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"""Faster RCNN Model."""
from __future__ import absolute_import
import os
import warnings
import mxnet as mx
from mxnet import autograd
from mxnet.gluon import nn
from mxnet.gluon.contrib.nn import SyncBatchNorm
from .rcnn_target import RCNNTargetSampler, RCNNTargetGenerator
from ..rcnn import RCNN
from ..rpn import RPN
from ...nn.feature import FPNFeatureExpander
__all__ = ['FasterRCNN', 'get_faster_rcnn',
'faster_rcnn_resnet50_v1b_voc',
'faster_rcnn_resnet50_v1b_coco',
'faster_rcnn_fpn_resnet50_v1b_coco',
'faster_rcnn_fpn_bn_resnet50_v1b_coco',
'faster_rcnn_resnet50_v1b_custom',
'faster_rcnn_resnet101_v1d_voc',
'faster_rcnn_resnet101_v1d_coco',
'faster_rcnn_fpn_resnet101_v1d_coco',
'faster_rcnn_resnet101_v1d_custom']
class FasterRCNN(RCNN):
r"""Faster RCNN network.
Parameters
----------
features : gluon.HybridBlock
Base feature extractor before feature pooling layer.
top_features : gluon.HybridBlock
Tail feature extractor after feature pooling layer.
classes : iterable of str
Names of categories, its length is ``num_class``.
box_features : gluon.HybridBlock, default is None
feature head for transforming shared ROI output (top_features) for box prediction.
If set to None, global average pooling will be used.
short : int, default is 600.
Input image short side size.
max_size : int, default is 1000.
Maximum size of input image long side.
min_stage : int, default is 4
Minimum stage NO. for FPN stages.
max_stage : int, default is 4
Maximum stage NO. for FPN stages.
train_patterns : str, default is None.
Matching pattern for trainable parameters.
nms_thresh : float, default is 0.3.
Non-maximum suppression threshold. You can specify < 0 or > 1 to disable NMS.
nms_topk : int, default is 400
Apply NMS to top k detection results, use -1 to disable so that every Detection
result is used in NMS.
post_nms : int, default is 100
Only return top `post_nms` detection results, the rest is discarded. The number is
based on COCO dataset which has maximum 100 objects per image. You can adjust this
number if expecting more objects. You can use -1 to return all detections.
roi_mode : str, default is align
ROI pooling mode. Currently support 'pool' and 'align'.
roi_size : tuple of int, length 2, default is (14, 14)
(height, width) of the ROI region.
strides : int/tuple of ints, default is 16
Feature map stride with respect to original image.
This is usually the ratio between original image size and feature map size.
For FPN, use a tuple of ints.
clip : float, default is None
Clip bounding box target to this value.
rpn_channel : int, default is 1024
Channel number used in RPN convolutional layers.
base_size : int
The width(and height) of reference anchor box.
scales : iterable of float, default is (8, 16, 32)
The areas of anchor boxes.
We use the following form to compute the shapes of anchors:
.. math::
width_{anchor} = size_{base} \times scale \times \sqrt{ 1 / ratio}
height_{anchor} = size_{base} \times scale \times \sqrt{ratio}
ratios : iterable of float, default is (0.5, 1, 2)
The aspect ratios of anchor boxes. We expect it to be a list or tuple.
alloc_size : tuple of int
Allocate size for the anchor boxes as (H, W).
Usually we generate enough anchors for large feature map, e.g. 128x128.
Later in inference we can have variable input sizes,
at which time we can crop corresponding anchors from this large
anchor map so we can skip re-generating anchors for each input.
rpn_train_pre_nms : int, default is 12000
Filter top proposals before NMS in training of RPN.
rpn_train_post_nms : int, default is 2000
Return top proposal results after NMS in training of RPN.
Will be set to rpn_train_pre_nms if it is larger than rpn_train_pre_nms.
rpn_test_pre_nms : int, default is 6000
Filter top proposals before NMS in testing of RPN.
rpn_test_post_nms : int, default is 300
Return top proposal results after NMS in testing of RPN.
Will be set to rpn_test_pre_nms if it is larger than rpn_test_pre_nms.
rpn_nms_thresh : float, default is 0.7
IOU threshold for NMS. It is used to remove overlapping proposals.
rpn_num_sample : int, default is 256
Number of samples for RPN targets.
rpn_pos_iou_thresh : float, default is 0.7
Anchor with IOU larger than ``pos_iou_thresh`` is regarded as positive samples.
rpn_neg_iou_thresh : float, default is 0.3
Anchor with IOU smaller than ``neg_iou_thresh`` is regarded as negative samples.
Anchors with IOU in between ``pos_iou_thresh`` and ``neg_iou_thresh`` are
ignored.
rpn_pos_ratio : float, default is 0.5
``pos_ratio`` defines how many positive samples (``pos_ratio * num_sample``) is
to be sampled.
rpn_box_norm : array-like of size 4, default is (1., 1., 1., 1.)
