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model.py
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"""
Mask R-CNN
The main Mask R-CNN model implementation.
Copyright (c) 2017 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by Waleed Abdulla
"""
import os
import random
import datetime
import re
import math
import logging
from collections import OrderedDict
import multiprocessing
import numpy as np
import skimage.transform
import tensorflow as tf
import keras
import keras.backend as K
import keras.layers as KL
import keras.engine as KE
import keras.models as KM
import utils
import time
# Requires TensorFlow 1.3+ and Keras 2.0.8+.
from distutils.version import LooseVersion
assert LooseVersion(tf.__version__) >= LooseVersion("1.3")
assert LooseVersion(keras.__version__) >= LooseVersion('2.0.8')
############################################################
# Utility Functions
############################################################
def log(text, array=None):
"""Prints a text message. And, optionally, if a Numpy array is provided it
prints it's shape, min, and max values.
"""
if array is not None:
text = text.ljust(25)
text += ("shape: {:20} min: {:10.5f} max: {:10.5f} {}".format(
str(array.shape),
array.min() if array.size else "",
array.max() if array.size else "",
array.dtype))
print(text)
class BatchNorm(KL.BatchNormalization):
"""Extends the Keras BatchNormalization class to allow a central place
to make changes if needed.
Batch normalization has a negative effect on training if batches are small
so this layer is often frozen (via setting in Config class) and functions
as linear layer.
"""
def call(self, inputs, training=None):
"""
Note about training values:
None: Train BN layers. This is the normal mode
False: Freeze BN layers. Good when batch size is small
True: (don't use). Set layer in training mode even when inferencing
"""
return super(self.__class__, self).call(inputs, training=training)
############################################################
# Group Normalization
############################################################
from keras.engine import Layer, InputSpec
from keras import initializers
from keras import regularizers
from keras import constraints
from keras import backend as K
from keras.utils.generic_utils import get_custom_objects
class GroupNormalization(Layer):
"""
Retrieved from https://github.com/titu1994/Keras-Group-Normalization
"""
"""Group normalization layer
Group Normalization divides the channels into groups and computes within each group
the mean and variance for normalization. GN's computation is independent of batch sizes,
and its accuracy is stable in a wide range of batch sizes
# Arguments
groups: Integer, the number of groups for Group Normalization.
axis: Integer, the axis that should be normalized
(typically the features axis).
For instance, after a `Conv2D` layer with
`data_format="channels_first"`,
set `axis=1` in `BatchNormalization`.
epsilon: Small float added to variance to avoid dividing by zero.
center: If True, add offset of `beta` to normalized tensor.
If False, `beta` is ignored.
scale: If True, multiply by `gamma`.
If False, `gamma` is not used.
When the next layer is linear (also e.g. `nn.relu`),
this can be disabled since the scaling
will be done by the next layer.
beta_initializer: Initializer for the beta weight.
gamma_initializer: Initializer for the gamma weight.
beta_regularizer: Optional regularizer for the beta weight.
gamma_regularizer: Optional regularizer for the gamma weight.
beta_constraint: Optional constraint for the beta weight.
gamma_constraint: Optional constraint for the gamma weight.
# Input shape
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
# Output shape
Same shape as input.
# References
- [Group Normalization](https://arxiv.org/abs/1803.08494)
"""
def __init__(self,
groups=32,
axis=-1,
epsilon=1e-5,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
trainable=None,
**kwargs):
super(GroupNormalization, self).__init__(**kwargs)
self.supports_masking = True
self.groups = groups
self.axis = axis
self.epsilon = epsilon
self.center = center
self.scale = scale
self.beta_initializer = initializers.get(beta_initializer)
self.gamma_initializer = initializers.get(gamma_initializer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_constraint = constraints.get(beta_constraint)
self.gamma_constraint = constraints.get(gamma_constraint)
self.trainable = trainable
def build(self, input_shape, trainable):
dim = input_shape[self.axis]
if dim is None:
raise ValueError('Axis ' + str(self.axis) + ' of '
'input tensor should have a defined dimension '
'but the layer received an input with shape ' +
str(input_shape) + '.')
if dim < self.groups:
raise ValueError('Number of groups (' + str(self.groups) + ') cannot be '
'more than the number of channels (' +
str(dim) + ').')
