def loss_fn(self, out, annot):
tar_vector = Losses.get_tar_vector(annot)
loss_loc = Losses.get_loc_error(out, tar_vector)
loss_wh = Losses.get_w_h_error(out, tar_vector)
loss_conf = Losses.get_confidence_error(out, tar_vector)
loss_cls = Losses.get_class_error(out, tar_vector)
return loss_loc, loss_wh, loss_conf, loss_cls
def transfer(model, decoder, sess, args, vocab, data0, data1, out_path):
batches, order0, order1 = get_batches(data0, data1, vocab.word2id,
args.batch_size)
data0_tsf, data1_tsf = [], []
losses = Losses(len(batches))
for batch in batches:
ori, tsf = decoder.rewrite(batch)
half = batch['size'] / 2
data0_tsf += tsf[:half]
data1_tsf += tsf[half:]
loss, loss_g, loss_d, loss_d0, loss_d1 = sess.run(
[
model.loss, model.loss_g, model.loss_d, model.loss_d0,
model.loss_d1
],
feed_dict=feed_dictionary(model, batch, args.rho, args.gamma_min))
losses.add(loss, loss_g, loss_d, loss_d0, loss_d1)
n0, n1 = len(data0), len(data1)
data0_tsf = reorder(order0, data0_tsf)[:n0]
data1_tsf = reorder(order1, data1_tsf)[:n1]
if out_path:
write_sent(data0_tsf, out_path + '.0' + '.tsf')
write_sent(data1_tsf, out_path + '.1' + '.tsf')
return losses
def __init__(self,
model,
optimizer,
train_loader,
test_loader,
args,
epoch=-1,
global_step=0,
test_mode=False):
if args.fp16:
try:
from apex import amp
global amp
amp = amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
self.model = model
self.args = args
self.optimizer = optimizer
self.train_loader = train_loader
self.test_loader = test_loader
self.epoch = epoch
self.module = model.module if hasattr(
model, 'module') else model # for data parallel
self.masking_policies = [
'random', 'seen_noun', 'seen_verb',
'seen_combo_seen_noun_seen_verb', 'new_noun', 'new_verb',
'new_combo_seen_noun_seen_verb', 'new_combo_new_noun_new_verb',
'seen_combo_seen_noun_seen_verb_merge',
'new_combo_seen_noun_seen_verb_merge',
'new_combo_new_noun_new_verb_merge'
]
if test_mode and not args.pointing:
self.masker = TestMasker(annotation_root=args.annotation_root,
masking_policy=args.test_masking_policy,
tok=self.train_loader.dataset.tokenizer,
p_mask_img=args.p_mask_img,
p_mask_txt=args.p_mask_txt)
else:
self.masker = Masker(self.train_loader.dataset.tokenizer,
**vars(args))
self.losses = Losses(self.module.cfg, args, **vars(args))
self.global_step = global_step
def transfer(model, sess, args, vocab, data0, data1, out_path):
batches, order0, order1 = get_batches(data0, data1, vocab.word2id,
args.batch_size)
data0_tsf, data1_tsf = [], []
losses = Losses(len(batches))
for batch in batches:
ori, tsf, loss, loss_g, loss_d, loss_d0, loss_d1 = rewrite(
model, sess, args, vocab, batch)
half = batch['size'] / 2
data0_tsf += tsf[:half]
data1_tsf += tsf[half:]
losses.add(loss, loss_g, loss_d, loss_d0, loss_d1)
n0, n1 = len(data0), len(data1)
data0_tsf = reorder(order0, data0_tsf)[:n0]
data1_tsf = reorder(order1, data1_tsf)[:n1]
if out_path:
write_sent(data0_tsf, out_path + '.0' + '.tsf')
write_sent(data1_tsf, out_path + '.1' + '.tsf')
return losses
class Trainer:
""" Class implementing the trainer for the project """
def __init__(self,
model,
optimizer,
train_loader,
test_loader,
args,
epoch=-1,
global_step=0,
test_mode=False):
if args.fp16:
try:
from apex import amp
global amp
amp = amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
self.model = model
self.args = args
self.optimizer = optimizer
self.train_loader = train_loader
self.test_loader = test_loader
self.epoch = epoch
self.module = model.module if hasattr(
model, 'module') else model # for data parallel
self.masking_policies = [
'random', 'seen_noun', 'seen_verb',
'seen_combo_seen_noun_seen_verb', 'new_noun', 'new_verb',
'new_combo_seen_noun_seen_verb', 'new_combo_new_noun_new_verb',
'seen_combo_seen_noun_seen_verb_merge',
'new_combo_seen_noun_seen_verb_merge',
'new_combo_new_noun_new_verb_merge'
]
if test_mode and not args.pointing:
self.masker = TestMasker(annotation_root=args.annotation_root,
masking_policy=args.test_masking_policy,
tok=self.train_loader.dataset.tokenizer,
p_mask_img=args.p_mask_img,
p_mask_txt=args.p_mask_txt)
else:
self.masker = Masker(self.train_loader.dataset.tokenizer,
**vars(args))
self.losses = Losses(self.module.cfg, args, **vars(args))
self.global_step = global_step
def train(self):
best_eval = 0
try:
for epoch in trange(self.epoch + 1,
self.args.num_train_epochs,
desc='Training model'):
if self.args.local_rank != -1:
self.train_loader.sampler.set_epoch(epoch)
self.run_epoch(epoch)
# Evaluate on validation set
# The last one is the one that we take into account for the checkpoints
val_score = self.run_epoch(epoch, train=False)
# Remember best eval score and save checkpoint
is_best = val_score > best_eval
best_eval = max(val_score, best_eval)
if self.args.local_rank <= 0 and not self.args.debug:
print('Saving checkpoint')
utils.save_checkpoint(self.model,
self.optimizer,
self.train_loader.dataset.tokenizer,
is_best,
epoch,
self.args.checkpoint_dir,
amp=amp,
global_step=self.global_step,
args=self.args)
except KeyboardInterrupt:
if self.args.local_rank <= 0:
print(f'You decided to finish the training at epoch {epoch}')
def run_epoch(self, epoch, train=True):
"""
During the training loop, we find the following arrays:
- text_mask_locs:
Tensor of size B x T, T being the maximum of all the B T's. Each element contains a boolean tensor that contains
True if the token at that position MUST be masked. This will depend on the `target_token_ids` and whether or not
the token at position belongs to the target sequence. This masking means that the specific token will be
predicted (true?) in all the text losses (language model, pointing, episodic), but will not necessarily be
substituted by a [MASK] token, as this is random and sometimes it stays the same or is substuted by a random
word.
