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97fcc90 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 | from __future__ import annotations
import os
from typing import List
import numpy as np
import pandas as pd
from PIL import Image
import torch
import torch.nn as nn
import torchvision.transforms.v2 as T
from clearml import InputModel, Task
from huggingface_hub import hf_hub_download
from src.models.cnn_model import PlantCNN
from src.models.resnet18_finetune import make_resnet18
from src.utils.class_names import CLASS_NAMES
_MODEL_CACHE = {}
_CLASS_NAMES_CACHE = None
def _device_key(device: torch.device) -> str:
return str(device)
def _get_device() -> torch.device:
if torch.cuda.is_available():
return torch.device("cuda")
if getattr(torch.backends, "mps", None) and torch.backends.mps.is_available():
return torch.device("mps")
return torch.device("cpu")
def _build_val_transform(image_size: int = 256) -> T.Compose:
return T.Compose([
T.Resize((image_size, image_size)),
T.ToImage(),
T.ToDtype(torch.float32, scale=True),
])
def _load_model_from_checkpoint(
model_path: str,
num_classes: int,
model_type: str,
device: torch.device,
) -> nn.Module:
if not os.path.isfile(model_path):
raise FileNotFoundError(f"Model file not found - {model_path}")
ckpt = torch.load(model_path, map_location=device)
if model_type.lower() == "resnet18":
model = make_resnet18(num_classes=num_classes)
elif model_type.lower() == "cnn":
model = PlantCNN(num_classes=num_classes)
else:
raise ValueError(f"Unknown model type - {model_type}. Must be 'resnet18' or 'cnn'.")
if isinstance(ckpt, dict) and "state_dict" in ckpt:
model.load_state_dict(ckpt["state_dict"])
elif isinstance(ckpt, nn.Module):
model = ckpt
else:
try:
model.load_state_dict(ckpt)
except Exception:
raise ValueError(f"Unexpected checkpoint format in - {model_path}. ")
model.to(device)
model.eval()
return model
def _load_model_from_clearml_model_id(
model_id: str,
num_classes: int,
model_type: str,
device: torch.device,
) -> nn.Module:
model_obj = InputModel(model_id=model_id)
downloaded_path = model_obj.get_local_copy()
if downloaded_path is None:
raise FileNotFoundError(f"Failed to download model from ClearML Model ID - {model_id}")
if os.path.isdir(downloaded_path):
model_files = [f for f in os.listdir(downloaded_path) if f.endswith((".pt", ".pth"))]
if model_files:
model_path = os.path.join(downloaded_path, model_files[0])
else:
for name in ["best_baseline.pt", "best_model.pt", "best_baseline.pth", "best_model.pth"]:
candidate = os.path.join(downloaded_path, name)
if os.path.isfile(candidate):
model_path = candidate
break
if model_path is None:
raise FileNotFoundError(f"No model file found in directory - {downloaded_path}")
else:
model_path = downloaded_path
if model_type.lower() == "resnet18":
model = make_resnet18(num_classes=num_classes)
elif model_type.lower() == "cnn":
model = PlantCNN(num_classes=num_classes)
else:
raise ValueError(f"Unknown model type - {model_type}. Must be 'resnet18' or 'cnn'.")
state_dict = torch.load(model_path, map_location=device)
if isinstance(state_dict, dict) and "state_dict" in state_dict:
state_dict = state_dict["state_dict"]
model.load_state_dict(state_dict)
model.to(device)
model.eval()
return model
def _load_model_from_clearml_task_id(
task_id: str,
num_classes: int,
model_type: str,
device: torch.device,
) -> nn.Module:
source_task = Task.get_task(task_id=task_id)
artifact_names = ["best_model", "best_baseline", "model"]
model_path = None
for artifact_name in artifact_names:
if artifact_name in source_task.artifacts:
model_path = source_task.artifacts[artifact_name].get_local_copy()
if model_path:
break
if model_path is None:
raise FileNotFoundError(f"No model artifact found in Task ID - {task_id}")
if model_type.lower() == "resnet18":
model = make_resnet18(num_classes=num_classes)
elif model_type.lower() == "cnn":
model = PlantCNN(num_classes=num_classes)
else:
raise ValueError(f"Unknown model type - {model_type}. Must be 'resnet18' or 'cnn'.")