if dim % self.groups != 0:
raise ValueError('Number of groups (' + str(self.groups) + ') must be a '
'multiple of the number of channels (' +
str(dim) + ').')
self.input_spec = InputSpec(ndim=len(input_shape),
axes={self.axis: dim})
shape = (dim,)
if self.scale:
self.gamma = self.add_weight(shape=shape,
name='gamma',
initializer=self.gamma_initializer,
regularizer=self.gamma_regularizer,
constraint=self.gamma_constraint,
trainable=True)
else:
self.gamma = None
if self.center:
self.beta = self.add_weight(shape=shape,
name='beta',
initializer=self.beta_initializer,
regularizer=self.beta_regularizer,
constraint=self.beta_constraint,
trainable=True)
else:
self.beta = None
self.built = True
def call(self, inputs, **kwargs):
input_shape = K.int_shape(inputs)
tensor_input_shape = K.shape(inputs)
# Prepare broadcasting shape.
reduction_axes = list(range(len(input_shape)))
del reduction_axes[self.axis]
broadcast_shape = [1] * len(input_shape)
broadcast_shape[self.axis] = input_shape[self.axis] // self.groups
broadcast_shape.insert(1, self.groups)
reshape_group_shape = K.shape(inputs)
group_axes = [reshape_group_shape[i] for i in range(len(input_shape))]
group_axes[self.axis] = input_shape[self.axis] // self.groups
group_axes.insert(1, self.groups)
# reshape inputs to new group shape
group_shape = [group_axes[0], self.groups] + group_axes[2:]
group_shape = K.stack(group_shape)
inputs = K.reshape(inputs, group_shape)
group_reduction_axes = list(range(len(group_axes)))
group_reduction_axes = group_reduction_axes[2:]
mean = K.mean(inputs, axis=group_reduction_axes, keepdims=True)
variance = K.var(inputs, axis=group_reduction_axes, keepdims=True)
inputs = (inputs - mean) / (K.sqrt(variance + self.epsilon))
# prepare broadcast shape
inputs = K.reshape(inputs, group_shape)
outputs = inputs
# In this case we must explicitly broadcast all parameters.
if self.scale:
broadcast_gamma = K.reshape(self.gamma, broadcast_shape)
outputs = outputs * broadcast_gamma
if self.center:
broadcast_beta = K.reshape(self.beta, broadcast_shape)
outputs = outputs + broadcast_beta
outputs = K.reshape(outputs, tensor_input_shape)
return outputs
def get_config(self):
config = {
'groups': self.groups,
'axis': self.axis,
'epsilon': self.epsilon,
'center': self.center,
'scale': self.scale,
'beta_initializer': initializers.serialize(self.beta_initializer),
'gamma_initializer': initializers.serialize(self.gamma_initializer),
'beta_regularizer': regularizers.serialize(self.beta_regularizer),
'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
'beta_constraint': constraints.serialize(self.beta_constraint),
'gamma_constraint': constraints.serialize(self.gamma_constraint)
}
base_config = super(GroupNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
class GroupNorm(GroupNormalization):
"""Group Normalization still performs well when the model is trained with
low batch sizes. Can be activated via the config class.
"""
def __init__(self, training=None, **kwargs):
super(GroupNorm, self).__init__(**kwargs)
self.trainable = training
def call(self, inputs):
return super(self.__class__, self).call(inputs)
def build(self, inputs, trainable=None):
"""
Note about training values:
None: Train GN layers. This is the normal mode
False: Freeze GN layers. Good when batch size is small
True: Train GN layers
"""
return super(self.__class__, self).build(inputs, self.trainable)
def compute_backbone_shapes(config, image_shape):
"""Computes the width and height of each stage of the backbone network.
