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12631d6 | 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 | #!/usr/bin/env python3
"""KrishokChat ML Models Automated Verification Suite.
Validates:
1. Every YOLO26 PyTorch (.pt) vision classification model loads and performs inference.
2. Every ONNX model loads and runs a test session via onnxruntime.
3. Output probability dimensions strictly match class_names.json.
4. All 5 folds of the EfficientNet-B0 soil moisture regression model load and predict.
5. Prints a comprehensive diagnostic table for academic reviewers.
"""
import sys
import time
import json
from pathlib import Path
import numpy as np
from PIL import Image
try:
import torch
from ultralytics import YOLO
except ImportError as e:
print(f"Error: Missing core dependencies. Run: pip install -r requirements.txt\n{e}")
sys.exit(1)
try:
import onnxruntime as ort
except ImportError:
ort = None
BASE_DIR = Path(__file__).resolve().parent
VISION_MODELS = [
("01_crop_classifier", "crop_classifier.pt", "crop_classifier.onnx", 10),
("02_potato_disease", "potato_disease.pt", "potato_disease.onnx", 3),
("03_rice_disease", "rice_disease.pt", "rice_disease.onnx", 10),
("04_wheat_disease", "wheat_disease.pt", "wheat_disease.onnx", 11),
("05_corn_disease", "corn_disease.pt", "corn_disease.onnx", 4),
("06_chilli_disease", "chilli_disease.pt", "chilli_disease.onnx", 8),
("07_brassica_disease", "brassica_disease.pt", "brassica_disease.onnx", 11),
]
def run_vision_verification():
print("=" * 80)
print(" KRISHOKCHAT ML SUITE — VISION MODELS (YOLO26-CLS) VERIFICATION")
print("=" * 80)
header = f"{'Folder / Task':<25} | {'PT File':<20} | {'Classes':<7} | {'PT Status':<10} | {'ONNX Status':<11} | {'Latency':<8}"
print(header)
print("-" * 80)
# Base test image
dummy_img = Image.fromarray(np.uint8(np.random.rand(224, 224, 3) * 255))
all_passed = True
for folder, pt_name, onnx_name, expected_classes in VISION_MODELS:
model_dir = BASE_DIR / folder
pt_path = model_dir / pt_name
onnx_path = model_dir / onnx_name
classes_path = model_dir / "class_names.json"
# Check files exist
if not pt_path.exists():
print(f"{folder:<25} | {pt_name:<20} | {expected_classes:<7} | FAILED (missing)")
all_passed = False
continue
# Check class names
with open(classes_path, encoding="utf-8") as f:
classes_data = json.load(f)
num_classes = len(classes_data)
assert num_classes == expected_classes, f"Class count mismatch: {num_classes} != {expected_classes}"
# 1. PyTorch YOLO26-cls test
pt_status = "FAIL"
elapsed = 0.0
try:
t0 = time.time()
model = YOLO(str(pt_path))
res = model.predict(dummy_img, imgsz=224, verbose=False)[0]
elapsed = (time.time() - t0) * 1000.0
probs = res.probs.data.cpu().numpy()
if len(probs) == expected_classes and np.isclose(np.sum(probs), 1.0, atol=1e-2):
pt_status = "PASSED"
else:
pt_status = f"ERR(shape {len(probs)})"
all_passed = False
except Exception as ex:
pt_status = f"EXC: {type(ex).__name__}"
all_passed = False
# 2. ONNX test
onnx_status = "SKIP"
if ort is not None and onnx_path.exists():
try:
session = ort.InferenceSession(str(onnx_path))
input_tensor = session.get_inputs()[0]
shape = input_tensor.shape
# Adapt resolution if static (e.g. 224 vs 256)
h = shape[2] if len(shape) > 2 and isinstance(shape[2], int) else 224
w = shape[3] if len(shape) > 3 and isinstance(shape[3], int) else 224
onnx_np = np.array(dummy_img.resize((w, h)), dtype=np.float32).transpose(2, 0, 1)[None, ...] / 255.0
outputs = session.run(None, {input_tensor.name: onnx_np})[0]
if outputs.shape[-1] == expected_classes:
onnx_status = "PASSED"
else:
onnx_status = f"ERR({outputs.shape})"
all_passed = False
except Exception as ex:
onnx_status = f"EXC: {type(ex).__name__}"
all_passed = False
elif not onnx_path.exists():
onnx_status = "MISSING"
print(f"{folder:<25} | {pt_name:<20} | {num_classes:<7} | {pt_status:<10} | {onnx_status:<11} | {elapsed:.1f} ms")
return all_passed
def run_soil_verification():
print("\n" + "=" * 80)
print(" KRISHOKCHAT ML SUITE — SOIL MOISTURE REGRESSION (EFFICIENTNET-B0) VERIFICATION")
print("=" * 80)
soil_dir = BASE_DIR / "08_soil_moisture"
sys.path.insert(0, str(soil_dir))
try:
from model_def import load_soil_model
except ImportError as e:
print(f"Soil model def import failed: {e}")
return False
all_passed = True
dummy_tensor = torch.randn(1, 3, 224, 224)
for fold in range(5):
fold_file = soil_dir / f"effnetb0_fold{fold}.pt"
if not fold_file.exists():
print(f"Fold {fold}: FAILED (file not found: {fold_file.name})")
all_passed = False
continue
try:
t0 = time.time()
model = load_soil_model(fold_file, device="cpu")
with torch.no_grad():
pred = model(dummy_tensor).item()
latency = (time.time() - t0) * 1000.0
print(f"Fold {fold:<2} ({fold_file.name:<18}) | Output: {pred:7.2f} kPa | Status: PASSED | Latency: {latency:.1f} ms")
except Exception as e:
print(f"Fold {fold:<2} ({fold_file.name:<18}) | Status: FAILED ({e})")
all_passed = False
return all_passed
if __name__ == "__main__":
v_ok = run_vision_verification()
s_ok = run_soil_verification()
print("\n" + "=" * 80)
if v_ok and s_ok:
print(" SUCCESS: ALL 8 MODEL CATEGORIES AND WEIGHTS VERIFIED CLEANLY!")
print(" The 'backend_models/' folder is 100% ready for reviewers and Hugging Face release.")
print("=" * 80)
sys.exit(0)
else:
print(" WARNING: Some model checks failed or were skipped.")
print("=" * 80)
sys.exit(1)
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