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all_utils.py
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all_utils.py
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# import tensorboardX
import pdb
import sys
from collections import MutableMapping, Hashable
import csv
import os
import torch
import torch.nn.functional as F
import numpy as np
from progressbar import ProgressBar
import sys
# Additional information that might be necessary to get the model
DATASET_NUM_CLASS = {
'modelnet40': 40,
'modelnet40_c': 40,
'modelnet40_rscnn': 40,
'modelnet40_pn2': 40,
'modelnet40_dgcnn': 40,
}
class TensorboardManager:
def __init__(self, path):
self.writer = tensorboardX.SummaryWriter(path)
def update(self, split, step, vals):
for k, v in vals.items():
self.writer.add_scalar('%s_%s' % (split, k), v, step)
def close(self):
self.writer.flush()
self.writer.close()
class TrackTrain:
def __init__(self, early_stop_patience):
self.early_stop_patience = early_stop_patience
self.counter = -1
self.best_epoch_val = -1
self.best_epoch_train = -1
self.best_epoch_test = -1
self.best_val = float("-inf")
self.best_test = float("-inf")
self.best_train = float("-inf")
self.test_best_val = float("-inf")
def record_epoch(self, epoch_id, train_metric, val_metric, test_metric):
assert epoch_id == (self.counter + 1)
self.counter += 1
if val_metric >= self.best_val:
self.best_val = val_metric
self.best_epoch_val = epoch_id
self.test_best_val = test_metric
if test_metric >= self.best_test:
self.best_test = test_metric
self.best_epoch_test = epoch_id
if train_metric >= self.best_train:
self.best_train = train_metric
self.best_epoch_train = epoch_id
def save_model(self, epoch_id, split):
"""
Whether to save the current model or not
:param epoch_id:
:param split:
:return:
"""
assert epoch_id == self.counter
if split == 'val':
if self.best_epoch_val == epoch_id:
_save_model = True
else:
_save_model = False
elif split == 'test':
if self.best_epoch_test == epoch_id:
_save_model = True
else:
_save_model = False
elif split == 'train':
if self.best_epoch_train == epoch_id:
_save_model = True
else:
_save_model = False
else:
assert False
return _save_model
def early_stop(self, epoch_id):
assert epoch_id == self.counter
if (epoch_id - self.best_epoch_val) > self.early_stop_patience:
return True
else:
return False
class PerfTrackVal:
"""
Records epoch wise performance for validation
"""
def __init__(self, task, extra_param=None):
self.task = task
if task in ['cls', 'cls_trans']:
assert extra_param is None
self.all = []
self.class_seen = None
self.class_corr = None
else:
assert False
def update(self, data_batch, out):
if self.task in ['cls', 'cls_trans']:
correct = self.get_correct_list(out['logit'], data_batch['label'])
self.all.extend(correct)
self.update_class_see_corr(out['logit'], data_batch['label'])
else:
assert False
def agg(self):
if self.task in ['cls', 'cls_trans']:
perf = {
'acc': self.get_avg_list(self.all),
'class_acc': np.mean(np.array(self.class_corr) / np.array(self.class_seen,dtype=np.float))
}
else:
assert False
return perf
def update_class_see_corr(self, logit, label):
if self.class_seen is None:
num_class = logit.shape[1]
self.class_seen = [0] * num_class
self.class_corr = [0] * num_class
pred_label = logit.argmax(axis=1).to('cpu').tolist()
for _pred_label, _label in zip(pred_label, label):
self.class_seen[_label] += 1
if _pred_label == _label:
self.class_corr[_pred_label] += 1
@staticmethod
def get_correct_list(logit, label):
label = label.to(logit.device)
pred_class = logit.argmax(axis=1)
return (label == pred_class).to('cpu').tolist()
@staticmethod
def get_avg_list(all_list):
for x in all_list:
assert isinstance(x, bool)
return sum(all_list) / len(all_list)
class PerfTrackTrain(PerfTrackVal):
"""
Records epoch wise performance during training
"""
def __init__(self, task, extra_param=None):
super().__init__(task, extra_param)
# add a list to track loss
self.all_loss = []
def update_loss(self, loss):
self.all_loss.append(loss.item())
def agg_loss(self):
# print(self.all_loss)
return sum(self.all_loss) / len(self.all_loss)
def update_all(self, data_batch, out, loss):
self.update(data_batch, out)
self.update_loss(loss)
# source: https://github.com/WangYueFt/dgcnn/blob/master/pytorch/util.py
def smooth_loss(pred, gold):
eps = 0.2
n_class = pred.size(1)
one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1)
one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
log_prb = F.log_softmax(pred, dim=1)
loss = -(one_hot * log_prb).sum(dim=1).mean()