- text_no_mask_locs:
Tensor of size B x T, each element containing a boolean tensor that contains True if in that position the token
CANNOT be masked.
- img_no_mask_locs similarly.
"""
torch.cuda.synchronize()
# Initialize meters
avg_batch_time = utils.AverageMeter()
avg_data_time = utils.AverageMeter()
list_losses = ['total', 'lm', 'vm']
list_losses.extend(['pointing'] if self.args.pointing else [])
list_losses.extend(
['input_pointing'] if self.args.input_pointing else [])
average_meters = defaultdict(lambda: utils.AverageMeter())
if not train:
avg_lm_top1 = utils.AverageMeter()
avg_lm_top5 = utils.AverageMeter()
avg_pointing_acc = utils.AverageMeter()
avg_input_pointing_acc = utils.AverageMeter()
# Switch to train mode
if train:
self.model.train()
else:
self.model.eval()
end = time.time()
with torch.set_grad_enabled(train), \
tqdm(self.train_loader if train else self.test_loader,
desc=f'Training epoch {epoch}' if train else f'Validating {f"epoch {epoch}" if epoch else ""}',
disable=self.args.local_rank > 0) as t:
for batch_idx, data in enumerate(t):
# Measure data loading time
avg_data_time.update(time.time() - end)
# -------------- Organize inputs ------------- #
img_no_mask_locs = None
text_no_mask_locs = None
text_mask_locs = None
with torch.no_grad():
if self.args.pointing:
text_mask_locs, text_no_mask_locs = masker.gen_pointing_text_mask_locs(
data)
imgs, vm_labels, neg_vm_labels = self.masker.mask_imgs(
data['imgs'].cuda(), no_mask_locs=img_no_mask_locs)
# Note that this does not mask sep tokens
text, lm_labels, input_pointing_labels = \
self.masker.mask_text(data['text'].cuda(), self.args.input_pointing, no_mask_locs=text_no_mask_locs,
mask_locs=text_mask_locs, **data)
img_bboxes = data['img_bboxes'].cuda()
imgs_len = data['imgs_len'].cuda()
text_len = data['text_len'].cuda()
img_locs = txt_locs = None
if self.args.pointing:
attn_mask, img_locs, txt_locs = masker.attn_mask_pointing(
imgs_len, text_len, data['seq_type'],
data['num_seqs'].cuda(), self.args.attn_masking)
# The input to the model is:
# imgs = [[img0, img1, ..., imgN1, PAD, ..., PAD], [...], [[img0, img1, ..., imgNk, PAD, ..., PAD]]]
# where the padding is such that all K in the batch have the same total lenght (minimal padding)
# The N images include all the images from all the sequences, concatenated. Only padding at the end
else:
img_attn_mask = \
torch.arange(self.args.max_img_seq_len, device=imgs.device)[None, :] < imgs_len[:, None]
text_attn_mask = \
torch.arange(self.args.max_txt_seq_len, device=imgs.device)[None, :] < text_len[:, None]
attn_mask = torch.cat(
(text_attn_mask[:, :1], img_attn_mask,
text_attn_mask[:, 1:]),
dim=1)
# text starts with [IMG] token that gets moved to beginning of input in forward pass
# -------------- Forward pass ---------------- #
lm_preds, vm_preds, input_pointing_pred, hidden_states, *_ = \
self.model(imgs, text, img_bboxes, attention_mask=attn_mask, img_lens=imgs_len,
txt_lens=text_len, img_locs=img_locs, txt_locs=txt_locs)
# -------------- Compute losses -------------- #
loss_values = {}
if self.args.pointing:
non_padding_text = (torch.arange(
text.shape[1], device=text.device)[None, :] <
text_len.cumsum(dim=1)[:, -1][:, None])
non_padding_imgs = (torch.arange(
imgs.shape[1], device=imgs.device)[None, :] <
imgs_len.cumsum(dim=1)[:, -1][:, None])
loss_values['lm'] = self.losses.lm_loss(
lm_preds, lm_labels[non_padding_text])
loss_values['vm'] = self.losses.vm_loss(
vm_preds,
vm_labels[non_padding_imgs],
neg_vm_labels[non_padding_imgs],
embedder=self.module.embeddings.img_embeddings)
else:
loss_values['lm'] = self.losses.lm_loss(
lm_preds, lm_labels)
loss_values['vm'] = self.losses.vm_loss(
vm_preds,
vm_labels,
neg_vm_labels,
embedder=self.module.embeddings.img_embeddings)
loss = self.args.lm_loss_lambda * loss_values[
'lm'] + self.args.vm_loss_lambda * loss_values['vm']
if self.args.pointing:
pointing_loss, (pointing_acc, pointing_cnt) = \
self.losses.pointing_loss(data, hidden_states, lm_labels, text, text_len, txt_locs)
loss_values['pointing'] = pointing_loss
loss += self.args.pointing_loss_lambda * loss_values[
'pointing']
if self.args.input_pointing:
input_pointing_loss, (input_pointing_acc, input_pointing_cnt), *_ = \
self.losses.input_pointing_pointing_loss(
input_pointing_pred[0], input_pointing_pred[1],
input_pointing_labels, txt_locs, lm_labels,
data=data, log=True)
loss_values['input_pointing'] = input_pointing_loss
loss += self.args.input_pointing_loss_lambda * loss_values[
'input_pointing']
if self.args.n_gpu > 1:
loss = loss.mean()
loss_values['total'] = loss
# --------------- Update model -------------- #
if train:
