LETR / models /letr.py
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"""
This file provides coarse stage LETR definition
Modified based on https://github.com/facebookresearch/detr/blob/master/models/backbone.py
"""
import torch
import torch.nn.functional as F
from torch import nn
from .misc import (NestedTensor, nested_tensor_from_tensor_list,
accuracy, get_world_size, interpolate,
is_dist_avail_and_initialized)
from .backbone import build_backbone
from .matcher import build_matcher
from .transformer import build_transformer
from .letr_stack import LETRstack
import numpy as np
class LETR(nn.Module):
""" This is the LETR module that performs object detection """
def __init__(self, backbone, transformer, num_classes, num_queries, args, aux_loss=False):
super().__init__()
self.num_queries = num_queries
self.transformer = transformer
hidden_dim = transformer.d_model
self.class_embed = nn.Linear(hidden_dim, num_classes + 1)
self.lines_embed = MLP(hidden_dim, hidden_dim, 4, 3)
self.query_embed = nn.Embedding(num_queries, hidden_dim)
channel = [256, 512, 1024, 2048]
self.input_proj = nn.Conv2d(channel[args.layer1_num], hidden_dim, kernel_size=1)
self.backbone = backbone
self.aux_loss = aux_loss
self.args = args
def forward(self, samples, postprocessors=None, targets=None, criterion=None):
if isinstance(samples, (list, torch.Tensor)):
samples = nested_tensor_from_tensor_list(samples)
features, pos = self.backbone(samples)
num = self.args.layer1_num
src, mask = features[num].decompose()
assert mask is not None
hs = self.transformer(self.input_proj(src), mask, self.query_embed.weight, pos[num])[0]
outputs_class = self.class_embed(hs)
outputs_coord = self.lines_embed(hs).sigmoid()
out = {'pred_logits': outputs_class[-1], 'pred_lines': outputs_coord[-1]}
if self.aux_loss:
out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord)
return out
@torch.jit.unused
def _set_aux_loss(self, outputs_class, outputs_coord):
return [{'pred_logits': a, 'pred_lines': b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]
class SetCriterion(nn.Module):
def __init__(self, num_classes, weight_dict, eos_coef, losses, args, matcher=None):
super().__init__()
self.num_classes = num_classes
self.matcher = matcher
self.weight_dict = weight_dict
self.eos_coef = eos_coef
self.losses = losses
empty_weight = torch.ones(self.num_classes + 1)
empty_weight[-1] = self.eos_coef
self.register_buffer('empty_weight', empty_weight)
self.args = args
try:
self.args.label_loss_params = eval(self.args.label_loss_params) # Convert the string to dict.
except:
pass
def loss_lines_labels(self, outputs, targets, num_items, log=False, origin_indices=None):
"""Classification loss (NLL)
targets dicts must contain the key "labels" containing a tensor of dim [nb_target_lines]
"""
assert 'pred_logits' in outputs
src_logits = outputs['pred_logits']
idx = self._get_src_permutation_idx(origin_indices)
target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, origin_indices)])
target_classes = torch.full(src_logits.shape[:2], self.num_classes,
dtype=torch.int64, device=src_logits.device)
target_classes[idx] = target_classes_o
if self.args.label_loss_func == 'cross_entropy':
loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight)
elif self.args.label_loss_func == 'focal_loss':
loss_ce = self.label_focal_loss(src_logits.transpose(1, 2), target_classes, self.empty_weight, **self.args.label_loss_params)
else:
raise ValueError()
losses = {'loss_ce': loss_ce}
return losses
def label_focal_loss(self, input, target, weight, gamma=2.0):
""" Focal loss for label prediction. """
# In our case, target has 2 classes: 0 for foreground (i.e. line) and 1 for background.
# The weight here can serve as the alpha hyperparameter in focal loss. However, in focal loss,
#
# Ref: https://github.com/facebookresearch/DETR/blob/699bf53f3e3ecd4f000007b8473eda6a08a8bed6/models/segmentation.py#L190
# Ref: https://medium.com/visionwizard/understanding-focal-loss-a-quick-read-b914422913e7
# input shape: [batch size, #classes, #queries]
# target shape: [batch size, #queries]
# weight shape: [#classes]
prob = F.softmax(input, 1) # Shape: [batch size, #classes, #queries].
