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from __future__ import annotations
import base64
import json
from functools import lru_cache
from io import BytesIO
from pathlib import Path
from typing import Any
import numpy as np
import torch
from fastapi import HTTPException
from PIL import Image, UnidentifiedImageError
from torch import nn
from torch.nn import functional as F
from torchvision.models import ResNet18_Weights, resnet18
APP_DIR = Path(__file__).resolve().parent
MODEL_DIR = APP_DIR / "models" / "cnn_resnet18_20cls"
MODEL_PATH = MODEL_DIR / "best_model.pt"
LABEL_MAP_PATH = MODEL_DIR / "label_map.json"
QUICKDRAW100_MODEL_DIR = APP_DIR / "models" / "cnn_residual_100cls_64"
QUICKDRAW100_MODEL_PATH = QUICKDRAW100_MODEL_DIR / "best_model.pt"
QUICKDRAW100_LABEL_MAP_PATH = QUICKDRAW100_MODEL_DIR / "label_map.json"
class ResidualBlock(nn.Module):
def __init__(self, channels: int, dropout: float = 0.0):
super().__init__()
self.block = nn.Sequential(
nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(channels),
nn.ReLU(),
nn.Dropout2d(dropout),
nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(channels),
)
self.activation = nn.ReLU()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.activation(x + self.block(x))
class ResidualCNN(nn.Module):
def __init__(self, num_classes: int):
super().__init__()
self.features = nn.Sequential(
nn.Conv2d(1, 48, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(48),
nn.ReLU(),
ResidualBlock(48, dropout=0.05),
nn.MaxPool2d(2),
nn.Conv2d(48, 96, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(96),
nn.ReLU(),
ResidualBlock(96, dropout=0.05),
nn.MaxPool2d(2),
nn.Conv2d(96, 192, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(192),
nn.ReLU(),
ResidualBlock(192, dropout=0.08),
nn.MaxPool2d(2),
nn.Conv2d(192, 256, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(),
ResidualBlock(256, dropout=0.08),
nn.AdaptiveAvgPool2d((1, 1)),
)
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Dropout(0.35),
nn.Linear(256, num_classes),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.classifier(self.features(x))
def build_model(
model_name: str,
num_classes: int,
pretrained: bool = False,
freeze_backbone: bool = False,
) -> nn.Module:
if model_name == "resnet18":
weights = ResNet18_Weights.DEFAULT if pretrained else None
model = resnet18(weights=weights)
model.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1, bias=False)
model.maxpool = nn.Identity()
model.fc = nn.Linear(model.fc.in_features, num_classes)
if freeze_backbone:
for name, param in model.named_parameters():
param.requires_grad = name.startswith("fc.")
return model
if model_name == "residual-cnn":
return ResidualCNN(num_classes=num_classes)
raise ValueError(f"Unsupported embedded CNN model: {model_name}")
def pick_cnn_device() -> torch.device:
if torch.backends.mps.is_available():
return torch.device("mps")
if torch.cuda.is_available():
return torch.device("cuda")
return torch.device("cpu")
def load_label_map(label_map_path: Path = LABEL_MAP_PATH) -> dict[str, int]:
if not label_map_path.exists():
raise FileNotFoundError(f"Missing CNN label map: {label_map_path}")
return json.loads(label_map_path.read_text(encoding="utf-8"))
def labels_from_map(label_map: dict[str, int]) -> list[str]:
return [name for name, _ in sorted(label_map.items(), key=lambda item: item[1])]
@lru_cache(maxsize=1)
def image_predictor() -> tuple[nn.Module, list[str], torch.device, int]:
if not MODEL_PATH.exists():
raise FileNotFoundError(f"Missing CNN model: {MODEL_PATH}")
return load_predictor(MODEL_PATH, LABEL_MAP_PATH)
@lru_cache(maxsize=1)
def quickdraw100_predictor() -> tuple[nn.Module, list[str], torch.device, int]:
return load_predictor(QUICKDRAW100_MODEL_PATH, QUICKDRAW100_LABEL_MAP_PATH)
def load_predictor(model_path: Path, label_map_path: Path) -> tuple[nn.Module, list[str], torch.device, int]:
if not model_path.exists():
raise FileNotFoundError(f"Missing CNN model: {model_path}")
label_map = load_label_map(label_map_path)
labels = labels_from_map(label_map)
device = pick_cnn_device()
checkpoint = torch.load(model_path, map_location=device, weights_only=False)
config = checkpoint.get("config", {})
image_size = int(config.get("image_size", 96))
model = build_model(
config.get("model", "resnet18"),
num_classes=len(labels),
pretrained=bool(config.get("pretrained", False)),
freeze_backbone=bool(config.get("freeze_backbone", False)),
).to(device)
model.load_state_dict(checkpoint["model_state"])
model.eval()
return model, labels, device, image_size
def decode_data_url(data_url: str) -> bytes:
if "," in data_url:
_, encoded = data_url.split(",", 1)
else:
encoded = data_url
try:
return base64.b64decode(encoded, validate=True)
except ValueError as exc:
raise HTTPException(status_code=400, detail="image must be a base64 PNG data URL.") from exc
def uploaded_image_to_tensor(image_bytes: bytes, image_size: int) -> torch.Tensor:
try:
image = Image.open(BytesIO(image_bytes)).convert("L")
except UnidentifiedImageError as exc:
raise HTTPException(status_code=400, detail="Unsupported image file. Use PNG, JPG, or WEBP.") from exc
array = np.asarray(image, dtype=np.float32) / 255.0
if array.mean() > 0.5:
array = 1.0 - array
ink = array > 0.15
if not np.any(ink):
raise HTTPException(status_code=400, detail="No visible sketch stroke detected in the image.")
