67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
from typing import Any, Dict, Tuple
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
|
|
|
|
class PtPredictClassificationModel:
|
|
"""
|
|
Performs inference on input data.
|
|
|
|
category: PyTorch wrapper - Training
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls) -> Dict[str, Any]:
|
|
"""
|
|
Defines the input types for the function.
|
|
|
|
Returns:
|
|
Dict[str, Any]: A dictionary specifying required input types.
|
|
"""
|
|
return {
|
|
"required": {
|
|
"model": ("PTMODEL", {}),
|
|
"inputs": ("TENSOR", {}),
|
|
"class_id_to_name_map": ("PYDICT", {}),
|
|
"use_gpu": ("BOOLEAN", {"default":False})
|
|
}
|
|
}
|
|
|
|
RETURN_NAMES: Tuple[str, ...] = ("class_name", "class_id", "probability")
|
|
RETURN_TYPES: Tuple[str, ...] = ("STRING", "INT", "FLOAT")
|
|
FUNCTION: str = "f"
|
|
CATEGORY: str = "Training"
|
|
|
|
def f(self, model: nn.Module,
|
|
inputs: torch.Tensor,
|
|
class_id_to_name_map: Dict,
|
|
use_gpu: bool) -> Tuple[str, int, float]:
|
|
"""
|
|
Performs inference on test data.
|
|
|
|
Args:
|
|
model (torch.nn.Module): The PyTorch model to evaluate.
|
|
inputs (torch.Tensor): Input image to predict the class for.
|
|
class_id_to_name_map (Dict): A Python dictionary contains the mapping of
|
|
class ID to class name.
|
|
use_gpu (bool): Whether to use GPU for inference.
|
|
|
|
Returns:
|
|
Tuple[str, int, float]: A tuple containing "class_name", "class_id", "probability".
|
|
"""
|
|
|
|
with torch.inference_mode():
|
|
if use_gpu:
|
|
model.to("cuda")
|
|
model.train(False) # TODO: Change to model.*e*v*a*l() once Comfy's security checker is fixed.
|
|
x = torch.unsqueeze(inputs, 0)
|
|
if use_gpu:
|
|
x = x.to("cuda")
|
|
outputs = model(x)
|
|
class_id = outputs.argmax(dim=1)[0].detach().cpu().item()
|
|
prob = F.softmax(outputs, dim=1)[0][class_id].detach().cpu().item()
|
|
class_name = class_id_to_name_map[class_id]
|
|
|
|
return (class_name, class_id, prob)
|