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e258548 | 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 | # src/model_loader.py
import logging
import os
import time
import requests
import torch
import torch.nn.functional as F
from huggingface_hub import hf_hub_download
from PIL import Image
from src.constants import CLASS_NAMES, HF_MODEL_FILENAME, HF_REPO_ID
from src.utils import preprocess_image
logger = logging.getLogger(__name__)
HF_REVISION = "217a9639ec46e2c5fd241973433c6ad69f984f54"
class ModelLoader:
"""A class to load and manage a trained PyTorch model for making predictions.
This class handles model loading, device management, and inference operations
for image classification tasks.
Attributes
----------
model_path (str): Path to the saved model file.
device (torch.device): Device to run the model on (CPU or CUDA).
model: The loaded PyTorch model.
class_names (list): List of class names for classification.
"""
def __init__(self):
"""Initialize the ModelLoader by downloading model from Hugging Face."""
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model = None
self.class_names = list(CLASS_NAMES.keys())
# Get HF token from environment
self.hf_token = os.getenv("HF_TOKEN")
if self.hf_token:
logger.info("HuggingFace token found, using authenticated access")
else:
logger.warning("No HF_TOKEN found, using anonymous access (rate limited)")
logger.info(f"Using device: {self.device}")
# Download and load model from HF with retry logic
try:
logger.info(f"Downloading model from Hugging Face: {HF_REPO_ID}")
self.model_path = self._download_with_retry(HF_REPO_ID, HF_MODEL_FILENAME)
logger.info(f"Model downloaded to: {self.model_path}")
self._load_model()
except Exception as e:
logger.error(f"Failed to download model from Hugging Face: {e}")
raise e
def _download_with_retry(
self, repo_id: str, filename: str, max_retries: int = 3, delay: float = 2.0
) -> str:
"""Download model with retry logic for network failures."""
for attempt in range(max_retries):
try:
return hf_hub_download(
repo_id=repo_id,
filename=filename,
revision="217a9639ec46e2c5fd241973433c6ad69f984f54",
token=self.hf_token,
)
except requests.HTTPError as e:
if e.response.status_code == 401:
raise ValueError(
"Authentication failed. Check HF_TOKEN environment variable"
) from e
elif e.response.status_code == 429:
# Rate limited - use longer delay
if attempt == max_retries - 1:
raise ValueError(
"Rate limited by HuggingFace. Try again later"
) from e
logger.warning(f"Rate limited. Retrying in {delay * 5} seconds...")
time.sleep(delay * 5)
continue
elif e.response.status_code >= 500:
# Server error - retry
if attempt == max_retries - 1:
raise e
logger.warning(
f"Server error {e.response.status_code}. Retrying..."
)
time.sleep(delay * (attempt + 1))
else:
# Other HTTP errors - don't retry
raise e
except (ConnectionError, TimeoutError) as e:
if attempt == max_retries - 1:
raise e
logger.warning(f"Network error: {e}. Retrying in {delay} seconds...")
time.sleep(delay * (attempt + 1))
except Exception as e:
# Handle repository not found and other general errors
if "not found" in str(e).lower() or "access denied" in str(e).lower():
raise ValueError(
f"Repository {repo_id} not found or access denied"
) from e
# For other unknown errors, retry
if attempt == max_retries - 1:
raise e
logger.warning(
f"Download attempt {attempt + 1} failed: {e}. Retrying..."
)
time.sleep(delay * (attempt + 1))
raise Exception("All download attempts failed")
def _load_model(self):
"""Load the trained model."""
try:
logger.info(f"Loading model from {self.model_path}")
# Load model
self.model = torch.load( # nosec B614
self.model_path, map_location=self.device, weights_only=False
)
self.model.to(self.device)
self.model.eval()
logger.info("Model loaded and set to evaluation mode")
# Test model with dummy input
self._validate_model()
# Prevent slow first requests caused by model initialization.
self._warmup_model()
except Exception as e:
logger.error(f"Failed to load model: {e}")
raise e
def _validate_model(self):
"""Validate model has expected architecture and outputs."""
try:
# Test with dummy input to check output shape
dummy_input = torch.randn(1, 3, 224, 224).to(self.device)
with torch.no_grad():
output = self.model(dummy_input)
# Validate output shape matches expected number of classes
expected_classes = len(self.class_names)
if output.shape[1] != expected_classes:
raise ValueError(
f"Model output shape {output.shape} "
f"doesn't match expected classes {expected_classes}"
)
# Validate output is proper logits (not already softmaxed)
if torch.any(output < 0):
logger.info("Model outputs raw logits (expected)")
else:
logger.warning("Model might output probabilities instead of logits")
logger.info(f"Model validation successful. Output shape: {output.shape}")
except Exception as e:
logger.error(f"Model validation failed: {e}")
raise e
def _warmup_model(self):
"""Warm up the model with multiple dummy predictions to optimize performance."""
logger.info("Warming up model for optimal performance...")
try:
# Run multiple warmup predictions with different batch sizes
dummy_inputs = [
torch.randn(1, 3, 224, 224).to(self.device),
torch.randn(2, 3, 224, 224).to(self.device), # Batch of 2
]
with torch.no_grad():
for i, dummy_input in enumerate(dummy_inputs):
start_time = time.time()
_ = self.model(dummy_input)
warmup_time = time.time() - start_time
logger.info(f"Warmup {i + 1} completed in {warmup_time:.3f}s")
logger.info("Model warmup completed successfully")
except Exception as e:
logger.warning(f"Model warmup failed: {e}. First prediction may be slower.")
def predict(self, image: Image.Image) -> dict:
"""Make prediction on a single image."""
if self.model is None:
raise ValueError("Model not loaded")
try:
# Preprocess image
input_tensor = preprocess_image(image).unsqueeze(0).to(self.device)
# Make prediction
with torch.no_grad():
outputs = self.model(input_tensor)
probabilities = F.softmax(outputs, dim=1)
confidence, predicted_idx = torch.max(probabilities, 1)
# Convert to CPU and numpy for JSON serialization
probabilities = probabilities.cpu().numpy()[0]
confidence = confidence.cpu().item()
predicted_idx = predicted_idx.cpu().item()
# Create results dictionary
predicted_class = self.class_names[predicted_idx]
all_probabilities = {
self.class_names[i]: float(prob)
for i, prob in enumerate(probabilities)
}
return {
"predicted_class": predicted_class,
"confidence": confidence,
"probabilities": all_probabilities,
"predicted_index": predicted_idx,
}
except Exception as e:
logger.error(f"Prediction error: {e}")
raise e
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