if self.args.fp16:
with amp.scale_loss(loss,
self.optimizer) as scaled_loss:
scaled_loss.backward()
else:
(loss /
self.args.gradient_accumulation_steps).backward()
if (batch_idx +
1) % self.args.gradient_accumulation_steps == 0:
for loss_name in list_losses: # Record losses
average_meters[loss_name].update(
loss_values[loss_name].item() /
self.args.gradient_accumulation_steps,
imgs.size(0))
if train:
if self.args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(self.optimizer),
self.args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(
self.model.parameters(),
self.args.max_grad_norm)
self.optimizer.step()
# scheduler.step() # no scheduler for now
self.model.zero_grad()
# Measure elapsed time
avg_batch_time.update(time.time() - end)
end = time.time()
# ------------- Show information ------------ #
postfix_kwargs = {}
if not train:
if self.args.pointing:
lm_labels = lm_labels[non_padding_text]
avg_pointing_acc.update(pointing_acc, pointing_cnt)
postfix_kwargs['PointingAcc'] = avg_pointing_acc.avg
if self.args.input_pointing:
avg_input_pointing_acc.update(
input_pointing_acc, input_pointing_cnt)
postfix_kwargs[
'input_pointingAcc'] = avg_input_pointing_acc.avg
results = tests.accuracy(lm_preds, lm_labels, topk=(1, 5))
avg_lm_top1.update(*results['top1'])
avg_lm_top5.update(*results['top5'])
postfix_kwargs['LMTop1'] = avg_lm_top1.avg
postfix_kwargs['LMTop5'] = avg_lm_top5.avg
for loss_name in list_losses:
postfix_kwargs[loss_name] = average_meters[loss_name].avg
t.set_postfix(DataTime=avg_data_time.avg,
BatchTime=avg_batch_time.avg,
**postfix_kwargs)
if train:
if self.global_step % self.args.print_freq == 0 and self.args.writer and not self.args.debug:
self.args.writer.add_scalars(
'train/loss', {**postfix_kwargs},
self.global_step * self.args.train_batch_size *
self.args.step_n_gpus)
self.global_step += 1
if not train:
cnt = average_meters['total'].count
if epoch is not None:
loss_scalars = {}
for loss_name in list_losses:
loss_scalars[loss_name] = utils.gather_score(
average_meters[loss_name].avg, cnt)
acc_scalars = {
'lm_top1': utils.gather_score(avg_lm_top1.avg, cnt),
'lm_top5': utils.gather_score(avg_lm_top5.avg, cnt)
}
if self.args.pointing:
acc_scalars['pointing_acc'] = utils.gather_score(
avg_pointing_acc.avg, cnt)
if self.args.input_pointing:
acc_scalars['input_pointing_acc'] = utils.gather_score(
avg_input_pointing_acc.avg, cnt)
if self.args.writer and not self.args.debug:
self.args.writer.add_scalars('val/loss', loss_scalars,
epoch)
self.args.writer.add_scalars('val/acc', acc_scalars, epoch)
return utils.gather_score(avg_lm_top5.avg, cnt)
def test(self, masking_policy=None):
torch.cuda.synchronize()
if masking_policy == 'all_acc_tests':
for p in self.masking_policies:
self.test(p)
else:
tests.test_accuracy(self, masking_policy)
with tf.Session(config=config) as sess:
model = create_model(sess, args, vocab)
if args.beam > 1:
decoder = beam_search.Decoder(sess, args, vocab, model)
else:
decoder = greedy_decoding.Decoder(sess, args, vocab, model)
if args.train:
batches, _, _ = get_batches(train0, train1, vocab.word2id,
args.batch_size)
random.shuffle(batches)
start_time = time.time()
step = 0
losses = Losses(args.steps_per_checkpoint)
best_dev = float('inf')
learning_rate = args.learning_rate
rho = args.rho
gamma = args.gamma_init
dropout = args.dropout_keep_prob
for epoch in range(1, 1 + args.max_epochs):
print '--------------------epoch %d--------------------' % epoch
print 'learning_rate:', learning_rate, ' gamma:', gamma
for batch in batches:
feed_dict = feed_dictionary(model, batch, rho, gamma,
dropout, learning_rate)
loss_d0, _ = sess.run([model.loss_d0, model.optimizer_d0],
some_target_for_loc_error = {
(3, 3):
[[0, [1, 1], [torch.Tensor([0.1]).cuda(),
torch.Tensor([0.1]).cuda()]]]
}
some_predictions_for_loc_error = torch.zeros((1, cell_num, cell_num, 30))
for y in range(cell_num):
for x in range(cell_num):
some_predictions_for_loc_error[0, y, x, :] = torch.Tensor([
0.2, 0.2, 0, 0, 0, 0.2, 0.2, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1
])
unittest.TestCase().assertAlmostEqual(
0.01,
Losses.get_loc_error(some_predictions_for_loc_error,
some_target_for_loc_error).cpu().detach().numpy()[0],
2)
some_targets_class_prob_exist = {
(3, 3): [[1, [0, 0], [torch.Tensor([0]).cuda(),
torch.Tensor([0]).cuda()]]]
}
some_predictions_class_prob_exist = torch.zeros((1, cell_num, cell_num, 30))
for y in range(cell_num):
for x in range(cell_num):
if y == 3 and x == 3:
some_predictions_class_prob_exist[0, y, x, :] = torch.Tensor([
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 0.2, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0