ce_loss = F.cross_entropy(input, target, weight, reduction='none') # Shape: [batch size, #queries].
p_t = prob[:,1,:] * target + prob[:,0,:] * (1 - target) # Shape: [batch size, #queries]. Note: prob[:,0,:] + prob[:,1,:] should be 1.
loss = ce_loss * ((1 - p_t) ** gamma)
loss = loss.mean() # Original label loss (i.e. cross entropy) does not consider the #lines, so we also do not consider that.
return loss
@torch.no_grad()
def loss_cardinality(self, outputs, targets, num_items, origin_indices=None):
""" Compute the cardinality error, ie the absolute error in the number of predicted non-empty lines
This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients
"""
pred_logits = outputs['pred_logits']
device = pred_logits.device
tgt_lengths = torch.as_tensor([len(v["labels"]) for v in targets], device=device)
# Count the number of predictions that are NOT "no-object" (which is the last class)
card_pred = (pred_logits.argmax(-1) != pred_logits.shape[-1] - 1).sum(1)
card_err = F.l1_loss(card_pred.float(), tgt_lengths.float())
losses = {'cardinality_error': card_err}
return losses
def loss_lines_POST(self, outputs, targets, num_items, origin_indices=None):
assert 'POST_pred_lines' in outputs
if outputs['POST_pred_lines'].shape[1] == 1000:
idx = self._get_src_permutation_idx(origin_indices)
src_lines = outputs['POST_pred_lines'][idx]
else:
src_lines = outputs['POST_pred_lines'].squeeze(0)
target_lines = torch.cat([t['lines'][i] for t, (_, i) in zip(targets, origin_indices)], dim=0)
loss_line = F.l1_loss(src_lines, target_lines, reduction='none')
losses = {}
losses['loss_line'] = loss_line.sum() / num_items
return losses
def loss_lines(self, outputs, targets, num_items, origin_indices=None):
assert 'pred_lines' in outputs
idx = self._get_src_permutation_idx(origin_indices)
src_lines = outputs['pred_lines'][idx]
target_lines = torch.cat([t['lines'][i] for t, (_, i) in zip(targets, origin_indices)], dim=0)
loss_line = F.l1_loss(src_lines, target_lines, reduction='none')
losses = {}
losses['loss_line'] = loss_line.sum() / num_items
return losses
def _get_src_permutation_idx(self, indices):
# permute predictions following indices
batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
src_idx = torch.cat([src for (src, _) in indices])
return batch_idx, src_idx
def _get_tgt_permutation_idx(self, indices):
# permute targets following indices
batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])
tgt_idx = torch.cat([tgt for (_, tgt) in indices])
return batch_idx, tgt_idx
def get_loss(self, loss, outputs, targets, num_items, **kwargs):
loss_map = {
'POST_lines_labels': self.loss_lines_labels,
'POST_lines': self.loss_lines,
'lines_labels': self.loss_lines_labels,
'cardinality': self.loss_cardinality,
'lines': self.loss_lines,
}
assert loss in loss_map, f'do you really want to compute {loss} loss?'
return loss_map[loss](outputs, targets, num_items, **kwargs)
def forward(self, outputs, targets, origin_indices=None):
""" This performs the loss computation.
Parameters:
outputs: dict of tensors, see the output specification of the model for the format
targets: list of dicts, such that len(targets) == batch_size.
