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import numpy as np
import torch
from PIL import Image
try:
from image_dataset_viz import render_datapoint
except ImportError:
raise ModuleNotFoundError(
"Please install image-dataset-viz via pip install --upgrade git+https://github.com/vfdev-5/ImageDatasetViz.git"
)
def _getvocpallete(num_cls):
n = num_cls
pallete = [0] * (n * 3)
for j in range(0, n):
lab = j
pallete[j * 3 + 0] = 0
pallete[j * 3 + 1] = 0
pallete[j * 3 + 2] = 0
i = 0
while lab > 0:
pallete[j * 3 + 0] |= ((lab >> 0) & 1) << (7 - i)
pallete[j * 3 + 1] |= ((lab >> 1) & 1) << (7 - i)
pallete[j * 3 + 2] |= ((lab >> 2) & 1) << (7 - i)
i = i + 1
lab >>= 3
return pallete
vocpallete = _getvocpallete(256)
def render_mask(mask):
if isinstance(mask, np.ndarray):
mask = Image.fromarray(mask)
mask.putpalette(vocpallete)
mask = mask.convert(mode="RGB")
return mask
def tensor_to_rgb(t):
img = t.cpu().numpy().transpose((1, 2, 0))
return img.astype(np.uint8)
def make_grid(batch_img, batch_mask, img_denormalize_fn, batch_gt_mask=None):
"""Create a grid from batch image and mask as
img1 | img2 | img3 | img4 | ...
i+m1 | i+m2 | i+m3 | i+m4 | ...
mask1 | mask2 | mask3 | mask4 | ...
i+M1 | i+M2 | i+M3 | i+M4 | ...
Mask1 | Mask2 | Mask3 | Mask4 | ...
i+m = image + mask blended with alpha=0.4
- maskN is predicted mask
- MaskN is ground-truth mask if given
Args:
batch_img (torch.Tensor) batch of images of any type
batch_mask (torch.Tensor) batch of masks
img_denormalize_fn (Callable): function to denormalize batch of images
batch_gt_mask (torch.Tensor, optional): batch of ground truth masks.
"""
assert isinstance(batch_img, torch.Tensor) and isinstance(batch_mask, torch.Tensor)
assert len(batch_img) == len(batch_mask)
if batch_gt_mask is not None:
assert isinstance(batch_gt_mask, torch.Tensor)
assert len(batch_mask) == len(batch_gt_mask)
b = batch_img.shape[0]
h, w = batch_img.shape[2:]
le = 3 if batch_gt_mask is None else 3 + 2
out_image = np.zeros((h * le, w * b, 3), dtype="uint8")
for i in range(b):
img = batch_img[i]
mask = batch_mask[i]
img = img_denormalize_fn(img)
img = tensor_to_rgb(img)
mask = mask.cpu().numpy()
mask = render_mask(mask)
out_image[0:h, i * w : (i + 1) * w, :] = img
out_image[1 * h : 2 * h, i * w : (i + 1) * w, :] = render_datapoint(img, mask, blend_alpha=0.4)
out_image[2 * h : 3 * h, i * w : (i + 1) * w, :] = mask
if batch_gt_mask is not None:
gt_mask = batch_gt_mask[i]
gt_mask = gt_mask.cpu().numpy()
gt_mask = render_mask(gt_mask)
out_image[3 * h : 4 * h, i * w : (i + 1) * w, :] = render_datapoint(img, gt_mask, blend_alpha=0.4)
out_image[4 * h : 5 * h, i * w : (i + 1) * w, :] = gt_mask
return out_image
def predictions_gt_images_handler(img_denormalize_fn, n_images=None, another_engine=None, prefix_tag=None):
def wrapper(engine, logger, event_name):
batch = engine.state.batch
output = engine.state.output
x = batch["image"]
y = batch["mask"]
y_pred = output[0]
if y.shape == y_pred.shape and y.ndim == 4:
# Case of y of shape (B, C, H, W)
y = torch.argmax(y, dim=1)
y_pred = torch.argmax(y_pred, dim=1).byte()
if n_images is not None:
x = x[:n_images, ...]
y = y[:n_images, ...]
y_pred = y_pred[:n_images, ...]
grid_pred_gt = make_grid(x, y_pred, img_denormalize_fn, batch_gt_mask=y)
state = engine.state if another_engine is None else another_engine.state
global_step = state.epoch
tag = "predictions_with_gt"
if prefix_tag is not None:
tag = f"{prefix_tag}: {tag} - epoch={global_step}"
logger.writer.add_image(tag=tag, img_tensor=grid_pred_gt, global_step=global_step, dataformats="HWC")
return wrapper
|