])
else:
def train(**args):
"""
Evaluate selected model
Args:
rerun (Int): Integer indicating number of repetitions for the select experiment
seed (Int): Integer indicating set seed for random state
save_dir (String): Top level directory to generate results folder
model (String): Name of selected model
dataset (String): Name of selected dataset
exp (String): Name of experiment
debug (Int): Debug state to avoid saving variables
load_type (String): Keyword indicator to evaluate the testing or validation set
pretrained (Int/String): Int/String indicating loading of random, pretrained or saved weights
opt (String): Int/String indicating loading of random, pretrained or saved weights
lr (Float): Learning rate
momentum (Float): Momentum in optimizer
weight_decay (Float): Weight_decay value
final_shape ([Int, Int]): Shape of data when passed into network
Return:
None
"""
print(
"\n############################################################################\n"
)
print("Experimental Setup: ", args)
print(
"\n############################################################################\n"
)
for total_iteration in range(args['rerun']):
# Generate Results Directory
d = datetime.datetime.today()
date = d.strftime('%Y%m%d-%H%M%S')
result_dir = os.path.join(
args['save_dir'], args['model'], '_'.join(
(args['dataset'], args['exp'], date)))
log_dir = os.path.join(result_dir, 'logs')
save_dir = os.path.join(result_dir, 'checkpoints')
if not args['debug']:
os.makedirs(result_dir, exist_ok=True)
os.makedirs(log_dir, exist_ok=True)
os.makedirs(save_dir, exist_ok=True)
# Save copy of config file
with open(os.path.join(result_dir, 'config.yaml'), 'w') as outfile:
yaml.dump(args, outfile, default_flow_style=False)
# Tensorboard Element
writer = SummaryWriter(log_dir)
# Check if GPU is available (CUDA)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Load Network
model = create_model_object(**args).to(device)
# Load Data
loader = data_loader(model_obj=model, **args)
if args['load_type'] == 'train':
train_loader = loader['train']
valid_loader = loader[
'train'] # Run accuracy on train data if only `train` selected
elif args['load_type'] == 'train_val':
train_loader = loader['train']
valid_loader = loader['valid']
else:
sys.exit('Invalid environment selection for training, exiting')
# END IF
# Training Setup
params = [p for p in model.parameters() if p.requires_grad]
if args['opt'] == 'sgd':
optimizer = optim.SGD(params,
lr=args['lr'],
momentum=args['momentum'],
weight_decay=args['weight_decay'])
elif args['opt'] == 'adam':
optimizer = optim.Adam(params,
lr=args['lr'],
weight_decay=args['weight_decay'])
else:
sys.exit('Unsupported optimizer selected. Exiting')
# END IF
scheduler = MultiStepLR(optimizer,
milestones=args['milestones'],
gamma=args['gamma'])
if isinstance(args['pretrained'], str):
ckpt = load_checkpoint(args['pretrained'])
model.load_state_dict(ckpt)
start_epoch = load_checkpoint(args['pretrained'],
key_name='epoch') + 1
optimizer.load_state_dict(
load_checkpoint(args['pretrained'], key_name='optimizer'))
for quick_looper in range(start_epoch):
scheduler.step()
# END FOR
else:
start_epoch = 0
# END IF
model_loss = Losses(device=device, **args)
acc_metric = Metrics(**args)
best_val_acc = 0.0
############################################################################################################################################################################
# Start: Training Loop
for epoch in range(start_epoch, args['epoch']):
running_loss = 0.0
print('Epoch: ', epoch)
# Setup Model To Train
model.train()
# Start: Epoch
for step, data in enumerate(train_loader):
if step % args['pseudo_batch_loop'] == 0:
loss = 0.0
optimizer.zero_grad()
# END IF
x_input = data['data'].to(device)
annotations = data['annots']
assert args['final_shape'] == list(x_input.size(
)[-2:]), "Input to model does not match final_shape argument"
outputs = model(x_input)
loss = model_loss.loss(outputs, annotations)
loss = loss * args['batch_size']
loss.backward()
running_loss += loss.item()
if np.isnan(running_loss):
import pdb
pdb.set_trace()
# END IF
if not args['debug']:
# Add Learning Rate Element
for param_group in optimizer.param_groups:
writer.add_scalar(
args['dataset'] + '/' + args['model'] +
'/learning_rate', param_group['lr'],
epoch * len(train_loader) + step)
# END FOR
# Add Loss Element
writer.add_scalar(
args['dataset'] + '/' + args['model'] +
'/minibatch_loss',
loss.item() / args['batch_size'],
epoch * len(train_loader) + step)
# END IF
if ((epoch * len(train_loader) + step + 1) % 100 == 0):
print('Epoch: {}/{}, step: {}/{} | train loss: {:.4f}'.