Std value to be divided from encoded values.
rpn_min_size : int, default is 16
Proposals whose size is smaller than ``min_size`` will be discarded.
per_device_batch_size : int, default is 1
Batch size for each device during training.
num_sample : int, default is 128
Number of samples for RCNN targets.
pos_iou_thresh : float, default is 0.5
Proposal whose IOU larger than ``pos_iou_thresh`` is regarded as positive samples.
pos_ratio : float, default is 0.25
``pos_ratio`` defines how many positive samples (``pos_ratio * num_sample``) is
to be sampled.
max_num_gt : int, default is 300
Maximum ground-truth number in whole training dataset. This is only an upper bound, not
necessarily very precise. However, using a very big number may impact the training speed.
additional_output : boolean, default is False
``additional_output`` is only used for Mask R-CNN to get internal outputs.
force_nms : bool, default is False
Appy NMS to all categories, this is to avoid overlapping detection results from different
categories.
Attributes
----------
classes : iterable of str
Names of categories, its length is ``num_class``.
num_class : int
Number of positive categories.
short : int
Input image short side size.
max_size : int
Maximum size of input image long side.
train_patterns : str
Matching pattern for trainable parameters.
nms_thresh : float
Non-maximum suppression threshold. You can specify < 0 or > 1 to disable NMS.
nms_topk : int
Apply NMS to top k detection results, use -1 to disable so that every Detection
result is used in NMS.
force_nms : bool
Appy NMS to all categories, this is to avoid overlapping detection results
from different categories.
post_nms : int
Only return top `post_nms` detection results, the rest is discarded. The number is
based on COCO dataset which has maximum 100 objects per image. You can adjust this
number if expecting more objects. You can use -1 to return all detections.
rpn_target_generator : gluon.Block
Generate training targets with cls_target, box_target, and box_mask.
target_generator : gluon.Block
Generate training targets with boxes, samples, matches, gt_label and gt_box.
"""
def __init__(self, features, top_features, classes, box_features=None,
short=600, max_size=1000, min_stage=4, max_stage=4, train_patterns=None,
nms_thresh=0.3, nms_topk=400, post_nms=100,
roi_mode='align', roi_size=(14, 14), strides=16, clip=None,
rpn_channel=1024, base_size=16, scales=(8, 16, 32),
ratios=(0.5, 1, 2), alloc_size=(128, 128), rpn_nms_thresh=0.7,
rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=6000,
rpn_test_post_nms=300, rpn_min_size=16, per_device_batch_size=1, num_sample=128,
pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=300, additional_output=False,
force_nms=False, **kwargs):
super(FasterRCNN, self).__init__(
features=features, top_features=top_features, classes=classes,
box_features=box_features, short=short, max_size=max_size,
train_patterns=train_patterns, nms_thresh=nms_thresh, nms_topk=nms_topk,
post_nms=post_nms, roi_mode=roi_mode, roi_size=roi_size, strides=strides, clip=clip,
force_nms=force_nms, **kwargs)
if rpn_train_post_nms > rpn_train_pre_nms:
rpn_train_post_nms = rpn_train_pre_nms
if rpn_test_post_nms > rpn_test_pre_nms:
rpn_test_post_nms = rpn_test_pre_nms
self.ashape = alloc_size[0]
self._min_stage = min_stage
self._max_stage = max_stage
self.num_stages = max_stage - min_stage + 1
if self.num_stages > 1:
assert len(scales) == len(strides) == self.num_stages, \
"The num_stages (%d) must match number of scales (%d) and strides (%d)" \
% (self.num_stages, len(scales), len(strides))
self._batch_size = per_device_batch_size
self._num_sample = num_sample
self._rpn_test_post_nms = rpn_test_post_nms
self._target_generator = RCNNTargetGenerator(self.num_class, int(num_sample * pos_ratio),
self._batch_size)
self._additional_output = additional_output
with self.name_scope():
self.rpn = RPN(
channels=rpn_channel, strides=strides, base_size=base_size,
scales=scales, ratios=ratios, alloc_size=alloc_size,
clip=clip, nms_thresh=rpn_nms_thresh, train_pre_nms=rpn_train_pre_nms,
train_post_nms=rpn_train_post_nms, test_pre_nms=rpn_test_pre_nms,
test_post_nms=rpn_test_post_nms, min_size=rpn_min_size,
multi_level=self.num_stages > 1, per_level_nms=False)
self.sampler = RCNNTargetSampler(num_image=self._batch_size,
num_proposal=rpn_train_post_nms, num_sample=num_sample,
pos_iou_thresh=pos_iou_thresh, pos_ratio=pos_ratio,
max_num_gt=max_num_gt)
@property
def target_generator(self):
"""Returns stored target generator
Returns
-------
mxnet.gluon.HybridBlock
The RCNN target generator
"""
return self._target_generator
def reset_class(self, classes, reuse_weights=None):
"""Reset class categories and class predictors.
Parameters
----------
classes : iterable of str
The new categories. ['apple', 'orange'] for example.
reuse_weights : dict
A {new_integer : old_integer} or mapping dict or {new_name : old_name} mapping dict,
or a list of [name0, name1,...] if class names don't change.
This allows the new predictor to reuse the
previously trained weights specified.
