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create_goodness_dataset.py
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create_goodness_dataset.py
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import os
import logging
from argparse import ArgumentParser
import pandas as pd
import torch
from torch.utils.data import DataLoader
from ignite.engine.engine import Engine, State, Events
from ignite.handlers import ModelCheckpoint, EarlyStopping
from ignite._utils import convert_tensor
from utils import Experiment
from utils.factory import *
from utils.helpers import static_vars
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
from tqdm import tqdm
logging.basicConfig(level=logging.INFO, format='')
logger = logging.getLogger()
def save_image(tensor, fname, cmap=plt.cm.jet):
data = tensor.to("cpu").numpy().squeeze(0).squeeze(0)
plt.imsave(fname, data, cmap=cmap)
def save_numpy(tensor, fname):
data = tensor.to("cpu").numpy()
np.save(fname, data)
def main(config, dataset="test"):
assert validate_config(config), "ERROR: Config file is invalid. Please see log for details."
logger.info("INFO: {}".format(config.toDict()))
if config.device == "cpu" and torch.cuda.is_available():
logger.warning("WARNING: Not using the GPU")
if "cuda" in config.device:
config.device = "cuda"
assert dataset in config.datasets, "ERROR: Not test dataset is specified in the config. Don't know how to proceed."
logger.info("INFO: Creating datasets and dataloaders...")
config.datasets[dataset].update({'shuffle': False, 'augment': False, 'workers': 1})
config.datasets[dataset].update({'batch_size': 1, "named": True, "return_all": True})
# Create the training dataset
dset_test = create_dataset(config.datasets[dataset])
loader_test = get_data_loader(dset_test, config.datasets[dataset])
logger.info("INFO: Running inference on {} samples".format(len(dset_test)))
cp_paths = None
last_epoch = 0
checkpoint_dir = config.result_dir
if 'checkpoint' in config:
checkpoint_dir = config.checkpoint_dir if 'checkpoint_dir' in config else config.result_path
cp_paths, last_epoch = config.get_checkpoints(path=checkpoint_dir, tag=config.checkpoint)
print(f"Found checkpoint {cp_paths} for epoch {last_epoch}")
if "DetA" in cp_paths:
del cp_paths["DetA"]
del cp_paths["DetB"]
if "Match" in cp_paths:
del cp_paths["Match"]
models = {}
for name, model in config.model.items():
if name in ["DetA", "DetB", "Match"]:
continue
logger.info("INFO: Building the {} model".format(name))
models[name] = build_model(model)
# Load the checkpoint
if name in cp_paths:
models[name].load_state_dict( torch.load( cp_paths[name] ) )
logger.info("INFO: Loaded model {} checkpoint {}".format(name, cp_paths[name]))
models[name].to(config.device)
print(models[name])
if 'debug' in config and config.debug is True:
print("*********** {} ************".format(name))
for name, param in models[name].named_parameters():
if param.requires_grad:
print(name, param.data)
losses = {}
for name, fcns in config.loss.items():
losses[name] = []
for l in fcns:
losses[name].append( get_loss(l) )
assert losses[name][-1], "Loss function {} for {} could not be found, please check your config".format(l, name)
exp_logger = None
if 'logger' in config:
logger.info("INFO: Initialising the experiment logger")
exp_logger = get_experiment_logger(config.result_path, config.logger)
logger.info("INFO: Creating training manager and configuring callbacks")
trainer = get_trainer(models, None, losses, None, config)
evaluator_engine = Engine(trainer.evaluate)
trainer.attach("test_loader", loader_test)
trainer.attach("evaluation_engine", evaluator_engine)
logger.info("INFO: Starting inference...")
results = []
save_path = os.path.join(config.checkpoint_dir, f"extracted_{last_epoch}", dataset)
os.makedirs(save_path, exist_ok=True)
with torch.no_grad():
for i, (xs, ys, names) in enumerate(tqdm(loader_test)):
batch = (xs, ys)
try:
entity = {
"wkt": names["WKT"][0],
"city": names["city"][0],
"shift_x": names["p_match"][0].to("cpu").numpy()[0],
"shift_y": names["p_match"][1].to("cpu").numpy()[0]
}
filename = "{}_{}".format(names["city"], names["WKT"])
except:
entity = {
"season": names["season"][0],
"scene": names["scene"][0],
"patch": names["patch"][0],
"shift_x": names["p_match"][0].to("cpu").numpy()[0],
"shift_y": names["p_match"][1].to("cpu").numpy()[0]
}
filename = "{}_{}_{}".format(names["season"], names["scene"], names["patch"])
imgs, hms, y, fts, dets = trainer.infer_batch(batch)
(search_img, template_img, template_hard, search_hard) = imgs
(heatmap_neg, heatmap_neg_raw, heatmap_hneg, heatmap_hneg_raw) = hms
(y_a, y_b, y_bhn) = fts
d_l2 = trainer.l2_shift_loss(heatmap_hneg, y[0], device="cuda")
d_target = trainer.weighted_binary_cross_entropy(heatmap_hneg.detach(), y[0], device="cuda", reduction="none")
d_target = torch.mean(d_target, dim=[1,2,3])
d_target = -torch.log(d_target)
# Save heatmaps
save_numpy( heatmap_hneg_raw, os.path.join(save_path, f"{filename}_hm.npy") )
save_numpy( search_img, os.path.join(save_path, f"{filename}_sar.npy") )
save_numpy( search_hard, os.path.join(save_path, f"{filename}_sar_crop.npy") )
save_numpy( template_img, os.path.join(save_path, f"{filename}_opt.npy") )
save_numpy( template_hard, os.path.join(save_path, f"{filename}_opt_crop.npy") )
save_numpy( y[0], os.path.join(save_path, f"{filename}_gt.npy") )
entity.update({
"l2": d_l2.to("cpu").numpy()[0],
"nlog_match_loss": d_target.to("cpu").numpy()[0]
})
results.append(entity)
if i % 1000 == 0:
df = pd.DataFrame.from_dict(results)
df.to_csv(os.path.join(config.checkpoint_dir, "checkpoint_{}_extracted_dset_{}.csv".format(last_epoch, dataset)) , index=None)
df = pd.DataFrame.from_dict(results)
df.to_csv(os.path.join(config.checkpoint_dir, "checkpoint_{}_extracted_dset_{}.csv".format(last_epoch, dataset)) , index=None)
config.save()
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument('-c', '--config', default=None, type=str, required=True, help='config file path (default: None)')
parser.add_argument('--checkpoint', default=None, type=str, help='Checkpoint tag to reload')
parser.add_argument('--checkpoint_dir', default=None, type=str, help='Checkpoint directory to reload')
parser.add_argument('--dataset', default="test", type=str, help="Which dataset to test on")
args = parser.parse_args()
OVERLOADABLE = ['checkpoint', 'epochs', 'checkpoint_dir', 'resume_from']
overloaded = {}
for k, v in vars(args).items():
if (k in OVERLOADABLE) and (v is not None):
overloaded[k] = v
config = Experiment.load_from_path(args.config, overloaded)
print(config.checkpoint)
assert config, "Config could not be loaded."
main(config, args.dataset)