state_dict = torch.load(model_path, map_location=device)
if isinstance(state_dict, dict) and "state_dict" in state_dict:
state_dict = state_dict["state_dict"]
model.load_state_dict(state_dict)
model.to(device)
model.eval()
return model
def _load_model_from_huggingface(
repo_id: str,
filename: str,
num_classes: int,
model_type: str,
device: torch.device,
) -> nn.Module:
model_path = hf_hub_download(repo_id=repo_id, filename=filename, repo_type="model")
if model_type.lower() == "resnet18":
model = make_resnet18(num_classes=num_classes)
elif model_type.lower() == "cnn":
model = PlantCNN(num_classes=num_classes)
else:
raise ValueError(f"Unknown model type - {model_type}. Must be 'resnet18' or 'cnn'.")
state_dict = torch.load(model_path, map_location=device)
if isinstance(state_dict, dict) and "state_dict" in state_dict:
state_dict = state_dict["state_dict"]
model.load_state_dict(state_dict)
model.to(device)
model.eval()
return model
def _get_class_names() -> List[str]:
return CLASS_NAMES
def predict_image(img: Image.Image, k: int = 5) -> pd.DataFrame:
"""
Predict top-k for a single PIL image.
Returns a DataFrame with columns: Img, Rank, Disease, Probability, Model
"""
if img is None:
return pd.DataFrame({"Disease": [], "Probability": []})
try:
class_names = _get_class_names()
if not class_names:
raise ValueError("class_names list is empty.")
model_type = os.getenv("MODEL_TYPE", "resnet18")
model_path = os.getenv("MODEL_PATH", "")
clearml_model_id = os.getenv("CLEARML_MODEL_ID", "")
clearml_task_id = os.getenv("CLEARML_TASK_ID", "")
hf_repo_id = os.getenv("HF_REPO_ID", "")
hf_filename = os.getenv("HF_FILENAME", "")
device = _get_device()
device_k = _device_key(device)
num_classes = len(class_names)
transform = _build_val_transform(image_size=256)
x = transform(img.convert("RGB")).unsqueeze(0).to(device)
model = None
# ClearML Model ID
if clearml_model_id and clearml_model_id.strip():
cache_key = ("clearml_model", model_type, clearml_model_id, num_classes, device_k)
if cache_key not in _MODEL_CACHE:
try:
_MODEL_CACHE[cache_key] = _load_model_from_clearml_model_id(clearml_model_id, num_classes, model_type, device)
except Exception:
_MODEL_CACHE[cache_key] = None
model = _MODEL_CACHE.get(cache_key)
# ClearML Task ID
if model is None and clearml_task_id and clearml_task_id.strip():
cache_key = ("clearml_task", model_type, clearml_task_id, num_classes, device_k)
if cache_key not in _MODEL_CACHE:
try:
_MODEL_CACHE[cache_key] = _load_model_from_clearml_task_id(clearml_task_id, num_classes, model_type, device)
except Exception:
_MODEL_CACHE[cache_key] = None
model = _MODEL_CACHE.get(cache_key)
# Hugging Face
if model is None and hf_repo_id and hf_repo_id.strip() and hf_filename and hf_filename.strip():
cache_key = ("huggingface", model_type, hf_repo_id, hf_filename, num_classes, device_k)
if cache_key not in _MODEL_CACHE:
try:
_MODEL_CACHE[cache_key] = _load_model_from_huggingface(hf_repo_id, hf_filename, num_classes, model_type, device)
except Exception:
_MODEL_CACHE[cache_key] = None
model = _MODEL_CACHE.get(cache_key)
# Local checkpoint
if model is None:
if model_path and os.path.isfile(model_path):
cache_key = ("local", model_type, model_path, num_classes, device_k)
if cache_key not in _MODEL_CACHE:
_MODEL_CACHE[cache_key] = _load_model_from_checkpoint(model_path, num_classes, model_type, device)
model = _MODEL_CACHE[cache_key]
else:
raise FileNotFoundError(
f"All loading methods failed. Model ID - {clearml_model_id}, Task ID - {clearml_task_id}, HF - {hf_repo_id}/{hf_filename}, Local path - {model_path}"
)
with torch.no_grad():
logits = model(x)
probs = torch.softmax(logits, dim=1)[0]
topk = min(int(k), len(class_names))
top_probs, top_indices = torch.topk(probs, k=topk)
results = [
(class_names[idx.item()], float(prob.item()))
for prob, idx in zip(top_probs, top_indices)
]
return pd.DataFrame({
"Disease": [r[0] for r in results],
"Probability": [r[1] for r in results],
})
except Exception as e:
return pd.DataFrame({"Disease": [f"Error: {str(e)}"], "Probability": [0.0]})
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