Example
-------
>>> net = gluoncv.model_zoo.get_model('faster_rcnn_resnet50_v1b_coco', pretrained=True)
>>> # use direct name to name mapping to reuse weights
>>> net.reset_class(classes=['person'], reuse_weights={'person':'person'})
>>> # or use interger mapping, person is the 14th category in VOC
>>> net.reset_class(classes=['person'], reuse_weights={0:14})
>>> # you can even mix them
>>> net.reset_class(classes=['person'], reuse_weights={'person':14})
>>> # or use a list of string if class name don't change
>>> net.reset_class(classes=['person'], reuse_weights=['person'])
"""
super(FasterRCNN, self).reset_class(classes, reuse_weights)
self._target_generator = RCNNTargetGenerator(self.num_class, self.sampler._max_pos,
self._batch_size)
def _pyramid_roi_feats(self, F, features, rpn_rois, roi_size, strides, roi_mode='align',
roi_canonical_scale=224.0, eps=1e-6):
"""Assign rpn_rois to specific FPN layers according to its area
and then perform `ROIPooling` or `ROIAlign` to generate final
region proposals aggregated features.
Parameters
----------
features : list of mx.ndarray or mx.symbol
Features extracted from FPN base network
rpn_rois : mx.ndarray or mx.symbol
(N, 5) with [[batch_index, x1, y1, x2, y2], ...] like
roi_size : tuple
The size of each roi with regard to ROI-Wise operation
each region proposal will be roi_size spatial shape.
strides : tuple e.g. [4, 8, 16, 32]
Define the gap that ori image and feature map have
roi_mode : str, default is align
ROI pooling mode. Currently support 'pool' and 'align'.
roi_canonical_scale : float, default is 224.0
Hyperparameters for the RoI-to-FPN level mapping heuristic.
Returns
-------
Pooled roi features aggregated according to its roi_level
"""
max_stage = self._max_stage
if self._max_stage > 5: # do not use p6 for RCNN
max_stage = self._max_stage - 1
_, x1, y1, x2, y2 = F.split(rpn_rois, axis=-1, num_outputs=5)
h = y2 - y1 + 1
w = x2 - x1 + 1
roi_level = F.floor(4 + F.log2(F.sqrt(w * h) / roi_canonical_scale + eps))
roi_level = F.squeeze(F.clip(roi_level, self._min_stage, max_stage))
# [2,2,..,3,3,...,4,4,...,5,5,...] ``Prohibit swap order here``
# roi_level_sorted_args = F.argsort(roi_level, is_ascend=True)
# roi_level = F.sort(roi_level, is_ascend=True)
# rpn_rois = F.take(rpn_rois, roi_level_sorted_args, axis=0)
pooled_roi_feats = []
for i, l in enumerate(range(self._min_stage, max_stage + 1)):
if roi_mode == 'pool':
# Pool features with all rois first, and then set invalid pooled features to zero,
# at last ele-wise add together to aggregate all features.
pooled_feature = F.ROIPooling(features[i], rpn_rois, roi_size, 1. / strides[i])
pooled_feature = F.where(roi_level == l, pooled_feature,
F.zeros_like(pooled_feature))
elif roi_mode == 'align':
if 'box_encode' in F.contrib.__dict__ and 'box_decode' in F.contrib.__dict__:
# TODO(jerryzcn): clean this up for once mx 1.6 is released.
masked_rpn_rois = F.where(roi_level == l, rpn_rois, F.ones_like(rpn_rois) * -1.)
pooled_feature = F.contrib.ROIAlign(features[i], masked_rpn_rois, roi_size,
1. / strides[i], sample_ratio=2)
else:
pooled_feature = F.contrib.ROIAlign(features[i], rpn_rois, roi_size,
1. / strides[i], sample_ratio=2)
pooled_feature = F.where(roi_level == l, pooled_feature,
F.zeros_like(pooled_feature))
else:
raise ValueError("Invalid roi mode: {}".format(roi_mode))
pooled_roi_feats.append(pooled_feature)
# Ele-wise add to aggregate all pooled features
pooled_roi_feats = F.ElementWiseSum(*pooled_roi_feats)
# Sort all pooled features by asceding order
# [2,2,..,3,3,...,4,4,...,5,5,...]
# pooled_roi_feats = F.take(pooled_roi_feats, roi_level_sorted_args)
# pooled roi feats (B*N, C, 7, 7), N = N2 + N3 + N4 + N5 = num_roi, C=256 in ori paper
return pooled_roi_feats
# pylint: disable=arguments-differ
def hybrid_forward(self, F, x, gt_box=None, gt_label=None):
"""Forward Faster-RCNN network.
The behavior during training and inference is different.
Parameters
----------
x : mxnet.nd.NDArray or mxnet.symbol
The network input tensor.
gt_box : type, only required during training
The ground-truth bbox tensor with shape (B, N, 4).
gt_label : type, only required during training
The ground-truth label tensor with shape (B, 1, 4).
Returns
-------
(ids, scores, bboxes)
During inference, returns final class id, confidence scores, bounding
boxes.
