roc_auc_elements[modality]["map_scores"].extend(
outputs[modality]["map_pred"].detach().cpu().tolist()
)
roc_auc_elements[modality]["vol_scores"].extend(
(outputs[modality]["vol_pred"]).detach().cpu().tolist()
)
# generate binary prediction mask
out_map = {
modality: outputs[modality]["out_map"] > opt.mask_threshold
for modality in itertools.chain(opt.modality, ["ensemble"])
}
# only compute pixel-level metrics for manipulated images
if label.item() == 1.0:
for modality in itertools.chain(opt.modality, ["ensemble"]):
pixel_metrics = misc.calculate_pixel_f1(
out_map[modality].float().detach().cpu().numpy().flatten(),
mask.detach().cpu().numpy().flatten(),
suffix=f"/{modality}",
)
metric_logger.update(**pixel_metrics)
# save images, mask, and prediction map
if opt.save_figure:
unnormalized_image = data["unnormalized_image"]
# image_id = data['id'][0].split('.')[0]
image_id = Path(data["id"][0]).stem
save_image(
(
outputs["ensemble"]["out_map"][0, ...] > opt.mask_threshold
).float()
* 255,
os.path.join(figure_path, f"{image_id}_ensemble_map.png"),
)
image_metrics = update_image_roc_auc_metric(
opt.modality + ["ensemble"],
roc_auc_elements,
{
modality: metric_logger.meters[f"pixel_f1/{modality}"].avg
for modality in itertools.chain(opt.modality, ["ensemble"])
},
)
metric_logger.update(**image_metrics)
metric_logger.prepend_subprefix(f"{dataset_title.upper()}_")
metric_logger.write_tensorboard(epoch)
print("Average status:")
print(metric_logger.stat_table())
return metric_logger.get_meters()
def update_image_roc_auc_metric(modalities: List, roc_auc_elements, pixel_f1=None):
result = {}
for modality in modalities:
image_metrics = misc.calculate_img_score(
np.array(roc_auc_elements[modality]["map_scores"]) > 0.5,
(np.array(roc_auc_elements["labels"]) > 0).astype(np.int),
suffix=f"/{modality}",
)
if pixel_f1 is not None:
image_f1 = image_metrics[f"image_f1/{modality}"]
combined_f1 = hmean([image_f1, pixel_f1[modality]])
image_metrics[f"comb_f1/{modality}"] = float(combined_f1)
if 0.0 in roc_auc_elements["labels"] and 1.0 in roc_auc_elements["labels"]:
image_auc = metrics.roc_auc_score(
roc_auc_elements["labels"], roc_auc_elements[modality]["map_scores"]
)
image_metrics[f"image_auc/{modality}"] = image_auc
result.update(image_metrics)
return result
def pad_image(image, target_size):
image_size = image.shape[-2:]
if image_size != target_size:
row_missing = target_size[0] - image_size[0]
col_missing = target_size[1] - image_size[1]
image = nn.functional.pad(
image, (0, row_missing, 0, col_missing), "constant", 0
)
return image
def sliding_predict(model: nn.Module, data, tile_size, tile_overlap, opt):
image = data["image"]
mask = data["mask"]
image = image.to(opt.device)
image_size = image.shape[-2:]
stride = ceil(tile_size * (1 - tile_overlap))
tile_rows = int(ceil((image_size[0] - tile_size) / stride) + 1)
tile_cols = int(ceil((image_size[1] - tile_size) / stride) + 1)
result = {}
for modality in itertools.chain(opt.modality, ["ensemble"]):
result[modality] = {
"out_map": torch.zeros_like(
mask, requires_grad=False, dtype=torch.float32, device=opt.device
),
"out_vol_map": torch.zeros_like(
mask, requires_grad=False, dtype=torch.float32, device=opt.device
),
}
map_counter = torch.zeros_like(
mask, requires_grad=False, dtype=torch.float32, device=opt.device
)
with torch.no_grad():
for row in range(tile_rows):
for col in range(tile_cols):
x1 = int(col * stride)
y1 = int(row * stride)
x2 = min(x1 + tile_size, image_size[1])
y2 = min(y1 + tile_size, image_size[0])
x1 = max(int(x2 - tile_size), 0)
y1 = max(int(y2 - tile_size), 0)
image_tile = image[:, :, y1:y2, x1:x2]
image_tile = pad_image(image_tile, [opt.tile_size, opt.tile_size])
tile_outputs = model(image_tile, seg_size=(image_tile.shape[-2:]))
for modality in itertools.chain(opt.modality, ["ensemble"]):
result[modality]["out_map"][:, :, y1:y2, x1:x2] += tile_outputs[
modality
]["out_map"][:, :, : y2 - y1, : x2 - x1]
out_vol_map = get_volume_seg_map(
tile_outputs[modality]["out_vol"],
size=image_tile.shape[-2:],
label=data["label"],
kmeans=KMeans(2) if opt.consistency_kmeans else None,
)[:, :, : y2 - y1, : x2 - x1]
result[modality]["out_vol_map"][:, :, y1:y2, x1:x2] += out_vol_map
map_counter[:, :, y1:y2, x1:x2] += 1
for modality in itertools.chain(opt.modality, ["ensemble"]):
result[modality]["out_map"] /= map_counter
result[modality]["out_vol_map"] /= map_counter
result[modality]["map_pred"] = (
result[modality]["out_map"].max().unsqueeze(0)
)
result[modality]["vol_pred"] = (
result[modality]["out_vol_map"].max().unsqueeze(0)
)
return result