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# Copyright (c) MONAI Consortium
# 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.
import logging
from typing import TYPE_CHECKING, Any, Callable, List, Optional
import numpy as np
import torch
import torch.distributed
from monai.config import IgniteInfo
from monai.utils import min_version, optional_import
from sklearn.metrics import classification_report
Events, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Events")
make_grid, _ = optional_import("torchvision.utils", name="make_grid")
Image, _ = optional_import("PIL.Image")
ImageDraw, _ = optional_import("PIL.ImageDraw")
if TYPE_CHECKING:
from ignite.engine import Engine
from torch.utils.tensorboard import SummaryWriter
else:
Engine, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Engine")
SummaryWriter, _ = optional_import("torch.utils.tensorboard", name="SummaryWriter")
class TensorBoardImageHandler:
def __init__(
self,
class_names,
summary_writer: Optional[SummaryWriter] = None,
log_dir: str = "./runs",
tag_name="val",
interval: int = 1,
batch_transform: Callable = lambda x: x,
output_transform: Callable = lambda x: x,
batch_limit=1,
device=None,
) -> None:
self.class_names = class_names
self.writer = SummaryWriter(log_dir=log_dir) if summary_writer is None else summary_writer
self.tag_name = tag_name
self.interval = interval
self.batch_transform = batch_transform
self.output_transform = output_transform
self.batch_limit = batch_limit
self.device = device
self.logger = logging.getLogger(__name__)
if torch.distributed.is_initialized():
self.tag_name = f"{self.tag_name}-r{torch.distributed.get_rank()}"
self.class_y: List[Any] = []
self.class_y_pred: List[Any] = []
def attach(self, engine: Engine) -> None:
if self.interval == 1:
engine.add_event_handler(Events.ITERATION_COMPLETED(every=self.interval), self, "iteration")
engine.add_event_handler(Events.EPOCH_COMPLETED(every=self.interval), self, "epoch")
def __call__(self, engine: Engine, action) -> None:
epoch = engine.state.epoch
batch_data = self.batch_transform(engine.state.batch)
output_data = self.output_transform(engine.state.output)
if action == "iteration":
for bidx in range(len(batch_data)):
y = output_data[bidx]["label"].detach().cpu().numpy()
y_pred = output_data[bidx]["pred"].detach().cpu().numpy()
self.class_y.append(np.argmax(y))
self.class_y_pred.append(np.argmax(y_pred))
return
self.write_metrics(epoch)
self.write_images(batch_data, output_data, epoch)
self.writer.flush()
def write_images(self, batch_data, output_data, epoch):
for bidx in range(len(batch_data)):
image = batch_data[bidx]["image"].detach().cpu().numpy()
y = output_data[bidx]["label"].detach().cpu().numpy()
y_pred = output_data[bidx]["pred"].detach().cpu().numpy()
sig_np = image[:3] * 128 + 128
sig_np[0, :, :] = np.where(image[3] > 0, 1, sig_np[0, :, :])
y_c = np.argmax(y)
y_pred_c = np.argmax(y_pred)
tag_prefix = f"{self.tag_name} - b{bidx} - " if self.batch_limit != 1 else f"{self.tag_name} - "
label_pred_tag = f"{tag_prefix}Image/Signal/Label/Pred:"
y_img = Image.new("RGB", image.shape[-2:])
draw = ImageDraw.Draw(y_img)
draw.text((10, 50), self.class_names.get(f"{y_c}", f"{y_c}"))
y_pred_img = Image.new("RGB", image.shape[-2:], "green" if y_c == y_pred_c else "red")
draw = ImageDraw.Draw(y_pred_img)
draw.text((10, 50), self.class_names.get(f"{y_pred_c}", f"{y_pred_c}"))
img_tensor = make_grid(
tensor=[
torch.from_numpy(sig_np),
torch.from_numpy(np.stack((np.where(image[3] > 0, 255, 0),) * 3)),
torch.from_numpy(np.moveaxis(np.array(y_img), -1, 0)),
torch.from_numpy(np.moveaxis(np.array(y_pred_img), -1, 0)),
],
nrow=4,
normalize=True,
pad_value=10,
)
self.writer.add_image(tag=label_pred_tag, img_tensor=img_tensor, global_step=epoch)
if self.batch_limit == 1 or bidx == (self.batch_limit - 1):
break
def write_metrics(self, epoch):
cr = classification_report(self.class_y, self.class_y_pred, output_dict=True, zero_division=0)
for k, v in cr.items():
if isinstance(v, dict):
ltext = []
cname = self.class_names.get(k, k)
for n, m in v.items():
ltext.append(f"{n} => {m:.4f}")
self.writer.add_scalar(f"{self.tag_name}_cr_{cname}_{n}", m, epoch)
self.logger.info(f"{self.tag_name} => Epoch[{epoch}] - {cname} - Metrics -- {'; '.join(ltext)}")
else:
self.logger.info(f"{self.tag_name} => Epoch[{epoch}] Metrics -- {k} => {v:.4f}")
self.writer.add_scalar(f"{self.tag_name}_cr_{k}", v, epoch)
self.class_y = []
self.class_y_pred = []
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