"""
def _split(x, axis, num_outputs, squeeze_axis):
x = F.split(x, axis=axis, num_outputs=num_outputs, squeeze_axis=squeeze_axis)
if isinstance(x, list):
return x
else:
return [x]
feat = self.features(x)
if not isinstance(feat, (list, tuple)):
feat = [feat]
# RPN proposals
if autograd.is_training():
rpn_score, rpn_box, raw_rpn_score, raw_rpn_box, anchors = \
self.rpn(F.zeros_like(x), *feat)
rpn_box, samples, matches = self.sampler(rpn_box, rpn_score, gt_box)
else:
_, rpn_box = self.rpn(F.zeros_like(x), *feat)
# create batchid for roi
num_roi = self._num_sample if autograd.is_training() else self._rpn_test_post_nms
batch_size = self._batch_size if autograd.is_training() else 1
with autograd.pause():
roi_batchid = F.arange(0, batch_size)
roi_batchid = F.repeat(roi_batchid, num_roi)
# remove batch dim because ROIPooling require 2d input
rpn_roi = F.concat(*[roi_batchid.reshape((-1, 1)), rpn_box.reshape((-1, 4))], dim=-1)
rpn_roi = F.stop_gradient(rpn_roi)
if self.num_stages > 1:
# using FPN
pooled_feat = self._pyramid_roi_feats(F, feat, rpn_roi, self._roi_size,
self._strides, roi_mode=self._roi_mode)
else:
# ROI features
if self._roi_mode == 'pool':
pooled_feat = F.ROIPooling(feat[0], rpn_roi, self._roi_size, 1. / self._strides)
elif self._roi_mode == 'align':
pooled_feat = F.contrib.ROIAlign(feat[0], rpn_roi, self._roi_size,
1. / self._strides, sample_ratio=2)
else:
raise ValueError("Invalid roi mode: {}".format(self._roi_mode))
# RCNN prediction
if self.top_features is not None:
top_feat = self.top_features(pooled_feat)
else:
top_feat = pooled_feat
if self.box_features is None:
box_feat = F.contrib.AdaptiveAvgPooling2D(top_feat, output_size=1)
else:
box_feat = self.box_features(top_feat)
cls_pred = self.class_predictor(box_feat)
# cls_pred (B * N, C) -> (B, N, C)
cls_pred = cls_pred.reshape((batch_size, num_roi, self.num_class + 1))
# no need to convert bounding boxes in training, just return
if autograd.is_training():
cls_targets, box_targets, box_masks, indices = \
self._target_generator(rpn_box, samples, matches, gt_label, gt_box)
box_feat = F.reshape(box_feat.expand_dims(0), (batch_size, -1, 0))
box_pred = self.box_predictor(F.concat(
*[F.take(F.slice_axis(box_feat, axis=0, begin=i, end=i + 1).squeeze(),
F.slice_axis(indices, axis=0, begin=i, end=i + 1).squeeze())
for i in range(batch_size)], dim=0))
# box_pred (B * N, C * 4) -> (B, N, C, 4)
box_pred = box_pred.reshape((batch_size, -1, self.num_class, 4))
if self._additional_output:
return (cls_pred, box_pred, rpn_box, samples, matches, raw_rpn_score, raw_rpn_box,
anchors, cls_targets, box_targets, box_masks, top_feat, indices)
return (cls_pred, box_pred, rpn_box, samples, matches, raw_rpn_score, raw_rpn_box,
anchors, cls_targets, box_targets, box_masks, indices)
box_pred = self.box_predictor(box_feat)
# box_pred (B * N, C * 4) -> (B, N, C, 4)
box_pred = box_pred.reshape((batch_size, num_roi, self.num_class, 4))
# cls_ids (B, N, C), scores (B, N, C)
cls_ids, scores = self.cls_decoder(F.softmax(cls_pred, axis=-1))
# cls_ids, scores (B, N, C) -> (B, C, N) -> (B, C, N, 1)
cls_ids = cls_ids.transpose((0, 2, 1)).reshape((0, 0, 0, 1))
scores = scores.transpose((0, 2, 1)).reshape((0, 0, 0, 1))
# box_pred (B, N, C, 4) -> (B, C, N, 4)
box_pred = box_pred.transpose((0, 2, 1, 3))
# rpn_boxes (B, N, 4) -> B * (1, N, 4)
rpn_boxes = _split(rpn_box, axis=0, num_outputs=batch_size, squeeze_axis=False)
# cls_ids, scores (B, C, N, 1) -> B * (C, N, 1)
cls_ids = _split(cls_ids, axis=0, num_outputs=batch_size, squeeze_axis=True)
scores = _split(scores, axis=0, num_outputs=batch_size, squeeze_axis=True)
# box_preds (B, C, N, 4) -> B * (C, N, 4)
box_preds = _split(box_pred, axis=0, num_outputs=batch_size, squeeze_axis=True)
# per batch predict, nms, each class has topk outputs
results = []
for rpn_box, cls_id, score, box_pred in zip(rpn_boxes, cls_ids, scores, box_preds):
# box_pred (C, N, 4) rpn_box (1, N, 4) -> bbox (C, N, 4)
bbox = self.box_decoder(box_pred, rpn_box)
# res (C, N, 6)
res = F.concat(*[cls_id, score, bbox], dim=-1)
if self.force_nms:
# res (1, C*N, 6), to allow cross-catogory suppression
res = res.reshape((1, -1, 0))
# res (C, self.nms_topk, 6)
res = F.contrib.box_nms(
res, overlap_thresh=self.nms_thresh, topk=self.nms_topk, valid_thresh=0.0001,
id_index=0, score_index=1, coord_start=2, force_suppress=self.force_nms)
# res (C * self.nms_topk, 6)
res = res.reshape((-3, 0))
results.append(res)
# result B * (C * topk, 6) -> (B, C * topk, 6)
result = F.stack(*results, axis=0)
ids = F.slice_axis(result, axis=-1, begin=0, end=1)
scores = F.slice_axis(result, axis=-1, begin=1, end=2)
bboxes = F.slice_axis(result, axis=-1, begin=2, end=6)
if self._additional_output:
return ids, scores, bboxes, feat
return ids, scores, bboxes
def get_faster_rcnn(name, dataset, pretrained=False, ctx=mx.cpu(),
root=os.path.join('~', '.mxnet', 'models'), **kwargs):
r"""Utility function to return faster rcnn networks.