Returns:
[N, (height, width)]. Where N is the number of stages
"""
if callable(config.BACKBONE):
return config.COMPUTE_BACKBONE_SHAPE(image_shape)
# Currently supports ResNet only
assert config.BACKBONE in ["resnet50", "resnet101"]
return np.array(
[[int(math.ceil(image_shape[0] / stride)),
int(math.ceil(image_shape[1] / stride))]
for stride in config.BACKBONE_STRIDES])
############################################################
# Resnet Graph
############################################################
# Code adopted from:
# https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py
def identity_block(input_tensor, kernel_size, filters, stage, block,
use_bias=True, train_bn=True, train_gn=True,
init_bn=True, init_gn=False):
"""The identity_block is the block that has no conv layer at shortcut
# Arguments
input_tensor: input tensor
kernel_size: default 3, the kernel size of middle conv layer at main path
filters: list of integers, the nb_filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
use_bias: Boolean. To use or not use a bias in conv layers.
train_bn: Boolean. Train or freeze Batch Norm layers.
"""
nb_filter1, nb_filter2, nb_filter3 = filters
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
gn_name_base = 'gn' + str(stage) + block + '_branch'
x = KL.Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a',
use_bias=use_bias)(input_tensor)
if init_bn:
x = BatchNorm(name=bn_name_base + '2a')(x, training=train_bn)
if init_gn:
x = GroupNorm(name=gn_name_base + '2a', training=train_gn)(x)
x = KL.Activation('relu')(x)
x = KL.Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same',
name=conv_name_base + '2b', use_bias=use_bias)(x)
if init_bn:
x = BatchNorm(name=bn_name_base + '2b')(x, training=train_bn)
if init_gn:
x = GroupNorm(name=gn_name_base + '2b', training=train_gn)(x)
x = KL.Activation('relu')(x)
x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c',
use_bias=use_bias)(x)
if init_bn:
x = BatchNorm(name=bn_name_base + '2c')(x, training=train_bn)
if init_gn:
x = GroupNorm(name=gn_name_base + '2c', training=train_gn,
gamma_initializer='zeros')(x)
x = KL.Add()([x, input_tensor])
x = KL.Activation('relu', name='res' + str(stage) + block + '_out')(x)
return x
def conv_block(input_tensor, kernel_size, filters, stage, block,
strides=(2, 2), use_bias=True, train_bn=True,
train_gn=True, init_bn=True, init_gn=False):
"""conv_block is the block that has a conv layer at shortcut
# Arguments
input_tensor: input tensor
kernel_size: default 3, the kernel size of middle conv layer at main path
filters: list of integers, the nb_filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
use_bias: Boolean. To use or not use a bias in conv layers.
train_bn: Boolean. Train or freeze Batch Norm layers
Note that from stage 3, the first conv layer at main path is with subsample=(2,2)
And the shortcut should have subsample=(2,2) as well
"""
nb_filter1, nb_filter2, nb_filter3 = filters
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
gn_name_base = 'gn' + str(stage) + block + '_branch'
x = KL.Conv2D(nb_filter1, (1, 1), strides=strides,
name=conv_name_base + '2a', use_bias=use_bias)(input_tensor)
if init_bn:
x = BatchNorm(name=bn_name_base + '2a')(x, training=train_bn)
if init_gn:
x = GroupNorm(name=gn_name_base + '2a', training=train_gn)(x)
x = KL.Activation('relu')(x)
x = KL.Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same',
name=conv_name_base + '2b', use_bias=use_bias)(x)
if init_bn:
x = BatchNorm(name=bn_name_base + '2b')(x, training=train_bn)
if init_gn:
x = GroupNorm(name=gn_name_base + '2b', training=train_gn)(x)
x = KL.Activation('relu')(x)
x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base +
'2c', use_bias=use_bias)(x)
if init_bn:
x = BatchNorm(name=bn_name_base + '2c')(x, training=train_bn)
if init_gn:
x = GroupNorm(name=gn_name_base + '2c', training=train_gn,
gamma_initializer='zeros')(x)
shortcut = KL.Conv2D(nb_filter3, (1, 1), strides=strides,
name=conv_name_base + '1', use_bias=use_bias)(input_tensor)
if init_bn:
shortcut = BatchNorm(name=bn_name_base + '1')(shortcut, training=train_bn)
if init_gn:
shortcut = GroupNorm(name=gn_name_base + '1', training=train_gn)(shortcut)
x = KL.Add()([x, shortcut])
x = KL.Activation('relu', name='res' + str(stage) + block + '_out')(x)
return x
def resnet_graph(input_image, architecture, stage5=False, train_bn=False, train_gn=False,
init_bn=True, init_gn=False):
"""Build a ResNet graph.