format(
epoch, args['epoch'], step + 1,
len(train_loader), running_loss /
float(step + 1) / args['batch_size']))
# END IF
if (epoch * len(train_loader) +
(step + 1)) % args['pseudo_batch_loop'] == 0 and step > 0:
# Apply large mini-batch normalization
for param in model.parameters():
param.grad *= 1. / float(
args['pseudo_batch_loop'] * args['batch_size'])
optimizer.step()
# END IF
# END FOR: Epoch
if not args['debug']:
# Save Current Model
save_path = os.path.join(
save_dir, args['dataset'] + '_epoch' + str(epoch) + '.pkl')
save_checkpoint(epoch, step, model, optimizer, save_path)
# END IF: Debug
scheduler.step(epoch=epoch)
print('Schedulers lr: %f', scheduler.get_lr()[0])
## START FOR: Validation Accuracy
running_acc = []
running_acc = valid(valid_loader, running_acc, model, device,
acc_metric)
if not args['debug']:
writer.add_scalar(
args['dataset'] + '/' + args['model'] +
'/validation_accuracy', 100. * running_acc[-1],
epoch * len(valid_loader) + step)
print('Accuracy of the network on the validation set: %f %%\n' %
(100. * running_acc[-1]))
# Save Best Validation Accuracy Model Separately
if best_val_acc < running_acc[-1]:
best_val_acc = running_acc[-1]
if not args['debug']:
# Save Current Model
save_path = os.path.join(
save_dir, args['dataset'] + '_best_model.pkl')
save_checkpoint(epoch, step, model, optimizer, save_path)
# END IF
# END IF
# END FOR: Training Loop
############################################################################################################################################################################
if not args['debug']:
# Close Tensorboard Element
writer.close()
def __init__(
self,
seed=None,
optimizer=Adam,
optimizer_kwargs={},
learning_rate_init=0.04,
gamma=0.995, # learning rate decay factor
considered_groups=list(
range(12)), ## group layers to be considered from start
sample_variance_threshold=0.002,
weight_loss_sample_variance=0, # 10.
evaluation_steps=250, # number of batches between loss tracking
N_batches_test=1, # number of batches considered for evaluation
):
super(ImageClassifier,
self).__init__(considered_groups=considered_groups)
if seed is not None:
torch.manual_seed(seed)
#'''
resnet = models.resnet18(pretrained=False)
self.conv = Sequential(
*(list(resnet.children())[:-1]),
Flatten(),
)
''' architecture used by Dielemann et al 2015
self.conv = Sequential(
# Conv2dUntiedBias(41, 41, 3, 32, kernel_size=6),
Conv2d(3,32, kernel_size=6),
ReLU(),
MaxPool2d(2),
# Conv2dUntiedBias(16, 16, 32, 64, kernel_size=5),
Conv2d(32, 64, kernel_size=5),
ReLU(),
MaxPool2d(2),
# Conv2dUntiedBias(6, 6, 64, 128, kernel_size=3),
Conv2d(64, 128, kernel_size=3),
ReLU(),
# Conv2dUntiedBias(4, 4, 128, 128, kernel_size=3), #weight_std=0.1),
Conv2d(128, 128, kernel_size=3),
ReLU(),
MaxPool2d(2),
Flatten(),
)
#'''
self.dense1 = MaxOut(8192, 2048, bias=0.01)
self.dense2 = MaxOut(2048, 2048, bias=0.01)
self.dense3 = Sequential(
MaxOut(2048, 37, bias=0.1),
# LeakyReLU(negative_slope=1e-7),
ALReLU(negative_slope=1e-2),
)
self.dropout = Dropout(p=0.5)
self.augment = Compose([
Lambda(lambda img: torch.cat([img, hflip(img)], 0)),
Lambda(lambda img: torch.cat([img, rotate(img, 45)], 0)),
FiveCrop(45),
Lambda(lambda crops: torch.cat([
rotate(crop, ang)
for crop, ang in zip(crops, (0, 90, 270, 180))
], 0)),
])
self.N_augmentations = 16
self.N_conv_outputs = 512
self.set_optimizer(optimizer,
lr=learning_rate_init,
**optimizer_kwargs)
# self.scheduler = ExponentialLR(self.optimizer, gamma=gamma)
self.scheduler = MultiStepLR(self.optimizer,
milestones=[292, 373],
gamma=gamma)
self.make_labels_hierarchical = False # if True, output probabilities are renormalized to fit the hierarchical label structure
self.N_batches_test = N_batches_test
self.evaluation_steps = evaluation_steps # number of batches between loss tracking
self.weight_loss_sample_variance = weight_loss_sample_variance
self.sample_variance_threshold = sample_variance_threshold
self.iteration = 0
self.epoch = 0
self.losses_train = Losses("loss", "train")
self.losses_valid = Losses("loss", "valid")
self.sample_variances_train = Losses("sample variance", "train")
self.sample_variances_valid = Losses("sample variance", "valid")
for g in range(1, 12):
setattr(self, f"accuracies_Q{g}_train",
Accuracies("accuracy train", f"Q{g}"))
setattr(self, f"accuracies_Q{g}_valid",