Parameters
----------
name : str
Model name.
dataset : str
The name of dataset.
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : mxnet.Context
Context such as mx.cpu(), mx.gpu(0).
root : str
Model weights storing path.
Returns
-------
mxnet.gluon.HybridBlock
The Faster-RCNN network.
"""
net = FasterRCNN(**kwargs)
if pretrained:
from ..model_store import get_model_file
full_name = '_'.join(('faster_rcnn', name, dataset))
net.load_parameters(get_model_file(full_name, tag=pretrained, root=root), ctx=ctx)
else:
for v in net.collect_params().values():
try:
v.reset_ctx(ctx)
except ValueError:
pass
return net
def faster_rcnn_resnet50_v1b_voc(pretrained=False, pretrained_base=True, **kwargs):
r"""Faster RCNN model from the paper
"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards
real-time object detection with region proposal networks"
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
pretrained_base : bool or str, optional, default is True
Load pretrained base network, the extra layers are randomized. Note that
if pretrained is `True`, this has no effect.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
Examples
--------
>>> model = get_faster_rcnn_resnet50_v1b_voc(pretrained=True)
>>> print(model)
"""
from ..resnetv1b import resnet50_v1b
from ...data import VOCDetection
classes = VOCDetection.CLASSES
pretrained_base = False if pretrained else pretrained_base
base_network = resnet50_v1b(pretrained=pretrained_base, dilated=False,
use_global_stats=True, **kwargs)
features = nn.HybridSequential()
top_features = nn.HybridSequential()
for layer in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']:
features.add(getattr(base_network, layer))
for layer in ['layer4']:
top_features.add(getattr(base_network, layer))
train_patterns = '|'.join(['.*dense', '.*rpn', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv'])
return get_faster_rcnn(
name='resnet50_v1b', dataset='voc', pretrained=pretrained,
features=features, top_features=top_features, classes=classes,
short=600, max_size=1000, train_patterns=train_patterns,
nms_thresh=0.3, nms_topk=400, post_nms=100,
roi_mode='align', roi_size=(14, 14), strides=16, clip=None,
rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32),
ratios=(0.5, 1, 2), alloc_size=(128, 128), rpn_nms_thresh=0.7,
rpn_train_pre_nms=12000, rpn_train_post_nms=2000,
rpn_test_pre_nms=6000, rpn_test_post_nms=300, rpn_min_size=16,
num_sample=128, pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=100,
**kwargs)
def faster_rcnn_resnet50_v1b_coco(pretrained=False, pretrained_base=True, **kwargs):
r"""Faster RCNN model from the paper
"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards
real-time object detection with region proposal networks"
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
pretrained_base : bool or str, optional, default is True
Load pretrained base network, the extra layers are randomized. Note that
if pretrained is `True`, this has no effect.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
Examples
--------
>>> model = get_faster_rcnn_resnet50_v1b_coco(pretrained=True)
>>> print(model)
"""
from ..resnetv1b import resnet50_v1b
from ...data import COCODetection
classes = COCODetection.CLASSES
pretrained_base = False if pretrained else pretrained_base
base_network = resnet50_v1b(pretrained=pretrained_base, dilated=False,
use_global_stats=True, **kwargs)
features = nn.HybridSequential()
top_features = nn.HybridSequential()
for layer in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']:
features.add(getattr(base_network, layer))
for layer in ['layer4']:
top_features.add(getattr(base_network, layer))
train_patterns = '|'.join(['.*dense', '.*rpn', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv'])
return get_faster_rcnn(
name='resnet50_v1b', dataset='coco', pretrained=pretrained,
features=features, top_features=top_features, classes=classes,
short=800, max_size=1333, train_patterns=train_patterns,
nms_thresh=0.5, nms_topk=-1, post_nms=-1,
roi_mode='align', roi_size=(14, 14), strides=16, clip=4.14,
rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32),
ratios=(0.5, 1, 2), alloc_size=(128, 128), rpn_nms_thresh=0.7,
rpn_train_pre_nms=12000, rpn_train_post_nms=2000,
rpn_test_pre_nms=6000, rpn_test_post_nms=1000, rpn_min_size=1,
num_sample=128, pos_iou_thresh=0.5, pos_ratio=0.25,
max_num_gt=100, **kwargs)
def faster_rcnn_fpn_resnet50_v1b_coco(pretrained=False, pretrained_base=True, **kwargs):
r"""Faster RCNN model with FPN from the paper
"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards
real-time object detection with region proposal networks"
"Lin, T., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S. (2016).