The expected keys in each dict depends on the losses applied, see each loss' doc
"""
outputs_without_aux = {k: v for k, v in outputs.items() if k != 'aux_outputs'}
origin_indices = self.matcher(outputs_without_aux, targets)
num_items = sum(len(t["labels"]) for t in targets)
num_items = torch.as_tensor([num_items], dtype=torch.float, device=next(iter(outputs.values())).device)
if is_dist_avail_and_initialized():
torch.distributed.all_reduce(num_items)
num_items = torch.clamp(num_items / get_world_size(), min=1).item()
# Compute all the requested losses
losses = {}
for loss in self.losses:
losses.update(self.get_loss(loss, outputs, targets, num_items, origin_indices=origin_indices))
# In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
aux_name = 'aux_outputs'
if aux_name in outputs:
for i, aux_outputs in enumerate(outputs[aux_name]):
origin_indices = self.matcher(aux_outputs, targets)
for loss in self.losses:
kwargs = {}
if loss == 'labels':
# Logging is enabled only for the last layer
kwargs = {'log': False}
l_dict = self.get_loss(loss, aux_outputs, targets, num_items, origin_indices=origin_indices, **kwargs)
l_dict = {k + f'_{i}': v for k, v in l_dict.items()}
losses.update(l_dict)
return losses
class PostProcess_Line(nn.Module):
""" This module converts the model's output into the format expected by the coco api"""
@torch.no_grad()
def forward(self, outputs, target_sizes, output_type):
""" Perform the computation
Parameters:
outputs: raw outputs of the model
target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch
For evaluation, this must be the original image size (before any data augmentation)
For visualization, this should be the image size after data augment, but before padding
"""
if output_type == "prediction":
out_logits, out_line = outputs['pred_logits'], outputs['pred_lines']
assert len(out_logits) == len(target_sizes)
assert target_sizes.shape[1] == 2
prob = F.softmax(out_logits, -1)
scores, labels = prob[..., :-1].max(-1)
# convert to [x0, y0, x1, y1] format
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
lines = out_line * scale_fct[:, None, :]
results = [{'scores': s, 'labels': l, 'lines': b} for s, l, b in zip(scores, labels, lines)]
elif output_type == "prediction_POST":
out_logits, out_line = outputs['pred_logits'], outputs['POST_pred_lines']
assert len(out_logits) == len(target_sizes)
assert target_sizes.shape[1] == 2
prob = F.softmax(out_logits, -1)
scores, labels = prob[..., :-1].max(-1)
# convert to [x0, y0, x1, y1] format
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
lines = out_line * scale_fct[:, None, :]
results = [{'scores': s, 'labels': l, 'lines': b} for s, l, b in zip(scores, labels, lines)]
elif output_type == "ground_truth":
results = []
for dic in outputs:
lines = dic['lines']
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
scaled_lines = lines * scale_fct
results.append({'labels': dic['labels'], 'lines': scaled_lines, 'image_id': dic['image_id']})
else:
assert False
return results
class MLP(nn.Module):
""" Very simple multi-layer perceptron (also called FFN)"""
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
def forward(self, x):
for i, layer in enumerate(self.layers):
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
return x
def build(args):
num_classes = 1
device = torch.device(args.device)
backbone = build_backbone(args)
transformer = build_transformer(args)
model = LETR(
backbone,
transformer,
num_classes=num_classes,
num_queries=args.num_queries,
args=args,
aux_loss=args.aux_loss,
)
if args.LETRpost:
model = LETRstack(model, args=args)
matcher = build_matcher(args, type='origin_line')
losses = []
weight_dict = {}
if args.LETRpost:
losses.append('POST_lines_labels')
losses.append('POST_lines')
weight_dict['loss_ce'] = 1
weight_dict['loss_line'] = args.line_loss_coef
aux_layer = args.second_dec_layers
else:
losses.append('lines_labels')
losses.append('lines')
weight_dict['loss_ce'] = 1
weight_dict['loss_line'] = args.line_loss_coef
aux_layer = args.dec_layers
if args.aux_loss:
aux_weight_dict = {}
for i in range(aux_layer - 1):
aux_weight_dict.update({k + f'_{i}': v for k, v in weight_dict.items()})
weight_dict.update(aux_weight_dict)
criterion = SetCriterion(num_classes, weight_dict=weight_dict, eos_coef=args.eos_coef, losses=losses, args=args, matcher=matcher)
criterion.to(device)
postprocessors = {'line': PostProcess_Line()}
return model, criterion, postprocessors