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inference.py
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import os
import numpy as np
from argparse import ArgumentParser
from torchvision import models as torch_models
from torchvision import transforms
from collections import OrderedDict
import pandas as pd
import torch
from dataloader import MultiLabelDatasetInference
from torch.utils.data import DataLoader
import torch.nn as nn
import sewer_models
import ml_models
TORCHVISION_MODEL_NAMES = sorted(name for name in torch_models.__dict__ if name.islower() and not name.startswith("__") and callable(torch_models.__dict__[name]))
SEWER_MODEL_NAMES = sorted(name for name in sewer_models.__dict__ if name.islower() and not name.startswith("__") and callable(sewer_models.__dict__[name]))
MULTILABEL_MODEL_NAMES = sorted(name for name in ml_models.__dict__ if name.islower() and not name.startswith("__") and callable(ml_models.__dict__[name]))
MODEL_NAMES = TORCHVISION_MODEL_NAMES + SEWER_MODEL_NAMES + MULTILABEL_MODEL_NAMES
def evaluate(dataloader, model, device):
model.eval()
sigmoidPredictions = None
imgPathsList = []
sigmoid = nn.Sigmoid()
dataLen = len(dataloader)
with torch.no_grad():
for i, (images, imgPaths) in enumerate(dataloader):
if i % 100 == 0:
print("{} / {}".format(i, dataLen))
images = images.to(device)
output = model(images)
sigmoidOutput = sigmoid(output).detach().cpu().numpy()
if sigmoidPredictions is None:
sigmoidPredictions = sigmoidOutput
else:
sigmoidPredictions = np.vstack((sigmoidPredictions, sigmoidOutput))
imgPathsList.extend(list(imgPaths))
return sigmoidPredictions, imgPathsList
def load_model(model_path, best_weights=False):
if best_weights:
if not os.path.isfile(model_path):
raise ValueError("The provided path does not lead to a valid file: {}".format(model_path))
last_ckpt_path = model_path
else:
last_ckpt_path = os.path.join(model_path, "last.ckpt")
if not os.path.isfile(last_ckpt_path):
raise ValueError("The provided directory path does not contain a 'last.ckpt' file: {}".format(model_path))
model_last_ckpt = torch.load(last_ckpt_path)
model_name = model_last_ckpt["hyper_parameters"]["model"]
num_classes = model_last_ckpt["hyper_parameters"]["num_classes"]
training_mode = model_last_ckpt["hyper_parameters"]["training_mode"]
br_defect = model_last_ckpt["hyper_parameters"]["br_defect"]
# Load best checkpoint
best_model = model_last_ckpt
# if best_weights:
# best_model = model_last_ckpt
# else:
# best_model_path = model_last_ckpt["checkpoint_callback_best_model_path"]
# best_model = torch.load(best_model_path)
best_model_state_dict = best_model["state_dict"]
updated_state_dict = OrderedDict()
for k,v in best_model_state_dict.items():
name = k.replace("model.", "")
if "criterion" in name:
continue
updated_state_dict[name] = v
return updated_state_dict, model_name, num_classes, training_mode, br_defect
def run_inference(args):
ann_root = args["ann_root"]
data_root = args["data_root"]
model_path = args["model_path"]
outputPath = args["results_output"]
best_weights = args["best_weights"]
# best_weights = False
split = args["split"]
if not os.path.isdir(outputPath):
os.makedirs(outputPath)
updated_state_dict, model_name, num_classes, training_mode, br_defect = load_model(model_path, best_weights)
if "model_version" not in args.keys():
model_version = model_name
else:
model_version = args["model_version"]
# Init model
if model_name in TORCHVISION_MODEL_NAMES:
model = torch_models.__dict__[model_name](num_classes = num_classes)
elif model_name in SEWER_MODEL_NAMES:
model = sewer_models.__dict__[model_name](num_classes = num_classes)
elif model_name in MULTILABEL_MODEL_NAMES:
model = ml_models.__dict__[model_name](num_classes = num_classes)
else:
raise ValueError("Got model {}, but no such model is in this codebase".format(model_name))
model.load_state_dict(updated_state_dict)
# initialize dataloaders
img_size = 299 if model in ["inception_v3", "chen2018_multilabel"] else 224
eval_transform=transforms.Compose([
transforms.Resize((img_size, img_size)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.523, 0.453, 0.345], std=[0.210, 0.199, 0.154])
])
dataset = MultiLabelDatasetInference(ann_root, data_root, split=split, transform=eval_transform, onlyDefects=False)
dataloader = DataLoader(dataset, batch_size=args["batch_size"], num_workers = args["workers"], pin_memory=True)
if training_mode in ["e2e", "defect"]:
labelNames = dataset.LabelNames
elif training_mode == "binary":
labelNames = ["Defect"]
elif training_mode == "binaryrelevance":
labelNames = [br_defect]
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
# Validation results
print("VALIDATION")
sigmoid_predictions, val_imgPaths = evaluate(dataloader, model, device)
sigmoid_dict = {}
sigmoid_dict["Filename"] = val_imgPaths
for idx, header in enumerate(labelNames):
sigmoid_dict[header] = sigmoid_predictions[:,idx]
sigmoid_df = pd.DataFrame(sigmoid_dict)
sigmoid_df.to_csv(os.path.join(outputPath, "{}_{}_sigmoid.csv".format(model_version, split.lower())), sep=",", index=False)
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument('--conda_env', type=str, default='pytorch_gpu')
parser.add_argument('--notification_email', type=str, default='')
parser.add_argument('--ann_root', type=str, default="D:\\Research\\3-code\\sewer-ml\\annotations")
parser.add_argument('--data_root', type=str, default="D:\\Research\\3-code\\sewer-ml\\Data\\val13")
parser.add_argument('--batch_size', type=int, default=16, help="Size of the batch per GPU")
parser.add_argument('--workers', type=int, default=2)
parser.add_argument("--model_path", type=str, default="D:\\Research\\3-code\\sewer-ml\\logs\\xie2019_binary\\binary-version_1\\checkpoints")
parser.add_argument("--best_weights", action="store_true", help="If true 'model_path' leads to a specific weight file. If False it leads to the output folder of lightning_trainer where the last.ckpt file is used to read the best model weights.")
parser.add_argument("--results_output", type=str, default = "D:\\Research\\3-code\\sewer-ml\\results")
parser.add_argument("--split", type=str, default = "Val", choices=["Train", "Val", "Test"])
args = vars(parser.parse_args())
run_inference(args)