Feature Pyramid Networks for Object Detection"
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
pretrained_base : bool or str, optional, default is True
Load pretrained base network, the extra layers are randomized. Note that
if pretrained is `Ture`, this has no effect.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
Examples
--------
>>> model = get_faster_rcnn_fpn_resnet50_v1b_coco(pretrained=True)
>>> print(model)
"""
from ..resnetv1b import resnet50_v1b
from ...data import COCODetection
classes = COCODetection.CLASSES
pretrained_base = False if pretrained else pretrained_base
base_network = resnet50_v1b(pretrained=pretrained_base, dilated=False,
use_global_stats=True, **kwargs)
features = FPNFeatureExpander(
network=base_network,
outputs=['layers1_relu8_fwd', 'layers2_relu11_fwd', 'layers3_relu17_fwd',
'layers4_relu8_fwd'], num_filters=[256, 256, 256, 256], use_1x1=True,
use_upsample=True, use_elewadd=True, use_p6=True, no_bias=False, pretrained=pretrained_base)
top_features = None
# 2 FC layer before RCNN cls and reg
box_features = nn.HybridSequential()
for _ in range(2):
box_features.add(nn.Dense(1024, weight_initializer=mx.init.Normal(0.01)))
box_features.add(nn.Activation('relu'))
train_patterns = '|'.join(
['.*dense', '.*rpn', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv', 'P'])
return get_faster_rcnn(
name='fpn_resnet50_v1b', dataset='coco', pretrained=pretrained, features=features,
top_features=top_features, classes=classes, box_features=box_features,
short=800, max_size=1333, min_stage=2, max_stage=6, train_patterns=train_patterns,
nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(7, 7),
strides=(4, 8, 16, 32, 64), clip=4.14, rpn_channel=1024, base_size=16,
scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(384, 384),
rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000,
rpn_test_pre_nms=6000, rpn_test_post_nms=1000, rpn_min_size=1, num_sample=512,
pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=100, **kwargs)
def faster_rcnn_fpn_bn_resnet50_v1b_coco(pretrained=False, pretrained_base=True, num_devices=0,
**kwargs):
r"""Faster RCNN model with FPN from the paper
"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards
real-time object detection with region proposal networks"
"Lin, T., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S. (2016).
Feature Pyramid Networks for Object Detection"
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
pretrained_base : bool or str, optional, default is True
Load pretrained base network, the extra layers are randomized. Note that
if pretrained is `Ture`, this has no effect.
num_devices : int, default is 0
Number of devices for sync batch norm layer. if less than 1, use all devices available.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
Examples
--------
>>> model = get_faster_rcnn_fpn_bn_resnet50_v1b_coco(pretrained=True)
>>> print(model)
"""
from ..resnetv1b import resnet50_v1b
from ...data import COCODetection
classes = COCODetection.CLASSES
pretrained_base = False if pretrained else pretrained_base
gluon_norm_kwargs = {'num_devices': num_devices} if num_devices >= 1 else {}
base_network = resnet50_v1b(pretrained=pretrained_base, dilated=False, use_global_stats=False,
norm_layer=SyncBatchNorm, norm_kwargs=gluon_norm_kwargs, **kwargs)
sym_norm_kwargs = {'ndev': num_devices} if num_devices >= 1 else {}
features = FPNFeatureExpander(
network=base_network,
outputs=['layers1_relu8_fwd', 'layers2_relu11_fwd', 'layers3_relu17_fwd',
'layers4_relu8_fwd'], num_filters=[256, 256, 256, 256], use_1x1=True,
use_upsample=True, use_elewadd=True, use_p6=True, no_bias=True, pretrained=pretrained_base,
norm_layer=mx.sym.contrib.SyncBatchNorm, norm_kwargs=sym_norm_kwargs)
top_features = None
# 1 Conv 1 FC layer before RCNN cls and reg
box_features = nn.HybridSequential()
box_features.add(nn.Conv2D(256, 3, padding=1, use_bias=False),
SyncBatchNorm(**gluon_norm_kwargs),
nn.Activation('relu'),
nn.Dense(1024, weight_initializer=mx.init.Normal(0.01)),
nn.Activation('relu'))
train_patterns = '(?!.*moving)' # excluding symbol bn moving mean and var
return get_faster_rcnn(
name='fpn_bn_resnet50_v1b', dataset='coco', pretrained=pretrained, features=features,
top_features=top_features, classes=classes, box_features=box_features,
short=(640, 800), max_size=1333, min_stage=2, max_stage=6, train_patterns=train_patterns,
nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(7, 7),
strides=(4, 8, 16, 32, 64), clip=4.14, rpn_channel=1024, base_size=16,
scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(384, 384),
rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000,
rpn_test_pre_nms=6000, rpn_test_post_nms=1000, rpn_min_size=1, num_sample=512,
pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=100, **kwargs)
def faster_rcnn_resnet50_v1b_custom(classes, transfer=None, pretrained_base=True,
pretrained=False, **kwargs):
r"""Faster RCNN model with resnet50_v1b base network on custom dataset.