Accuracies("accuracy valid", f"Q{g}"))
self.losses_regression = Losses("loss", "regression")
self.losses_variance = Losses("loss", "sample variance")
## return to random seed
if seed is not None:
sd = np.random.random() * 10000
torch.manual_seed(sd)
class ImageClassifier(ClassifierBase):
""" model for morphological classification of galaxy images
Usage
-----
to use pretrained model, do
>>> classifier = ImageClassifier()
>>> classifier.load()
>>> classifier.eval()
>>> classifier.use_label_hierarchy()
>>> labels = classifier(images)
"""
def __init__(
self,
seed=None,
optimizer=Adam,
optimizer_kwargs={},
learning_rate_init=0.04,
gamma=0.995, # learning rate decay factor
considered_groups=list(
range(12)), ## group layers to be considered from start
sample_variance_threshold=0.002,
weight_loss_sample_variance=0, # 10.
evaluation_steps=250, # number of batches between loss tracking
N_batches_test=1, # number of batches considered for evaluation
):
super(ImageClassifier,
self).__init__(considered_groups=considered_groups)
if seed is not None:
torch.manual_seed(seed)
#'''
resnet = models.resnet18(pretrained=False)
self.conv = Sequential(
*(list(resnet.children())[:-1]),
Flatten(),
)
''' architecture used by Dielemann et al 2015
self.conv = Sequential(
# Conv2dUntiedBias(41, 41, 3, 32, kernel_size=6),
Conv2d(3,32, kernel_size=6),
ReLU(),
MaxPool2d(2),
# Conv2dUntiedBias(16, 16, 32, 64, kernel_size=5),
Conv2d(32, 64, kernel_size=5),
ReLU(),
MaxPool2d(2),
# Conv2dUntiedBias(6, 6, 64, 128, kernel_size=3),
Conv2d(64, 128, kernel_size=3),
ReLU(),
# Conv2dUntiedBias(4, 4, 128, 128, kernel_size=3), #weight_std=0.1),
Conv2d(128, 128, kernel_size=3),
ReLU(),
MaxPool2d(2),
Flatten(),
)
#'''
self.dense1 = MaxOut(8192, 2048, bias=0.01)
self.dense2 = MaxOut(2048, 2048, bias=0.01)
self.dense3 = Sequential(
MaxOut(2048, 37, bias=0.1),
# LeakyReLU(negative_slope=1e-7),
ALReLU(negative_slope=1e-2),
)
self.dropout = Dropout(p=0.5)
self.augment = Compose([
Lambda(lambda img: torch.cat([img, hflip(img)], 0)),
Lambda(lambda img: torch.cat([img, rotate(img, 45)], 0)),
FiveCrop(45),
Lambda(lambda crops: torch.cat([
rotate(crop, ang)
for crop, ang in zip(crops, (0, 90, 270, 180))
], 0)),
])
self.N_augmentations = 16
self.N_conv_outputs = 512
self.set_optimizer(optimizer,
lr=learning_rate_init,
**optimizer_kwargs)
# self.scheduler = ExponentialLR(self.optimizer, gamma=gamma)
self.scheduler = MultiStepLR(self.optimizer,
milestones=[292, 373],
gamma=gamma)
self.make_labels_hierarchical = False # if True, output probabilities are renormalized to fit the hierarchical label structure
self.N_batches_test = N_batches_test
self.evaluation_steps = evaluation_steps # number of batches between loss tracking
self.weight_loss_sample_variance = weight_loss_sample_variance
self.sample_variance_threshold = sample_variance_threshold
self.iteration = 0
self.epoch = 0
self.losses_train = Losses("loss", "train")
self.losses_valid = Losses("loss", "valid")
self.sample_variances_train = Losses("sample variance", "train")
self.sample_variances_valid = Losses("sample variance", "valid")
for g in range(1, 12):
setattr(self, f"accuracies_Q{g}_train",
Accuracies("accuracy train", f"Q{g}"))
setattr(self, f"accuracies_Q{g}_valid",
Accuracies("accuracy valid", f"Q{g}"))
self.losses_regression = Losses("loss", "regression")
self.losses_variance = Losses("loss", "sample variance")
## return to random seed
if seed is not None:
sd = np.random.random() * 10000
torch.manual_seed(sd)
def update_optimizer(self, **kwargs) -> None:
self.set_optimizer(optimizer, **kwargs)
def update_optimizer_learningrate(self, learning_rate) -> None:
print("update lr", learning_rate)
for i in range(len(self.optimizer.param_groups)):
self.optimizer.param_groups[i]['lr'] = learning_rate
def use_label_hierarchy(self) -> None:
self.make_labels_hierarchical = True
def forward(self, x: torch.Tensor, train=False) -> torch.Tensor:
x = self.augment(x)
x = self.conv(x)
x = self.recombine_augmentation(x)
x = self.dropout(x)
x = self.dense1(x)
x = self.dropout(x)
x = self.dense2(x)
x = self.dropout(x)
x = self.dense3(x)
# x += 1e-4 ## use only with LeakyReLU to prevent values < 0
if self.make_labels_hierarchical:
x = make_galaxy_labels_hierarchical(x)
return x
def recombine_augmentation(self, x) -> torch.Tensor:
""" recombine results of augmented views to single vector """
