File size: 7,163 Bytes
6dd4c34 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 | """
Test-time augmentation (D4 dihedral group) and model ensemble averaging.
D4 TTA: 4 rotations x 2 reflections = 8 geometric views
+ 2 intensity variants = 10 total forward passes.
Gold beads are rotationally invariant — D4 TTA is maximally effective.
Expected F1 gain: +1-3% over single forward pass.
"""
import numpy as np
import torch
import torch.nn.functional as F
from typing import List, Optional
from src.model import ImmunogoldCenterNet
def d4_tta_predict(
model: ImmunogoldCenterNet,
image: np.ndarray,
device: torch.device = torch.device("cpu"),
) -> tuple:
"""
Test-time augmentation over D4 dihedral group + intensity variants.
Args:
model: trained CenterNet model
image: (H, W) uint8 preprocessed image
device: torch device
Returns:
averaged_heatmap: (2, H/2, W/2) numpy array
averaged_offsets: (2, H/2, W/2) numpy array
"""
model.eval()
heatmaps = []
offsets_list = []
# Ensure image dimensions are divisible by 32 for the encoder
h, w = image.shape[:2]
pad_h = (32 - h % 32) % 32
pad_w = (32 - w % 32) % 32
def _forward(img_np):
"""Run model on numpy image, return heatmap and offsets."""
# Pad to multiple of 32
if pad_h > 0 or pad_w > 0:
img_np = np.pad(img_np, ((0, pad_h), (0, pad_w)), mode="reflect")
tensor = (
torch.from_numpy(img_np)
.float()
.unsqueeze(0)
.unsqueeze(0) # (1, 1, H, W)
/ 255.0
).to(device)
with torch.no_grad():
hm, off = model(tensor)
hm = hm.squeeze(0).cpu().numpy() # (2, H/2, W/2)
off = off.squeeze(0).cpu().numpy() # (2, H/2, W/2)
# Remove padding from output
hm_h = h // 2
hm_w = w // 2
return hm[:, :hm_h, :hm_w], off[:, :hm_h, :hm_w]
# D4 group: 4 rotations x 2 reflections = 8 geometric views
for k in range(4):
for flip in [False, True]:
aug = np.rot90(image, k).copy()
if flip:
aug = np.fliplr(aug).copy()
hm, off = _forward(aug)
# Inverse transforms on heatmap and offsets
if flip:
hm = np.flip(hm, axis=2).copy() # flip W axis
off = np.flip(off, axis=2).copy()
off[0] = -off[0] # negate x offset for horizontal flip
if k > 0:
hm = np.rot90(hm, -k, axes=(1, 2)).copy()
off = np.rot90(off, -k, axes=(1, 2)).copy()
# Rotate offset vectors
if k == 1: # 90° CCW undo
off = np.stack([-off[1], off[0]], axis=0)
elif k == 2: # 180°
off = np.stack([-off[0], -off[1]], axis=0)
elif k == 3: # 270° CCW undo
off = np.stack([off[1], -off[0]], axis=0)
heatmaps.append(hm)
offsets_list.append(off)
# 2 intensity variants
for factor in [0.9, 1.1]:
aug = np.clip(image.astype(np.float32) * factor, 0, 255).astype(np.uint8)
hm, off = _forward(aug)
heatmaps.append(hm)
offsets_list.append(off)
# Average all views
avg_heatmap = np.mean(heatmaps, axis=0)
avg_offsets = np.mean(offsets_list, axis=0)
return avg_heatmap, avg_offsets
def ensemble_predict(
models: List[ImmunogoldCenterNet],
image: np.ndarray,
device: torch.device = torch.device("cpu"),
use_tta: bool = True,
) -> tuple:
"""
Ensemble prediction: average heatmaps from N models.
Args:
models: list of trained models (e.g., 5 seeds x 3 snapshots = 15)
image: (H, W) uint8 preprocessed image
device: torch device
use_tta: whether to apply D4 TTA per model
Returns:
averaged_heatmap: (2, H/2, W/2) numpy array
averaged_offsets: (2, H/2, W/2) numpy array
"""
all_heatmaps = []
all_offsets = []
for model in models:
model.eval()
model.to(device)
if use_tta:
hm, off = d4_tta_predict(model, image, device)
else:
h, w = image.shape[:2]
pad_h = (32 - h % 32) % 32
pad_w = (32 - w % 32) % 32
img_padded = np.pad(image, ((0, pad_h), (0, pad_w)), mode="reflect")
tensor = (
torch.from_numpy(img_padded)
.float()
.unsqueeze(0)
.unsqueeze(0)
/ 255.0
).to(device)
with torch.no_grad():
hm_t, off_t = model(tensor)
hm = hm_t.squeeze(0).cpu().numpy()[:, : h // 2, : w // 2]
off = off_t.squeeze(0).cpu().numpy()[:, : h // 2, : w // 2]
all_heatmaps.append(hm)
all_offsets.append(off)
return np.mean(all_heatmaps, axis=0), np.mean(all_offsets, axis=0)
def sliding_window_inference(
model: ImmunogoldCenterNet,
image: np.ndarray,
patch_size: int = 512,
overlap: int = 128,
device: torch.device = torch.device("cpu"),
) -> tuple:
"""
Full-image inference via sliding window with overlap stitching.
Tiles the image into overlapping patches, runs the model on each,
and stitches heatmaps using max in overlap regions.
Args:
model: trained model
image: (H, W) uint8 preprocessed image
patch_size: tile size
overlap: overlap between tiles
device: torch device
Returns:
heatmap: (2, H/2, W/2) numpy array
offsets: (2, H/2, W/2) numpy array
"""
model.eval()
h, w = image.shape[:2]
stride_step = patch_size - overlap
# Output dimensions at model stride
out_h = h // 2
out_w = w // 2
out_patch = patch_size // 2
heatmap = np.zeros((2, out_h, out_w), dtype=np.float32)
offsets = np.zeros((2, out_h, out_w), dtype=np.float32)
count = np.zeros((out_h, out_w), dtype=np.float32)
for y0 in range(0, h - patch_size + 1, stride_step):
for x0 in range(0, w - patch_size + 1, stride_step):
patch = image[y0 : y0 + patch_size, x0 : x0 + patch_size]
tensor = (
torch.from_numpy(patch)
.float()
.unsqueeze(0)
.unsqueeze(0)
/ 255.0
).to(device)
with torch.no_grad():
hm, off = model(tensor)
hm_np = hm.squeeze(0).cpu().numpy()
off_np = off.squeeze(0).cpu().numpy()
# Output coordinates
oy0 = y0 // 2
ox0 = x0 // 2
# Max-stitch heatmap, average-stitch offsets
heatmap[:, oy0 : oy0 + out_patch, ox0 : ox0 + out_patch] = np.maximum(
heatmap[:, oy0 : oy0 + out_patch, ox0 : ox0 + out_patch],
hm_np,
)
offsets[:, oy0 : oy0 + out_patch, ox0 : ox0 + out_patch] += off_np
count[oy0 : oy0 + out_patch, ox0 : ox0 + out_patch] += 1
# Average offsets where counted
count = np.maximum(count, 1)
offsets /= count[np.newaxis, :, :]
return heatmap, offsets
|