Parameters
----------
classes : iterable of str
Names of custom foreground classes. `len(classes)` is the number of foreground classes.
transfer : str or None
If not `None`, will try to reuse pre-trained weights from faster RCNN networks trained
on other datasets.
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
pretrained_base : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
Returns
-------
mxnet.gluon.HybridBlock
Hybrid faster RCNN network.
"""
if pretrained:
warnings.warn("Custom models don't provide `pretrained` weights, ignored.")
if transfer is None:
from ..resnetv1b import resnet50_v1b
base_network = resnet50_v1b(pretrained=pretrained_base, dilated=False,
use_global_stats=True, **kwargs)
features = nn.HybridSequential()
top_features = nn.HybridSequential()
for layer in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']:
features.add(getattr(base_network, layer))
for layer in ['layer4']:
top_features.add(getattr(base_network, layer))
train_patterns = '|'.join(['.*dense', '.*rpn', '.*down(2|3|4)_conv',
'.*layers(2|3|4)_conv'])
return get_faster_rcnn(
name='resnet50_v1b', dataset='custom', pretrained=pretrained,
features=features, top_features=top_features, classes=classes,
short=600, max_size=1000, train_patterns=train_patterns,
nms_thresh=0.3, nms_topk=400, post_nms=100,
roi_mode='align', roi_size=(14, 14), strides=16, clip=None,
rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32),
ratios=(0.5, 1, 2), alloc_size=(128, 128), rpn_nms_thresh=0.7,
rpn_train_pre_nms=12000, rpn_train_post_nms=2000,
rpn_test_pre_nms=6000, rpn_test_post_nms=300, rpn_min_size=16,
num_sample=128, pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=300,
**kwargs)
else:
from ...model_zoo import get_model
net = get_model('faster_rcnn_resnet50_v1b_' + str(transfer), pretrained=True, **kwargs)
reuse_classes = [x for x in classes if x in net.classes]
net.reset_class(classes, reuse_weights=reuse_classes)
return net
def faster_rcnn_resnet101_v1d_voc(pretrained=False, pretrained_base=True, **kwargs):
r"""Faster RCNN model from the paper
"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards
real-time object detection with region proposal networks"
Parameters
----------
pretrained : bool, optional, default is False
Load pretrained weights.
pretrained_base : bool or str, optional, default is True
Load pretrained base network, the extra layers are randomized. Note that
if pretrained is `True`, this has no effect.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
Examples
--------
>>> model = get_faster_rcnn_resnet101_v1d_voc(pretrained=True)
>>> print(model)
"""
from ..resnetv1b import resnet101_v1d
from ...data import VOCDetection
classes = VOCDetection.CLASSES
pretrained_base = False if pretrained else pretrained_base
base_network = resnet101_v1d(pretrained=pretrained_base, dilated=False,
use_global_stats=True, **kwargs)
features = nn.HybridSequential()
top_features = nn.HybridSequential()
for layer in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']:
features.add(getattr(base_network, layer))
for layer in ['layer4']:
top_features.add(getattr(base_network, layer))
train_patterns = '|'.join(['.*dense', '.*rpn', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv'])
return get_faster_rcnn(
name='resnet101_v1d', dataset='voc', pretrained=pretrained,
features=features, top_features=top_features, classes=classes,
short=600, max_size=1000, train_patterns=train_patterns,
nms_thresh=0.3, nms_topk=400, post_nms=100,
roi_mode='align', roi_size=(14, 14), strides=16, clip=None,
rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32),
ratios=(0.5, 1, 2), alloc_size=(128, 128), rpn_nms_thresh=0.7,
rpn_train_pre_nms=12000, rpn_train_post_nms=2000,
rpn_test_pre_nms=6000, rpn_test_post_nms=300, rpn_min_size=16,
num_sample=128, pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=100,
**kwargs)
def faster_rcnn_resnet101_v1d_coco(pretrained=False, pretrained_base=True, **kwargs):
r"""Faster RCNN model from the paper
"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards
real-time object detection with region proposal networks"
Parameters
----------
pretrained : bool, optional, default is False
Load pretrained weights.