batch_size = x.size(0) // self.N_augmentations
x = x.reshape(self.N_augmentations, batch_size, self.N_conv_outputs)
x = x.permute(1, 0, 2)
x = x.reshape(batch_size, self.N_augmentations * self.N_conv_outputs)
return x
def train_step(self, images: torch.tensor, labels: torch.tensor) -> float:
self.train()
labels_pred = self.forward(images, train=True)
loss_regression = mse(labels_pred[:, self.considered_label_indices],
labels[:, self.considered_label_indices])
loss_variance = self.weight_loss_sample_variance * \
loss_sample_variance(labels_pred[:,self.considered_label_indices],
threshold=self.sample_variance_threshold)
loss = loss_regression + loss_variance
self.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.parameters(), max_norm=1)
self.optimizer.step()
self.iteration += 1
return loss.item()
def train_epoch(
self,
data_loader_train: torch.utils.data.DataLoader,
data_loader_valid: torch.utils.data.DataLoader,
track: bool = False,
) -> None:
for images, labels in tqdm(data_loader_train,
desc=f"epoch {self.epoch}"):
images = images.to(device)
labels = labels.to(device)
loss = self.train_step(images, labels)
if np.isnan(loss):
from pdb import set_trace
set_trace()
loss = self.train_step(images, labels)
raise Exception("loss is NaN")
if not self.iteration % self.evaluation_steps - 1:
loss_regression_train, loss_variance_train, accs_train, variance_train = self.evaluate_batch(
images, labels, print_labels=False)
loss_train = loss_regression_train + loss_variance_train * self.weight_loss_sample_variance
self.losses_regression.append(self.iteration,
loss_regression_train)
self.losses_variance.append(self.iteration,
loss_variance_train)
self.losses_train.append(self.iteration, loss_train)
self.sample_variances_train.append(self.iteration,
variance_train)
for group, acc in accs_train.items():
getattr(self, f"accuracies_Q{group}_train").append(
self.iteration, acc)
for images, labels in data_loader_valid:
images = images.to(device)
labels = labels.to(device)
break
loss_regression_valid, loss_variance_valid, accs_valid, variance_valid = self.evaluate_batch(
images, labels)
loss_valid = loss_regression_valid + loss_variance_valid * self.weight_loss_sample_variance
self.losses_valid.append(self.iteration, loss_valid)
self.sample_variances_valid.append(self.iteration,
variance_valid)
for group, acc in accs_valid.items():
getattr(self, f"accuracies_Q{group}_valid").append(
self.iteration, acc)
if track:
import wandb
logs = {
"loss_regression_train": loss_regression_train,
"loss_variance_train": loss_variance_train,
"loss_train": loss_train,
"variance_train": variance_train,
"loss_regression_valid": loss_regression_valid,
"loss_variance_valid": loss_variance_valid,
"loss_valid": loss_valid,
"variance_valid": variance_valid,
}
logs.update({
f"accuracy_Q{group}_train": acc
for group, acc in accs_train.items()
})
logs.update({
f"accuracy_Q{group}_valid": acc
for group, acc in accs_valid.items()
})
wandb.log(logs)
self.epoch += 1
self.scheduler.step()
self.save()
def predict(self, images: torch.tensor) -> torch.Tensor:
self.eval()
return self(images)
def evaluate_batches(self,
data_loader: torch.utils.data.DataLoader) -> list:
with torch.no_grad():
loss = 0
accs = Counter({group: 0 for group in range(1, 12)})
variance = 0
for N_test, (images, labels) in enumerate(data_loader):
images = images.to(device)
labels = labels.to(device)
if N_test >= self.N_batches_test:
break
loss_, accs_, variance_ = self.evaluate_batch(images, labels)
loss += loss_
accs.update(accs_)
variance += variance_
loss /= N_test + 1
variance /= N_test + 1
for group in accs.keys():
accs[group] /= N_test + 1
return loss, accs, variance
def evaluate_batch(self,
images: torch.tensor,
labels: torch.tensor,
print_labels=False) -> tuple:
""" evaluations for batch """
self.eval()
with torch.no_grad():
labels_pred = self.forward(images)
if print_labels:
for i, (prediction,
target) in enumerate(zip(labels_pred, labels)):
print(
"target\t\t",
np.around(target[self.considered_label_indices].cpu(),
3))
print("\033[1mprediction\t",
np.around(
prediction[self.considered_label_indices].cpu(),
3),
end="\033[0m\n")
if i >= 2:
break
print(
"<target>\t",
np.around(
torch.mean(labels[:, self.considered_label_indices],
dim=0).cpu(), 3))
print(
"<target>\t",
np.around(
torch.std(labels[:, self.considered_label_indices],