ys, xs = np.where(ink)
y0, y1 = int(ys.min()), int(ys.max()) + 1
x0, x1 = int(xs.min()), int(xs.max()) + 1
crop = array[y0:y1, x0:x1]
side = max(crop.shape)
pad = max(2, int(side * 0.12))
canvas = np.zeros((side + pad * 2, side + pad * 2), dtype=np.float32)
offset_y = (canvas.shape[0] - crop.shape[0]) // 2
offset_x = (canvas.shape[1] - crop.shape[1]) // 2
canvas[offset_y : offset_y + crop.shape[0], offset_x : offset_x + crop.shape[1]] = crop
resample = Image.Resampling.BILINEAR if hasattr(Image, "Resampling") else Image.BILINEAR
resized = Image.fromarray(np.uint8(np.clip(canvas, 0.0, 1.0) * 255)).resize(
(image_size, image_size),
resample,
)
tensor_array = np.asarray(resized, dtype=np.float32) / 255.0
return torch.from_numpy(tensor_array[None, :, :]).unsqueeze(0)
def tensor_image_to_input(image: torch.Tensor, image_size: int) -> torch.Tensor:
if image.ndim == 2:
image = image[None, :, :]
if image.ndim == 3:
image = image.unsqueeze(0)
if image.shape[1] != 1:
image = image.mean(dim=1, keepdim=True)
image = image.float().clamp(0, 1)
if image.shape[-2:] != (image_size, image_size):
image = F.interpolate(image, size=(image_size, image_size), mode="bilinear", align_corners=False)
return image
def top_predictions(probs: torch.Tensor, labels: list[str], top_k: int) -> list[dict[str, Any]]:
top_k = max(1, min(int(top_k), len(labels)))
scores, indices = torch.topk(probs, k=top_k)
predictions = []
for score, idx in zip(scores.tolist(), indices.tolist()):
label = labels[idx]
predictions.append(
{
"label": label,
"confidence": float(score),
"reference": f"/api/reference/{label}.svg",
}
)
return predictions
def classes(predictor: str = "legacy20") -> list[str]:
if predictor == "quickdraw100":
return labels_from_map(load_label_map(QUICKDRAW100_LABEL_MAP_PATH))
return labels_from_map(load_label_map(LABEL_MAP_PATH))
def get_predictor(predictor: str) -> tuple[nn.Module, list[str], torch.device, int]:
if predictor == "quickdraw100":
return quickdraw100_predictor()
return image_predictor()
def predict_tensor(image: torch.Tensor, top_k: int = 5, predictor: str = "legacy20") -> dict[str, Any]:
model, labels, device, image_size = get_predictor(predictor)
tensor = tensor_image_to_input(image, image_size=image_size).to(device)
with torch.no_grad():
logits = model(tensor)
probs = torch.softmax(logits, dim=1).squeeze(0).detach().cpu()
predictions = top_predictions(probs, labels, top_k)
return {
"model": "cnn",
"predictor": predictor,
"input": "tensor",
"image_size": image_size,
"prediction": predictions[0],
"top": predictions,
}
def predict_image(image_bytes: bytes, top_k: int = 5, predictor: str = "legacy20") -> dict[str, Any]:
model, labels, device, image_size = get_predictor(predictor)
tensor = uploaded_image_to_tensor(image_bytes, image_size=image_size).to(device)
with torch.no_grad():
logits = model(tensor)
probs = torch.softmax(logits, dim=1).squeeze(0).detach().cpu()
predictions = top_predictions(probs, labels, top_k)
return {
"model": "cnn",
"predictor": predictor,
"input": "image",
"image_size": image_size,
"prediction": predictions[0],
"top": predictions,
}
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