pretrained_base : bool or str, optional, default is True
Load pretrained base network, the extra layers are randomized. Note that
if pretrained is `True`, this has no effect.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
Examples
--------
>>> model = get_faster_rcnn_resnet101_v1d_coco(pretrained=True)
>>> print(model)
"""
from ..resnetv1b import resnet101_v1d
from ...data import COCODetection
classes = COCODetection.CLASSES
pretrained_base = False if pretrained else pretrained_base
base_network = resnet101_v1d(pretrained=pretrained_base, dilated=False,
use_global_stats=True, **kwargs)
features = nn.HybridSequential()
top_features = nn.HybridSequential()
for layer in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']:
features.add(getattr(base_network, layer))
for layer in ['layer4']:
top_features.add(getattr(base_network, layer))
train_patterns = '|'.join(['.*dense', '.*rpn', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv'])
return get_faster_rcnn(
name='resnet101_v1d', dataset='coco', pretrained=pretrained,
features=features, top_features=top_features, classes=classes,
short=800, max_size=1333, train_patterns=train_patterns,
nms_thresh=0.5, nms_topk=-1, post_nms=-1,
roi_mode='align', roi_size=(14, 14), strides=16, clip=4.14,
rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32),
ratios=(0.5, 1, 2), alloc_size=(128, 128), rpn_nms_thresh=0.7,
rpn_train_pre_nms=12000, rpn_train_post_nms=2000,
rpn_test_pre_nms=6000, rpn_test_post_nms=1000, rpn_min_size=1,
num_sample=128, pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=100,
**kwargs)
def faster_rcnn_fpn_resnet101_v1d_coco(pretrained=False, pretrained_base=True, **kwargs):
r"""Faster RCNN model with FPN from the paper
"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards
real-time object detection with region proposal networks"
"Lin, T., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S. (2016).
Feature Pyramid Networks for Object Detection"
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
pretrained_base : bool or str, optional, default is True
Load pretrained base network, the extra layers are randomized. Note that
if pretrained is `Ture`, this has no effect.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
Examples
--------
>>> model = get_faster_rcnn_fpn_resnet101_v1d_coco(pretrained=True)
>>> print(model)
"""
from ..resnetv1b import resnet101_v1d
from ...data import COCODetection
classes = COCODetection.CLASSES
pretrained_base = False if pretrained else pretrained_base
base_network = resnet101_v1d(pretrained=pretrained_base, dilated=False,
use_global_stats=True, **kwargs)
features = FPNFeatureExpander(
network=base_network,
outputs=['layers1_relu8_fwd', 'layers2_relu11_fwd', 'layers3_relu68_fwd',
'layers4_relu8_fwd'], num_filters=[256, 256, 256, 256], use_1x1=True,
use_upsample=True, use_elewadd=True, use_p6=True, no_bias=False, pretrained=pretrained_base)
top_features = None
# 2 FC layer before RCNN cls and reg
box_features = nn.HybridSequential()
for _ in range(2):
box_features.add(nn.Dense(1024, weight_initializer=mx.init.Normal(0.01)))
box_features.add(nn.Activation('relu'))
train_patterns = '|'.join(
['.*dense', '.*rpn', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv', 'P'])
return get_faster_rcnn(
name='fpn_resnet101_v1d', dataset='coco', pretrained=pretrained, features=features,
top_features=top_features, classes=classes, box_features=box_features,
short=800, max_size=1333, min_stage=2, max_stage=6, train_patterns=train_patterns,
nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(7, 7),
strides=(4, 8, 16, 32, 64), clip=4.14, rpn_channel=1024, base_size=16,
scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(384, 384),
rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000,
rpn_test_pre_nms=6000, rpn_test_post_nms=1000, rpn_min_size=1, num_sample=512,
pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=100, **kwargs)
def faster_rcnn_resnet101_v1d_custom(classes, transfer=None, pretrained_base=True,
pretrained=False, **kwargs):
r"""Faster RCNN model with resnet101_v1d base network on custom dataset.
Parameters
----------
classes : iterable of str
Names of custom foreground classes. `len(classes)` is the number of foreground classes.
transfer : str or None
If not `None`, will try to reuse pre-trained weights from faster RCNN networks trained
on other datasets.
pretrained_base : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
Returns
-------
mxnet.gluon.HybridBlock
Hybrid faster RCNN network.
"""
if pretrained:
warnings.warn("Custom models don't provide `pretrained` weights, ignored.")
if transfer is None:
from ..resnetv1b import resnet101_v1d
base_network = resnet101_v1d(pretrained=pretrained_base, dilated=False,
use_global_stats=True, **kwargs)
features = nn.HybridSequential()
top_features = nn.HybridSequential()
for layer in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']:
features.add(getattr(base_network, layer))
for layer in ['layer4']:
top_features.add(getattr(base_network, layer))
train_patterns = '|'.join(['.*dense', '.*rpn', '.*down(2|3|4)_conv',
'.*layers(2|3|4)_conv'])
return get_faster_rcnn(
name='resnet101_v1d', dataset='custom', pretrained=pretrained,
features=features, top_features=top_features, classes=classes,
short=600, max_size=1000, train_patterns=train_patterns,
nms_thresh=0.3, nms_topk=400, post_nms=100,
roi_mode='align', roi_size=(14, 14), strides=16, clip=None,
rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32),
ratios=(0.5, 1, 2), alloc_size=(128, 128), rpn_nms_thresh=0.7,
rpn_train_pre_nms=12000, rpn_train_post_nms=2000,
rpn_test_pre_nms=6000, rpn_test_post_nms=300, rpn_min_size=16,
num_sample=128, pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=300,
**kwargs)
else:
from ...model_zoo import get_model
net = get_model('faster_rcnn_resnet101_v1d_' + str(transfer), pretrained=True, **kwargs)
reuse_classes = [x for x in classes if x in net.classes]
net.reset_class(classes, reuse_weights=reuse_classes)
return net
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