dim=0).cpu(), 3))
print("\033[1m<prediction>\t",
np.around(
torch.mean(
labels_pred[:, self.considered_label_indices],
dim=0).cpu(), 3),
end="\033[0m\n")
print("\033[1m<prediction>\t",
np.around(
torch.std(labels_pred[:,
self.considered_label_indices],
dim=0).cpu(), 3),
end="\033[0m\n")
loss_regression = torch.sqrt(
mse(labels_pred[:, self.considered_label_indices],
labels[:, self.considered_label_indices])).item()
loss_variance = self.weight_loss_sample_variance * \
loss_sample_variance(labels_pred[:,self.considered_label_indices],
threshold=self.sample_variance_threshold
).item()
accs = measure_accuracy_classifier(
labels_pred,
labels,
considered_groups=self.considered_groups.considered_groups)
variance = get_sample_variance(
labels_pred[:, self.considered_label_indices]).item()
return loss_regression, loss_variance, accs, variance
def plot_losses(self, save=False):
self.losses_train.plot()
self.losses_valid.plot()
self.losses_regression.plot(linestyle=":")
self.losses_variance.plot(linestyle=":")
if save:
plt.savefig(folder_results + "loss.png")
plt.close()
else:
plt.show()
def plot_sample_variances(self, save=False):
self.sample_variances_train.plot()
self.sample_variances_valid.plot()
if save:
plt.savefig(folder_results + "variances.png")
plt.close()
else:
plt.show()
def plot_accuracy(self, save=False):
for group in range(1, 12):
if not group in self.considered_groups.considered_groups:
continue
getattr(self, f"accuracies_Q{group}_train").plot()
if save:
plt.savefig(folder_results + "accuracy_train.png")
plt.close()
else:
plt.show()
def plot_test_accuracy(self, save=False):
for group in range(1, 12):
if not group in self.considered_groups.considered_groups:
continue
getattr(self, f"accuracies_Q{group}_valid").plot()
if save:
plt.savefig(folder_results + "accuracy_valid.png")
plt.close()
else:
plt.show()
def unet_model_fn(features, labels, mode, params):
tf.local_variables_initializer()
loss, train_op, = None, None
eval_metric_ops, training_hooks, evaluation_hooks = None, None, None
predictions_dict = None
unet = Unet(params=params)
logits = unet.model(input_tensor=features['image'])
y_pred = tf.math.softmax(logits, axis=-1)
output_img = tf.expand_dims(tf.cast(tf.math.argmax(y_pred, axis=-1) * 255, dtype=tf.uint8), axis=-1)
if mode in (estimator.ModeKeys.TRAIN, estimator.ModeKeys.EVAL):
with tf.name_scope('Loss_Calculation'):
loss = Losses(logits=logits, labels=labels['label'])
loss = loss.custom_loss()
with tf.name_scope('Dice_Score_Calculation'):
dice = f1(labels=labels['label'], predictions=y_pred)
with tf.name_scope('Images_{}'.format(mode)):
with tf.name_scope('Reformat_Outputs'):
label = tf.expand_dims(tf.cast(tf.argmax(labels['label'], -1) * 255, dtype=tf.uint8), axis=-1)
image = tf.math.divide(features['image'] - tf.reduce_max(features['image'], [0, 1, 2]),
tf.reduce_max(features['image'], [0, 1, 2]) - tf.reduce_min(features['image'],
[0, 1, 2]))
summary.image('1_Medical_Image', image, max_outputs=1)
summary.image('2_Output', output_img, max_outputs=1)
summary.image('3_Output_pred', tf.expand_dims(y_pred[:, :, :, 1], -1), max_outputs=1)
summary.image('4_Output_label', label, max_outputs=1)
if mode == estimator.ModeKeys.TRAIN:
with tf.name_scope('Learning_Rate'):
global_step = tf.compat.v1.train.get_or_create_global_step()
learning_rate = tf.compat.v1.train.exponential_decay(params['lr'], global_step=global_step,
decay_steps=params['decay_steps'],
decay_rate=params['decay_rate'], staircase=False)
with tf.name_scope('Optimizer_conf'):
train_op = Adam(learning_rate=learning_rate).minimize(loss=loss, global_step=global_step)
with tf.name_scope('Metrics'):
summary.scalar('Output_DSC', dice[1])
summary.scalar('Learning_Rate', learning_rate)
if mode == estimator.ModeKeys.EVAL:
eval_metric_ops = {'Metrics/Output_DSC': dice}
eval_summary_hook = tf.estimator.SummarySaverHook(output_dir=params['eval_path'],
summary_op=summary.merge_all(),
save_steps=params['eval_steps'])
evaluation_hooks = [eval_summary_hook]
if mode == estimator.ModeKeys.PREDICT:
predictions_dict = {'image': features['image'],
'y_preds': y_pred[:, :, :, 1],
'output_img': output_img,
'path': features['path']}
return estimator.EstimatorSpec(mode,
predictions=predictions_dict,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops,
training_hooks=training_hooks,
evaluation_hooks=evaluation_hooks)