return loss
def rscnn_voting_evaluate_cls(loader, model, data_batch_to_points_target,
points_to_inp, out_to_prob, log_file):
"""
:param loader:
:param model:
:param data_batch_to_points_target:
:param points_to_inp: transform the points to input for the particular model
that is evaluated
:param out_to_prob:
:return:
"""
import rs_cnn.data.data_utils as d_utils
import pointnet2.utils.pointnet2_utils as pointnet2_utils
import numpy as np
terminal = sys.stdout
log = open(log_file, "w")
NUM_REPEAT = 300
NUM_VOTE = 10
PointcloudScale = d_utils.PointcloudScale() # initialize random scaling
def data_aug(vote_id, pc):
# furthest point sampling
# (B, npoint)
fps_idx = pointnet2_utils.furthest_point_sample(points, 1200)
new_fps_idx = fps_idx[:, np.random.choice(1200, num_points, False)]
new_points = pointnet2_utils.gather_operation(points.transpose(1, 2).contiguous(), new_fps_idx).transpose(1, 2).contiguous()
if vote_id > 0:
pc_out = PointcloudScale(new_points)
else:
pc_out = pc
return pc_out
print(f"RSCNN EVALUATE, NUM_REPEAT {NUM_REPEAT}, NUM_VOTE {NUM_VOTE}")
num_points = loader.dataset.num_points
print(f"Number of points {num_points}")
# evaluate
sys.stdout.flush()
PointcloudScale = d_utils.PointcloudScale() # initialize random scaling
model.eval()
global_acc = 0
with torch.no_grad():
for i in range(NUM_REPEAT):
preds = []
labels = []
for j, data in enumerate(loader, 0):
points, target = data_batch_to_points_target(data)
points, target = points.cuda(), target.cuda()
pred = 0
for v in range(NUM_VOTE):
new_points = data_aug(v, points)
inp = points_to_inp(new_points)
out = model(**inp)
prob = out_to_prob(out)
pred += prob
# pred += F.softmax(model(**inp), dim = 1)
pred /= NUM_VOTE
target = target.view(-1)
_, pred_choice = torch.max(pred.data, -1)
preds.append(pred_choice)
labels.append(target.data)
preds = torch.cat(preds, 0)
labels = torch.cat(labels, 0)
acc = (preds == labels).sum().float() / labels.numel()
if acc > global_acc:
global_acc = acc
message1 = 'Repeat %3d \t Acc: %0.6f' % (i + 1, acc)
message2 = '\nBest voting till now, acc: %0.6f' % (global_acc)
message = f'{message1} \n {message2}'
terminal.write(message)
log.write(message)
message = '\nBest voting acc: %0.6f' % (global_acc)
terminal.write(message)
log.write(message)
return global_acc
# https://github.com/charlesq34/pointnet2/blob/master/evaluate.py
# https://github.com/charlesq34/pointnet2/issues/8
# we try to keep the variables names similar to the original implementation
def pn2_vote_evaluate_cls(dataloader, model, log_file, num_votes=[12]):
from pointnet2_tf.utils import provider
model.eval()
terminal = sys.stdout
log = open(log_file, "w")
if isinstance(num_votes, list):
pass
else:
num_votes = [num_votes]
for _num_votes in num_votes:
print(f"num_votes: {_num_votes}")
NUM_CLASSES = DATASET_NUM_CLASS[dataloader.dataset.dataset_name]
SHAPE_NAMES = [line.rstrip() for line in
open('./data/modelnet40_ply_hdf5_2048/shape_names.txt')]
total_correct = 0
total_seen = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
with torch.no_grad():
for _batch_data in dataloader:
# based on https://github.com/charlesq34/pointnet2/blob/master/evaluate.py#L125-L150
batch_data, batch_label = np.array(_batch_data['pc'].cpu()), np.array(_batch_data['label'].cpu())
bsize = batch_data.shape[0]
BATCH_SIZE = batch_data.shape[0]
NUM_POINT = batch_data.shape[1]
batch_pred_sum = np.zeros((BATCH_SIZE, NUM_CLASSES)) # score for classes
for vote_idx in range(_num_votes):
# Shuffle point order to achieve different farthest samplings
shuffled_indices = np.arange(NUM_POINT)
np.random.shuffle(shuffled_indices)
rotated_data = provider.rotate_point_cloud_by_angle(
batch_data[:, shuffled_indices, :], vote_idx/float(_num_votes) * np.pi * 2)
inp = {'pc': torch.tensor(rotated_data)}
out = model(**inp)
pred_val = np.array(out['logit'].cpu())
batch_pred_sum += pred_val
pred_val = np.argmax(batch_pred_sum, 1)
correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize])
total_correct += correct
total_seen += bsize
for i in range(bsize):
l = batch_label[i]
total_seen_class[l] += 1
total_correct_class[l] += (pred_val[i] == l)
class_accuracies = np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)
message = ""
for i, name in enumerate(SHAPE_NAMES):
message += f"\n {'%10s: %0.3f' % (name, class_accuracies[i])}"
message += f"\n {'eval accuracy: %f'% (total_correct / float(total_seen))}"
message += f"\n {'eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)))}"
terminal.write(message)
log.write(message)