architecture: Can be resnet50 or resnet101
stage5: Boolean. If False, stage5 of the network is not created
train_bn: Boolean. Train or freeze Batch Norm layers
"""
assert architecture in ["resnet50", "resnet101"]
# Stage 1
x = KL.ZeroPadding2D((3, 3))(input_image)
x = KL.Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=True)(x)
if init_bn:
x = BatchNorm(name='bn_conv1')(x, training=train_bn)
if init_gn:
x = GroupNorm(name='gn_conv1', training=train_gn)(x)
x = KL.Activation('relu')(x)
C1 = x = KL.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x)
# Stage 2
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1), train_bn=train_bn, train_gn=train_gn, init_bn=init_bn, init_gn=init_gn)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', train_bn=train_bn, train_gn=train_gn, init_bn=init_bn, init_gn=init_gn)
C2 = x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', train_bn=train_bn, train_gn=train_gn, init_bn=init_bn, init_gn=init_gn)
# Stage 3
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', train_bn=train_bn, train_gn=train_gn,
init_bn=init_bn, init_gn=init_gn)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', train_bn=train_bn, train_gn=train_gn, init_bn=init_bn, init_gn=init_gn)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', train_bn=train_bn, train_gn=train_gn, init_bn=init_bn, init_gn=init_gn)
C3 = x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', train_bn=train_bn, train_gn=train_gn, init_bn=init_bn, init_gn=init_gn)
# Stage 4
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', train_bn=train_bn, train_gn=train_gn, init_bn=init_bn, init_gn=init_gn)
block_count = {"resnet50": 5, "resnet101": 22}[architecture]
for i in range(block_count):
x = identity_block(x, 3, [256, 256, 1024], stage=4, block=chr(98 + i), train_bn=train_bn, train_gn=train_gn, init_bn=init_bn, init_gn=init_gn)
C4 = x
# Stage 5
if stage5:
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a', train_bn=train_bn, train_gn=train_gn, init_bn=init_bn, init_gn=init_gn)
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b', train_bn=train_bn, train_gn=train_gn, init_bn=init_bn, init_gn=init_gn)
C5 = x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c', train_bn=train_bn, train_gn=train_gn, init_bn=init_bn, init_gn=init_gn)
else:
C5 = None
return [C1, C2, C3, C4, C5]
############################################################
# Proposal Layer
############################################################
def apply_box_deltas_graph(boxes, deltas):
"""Applies the given deltas to the given boxes.
boxes: [N, (y1, x1, y2, x2)] boxes to update
deltas: [N, (dy, dx, log(dh), log(dw))] refinements to apply
"""
# Convert to y, x, h, w
height = boxes[:, 2] - boxes[:, 0]
width = boxes[:, 3] - boxes[:, 1]
center_y = boxes[:, 0] + 0.5 * height
center_x = boxes[:, 1] + 0.5 * width
# Apply deltas
center_y += deltas[:, 0] * height
center_x += deltas[:, 1] * width
height *= tf.exp(deltas[:, 2])
width *= tf.exp(deltas[:, 3])
# Convert back to y1, x1, y2, x2
y1 = center_y - 0.5 * height
x1 = center_x - 0.5 * width
y2 = y1 + height
x2 = x1 + width
result = tf.stack([y1, x1, y2, x2], axis=1, name="apply_box_deltas_out")
return result
def clip_boxes_graph(boxes, window):
"""
boxes: [N, (y1, x1, y2, x2)]
window: [4] in the form y1, x1, y2, x2
"""
# Split
wy1, wx1, wy2, wx2 = tf.split(window, 4)
y1, x1, y2, x2 = tf.split(boxes, 4, axis=1)
# Clip
y1 = tf.maximum(tf.minimum(y1, wy2), wy1)
x1 = tf.maximum(tf.minimum(x1, wx2), wx1)
y2 = tf.maximum(tf.minimum(y2, wy2), wy1)
x2 = tf.maximum(tf.minimum(x2, wx2), wx1)
clipped = tf.concat([y1, x1, y2, x2], axis=1, name="clipped_boxes")
clipped.set_shape((clipped.shape[0], 4))
return clipped
class ProposalLayer(KE.Layer):
"""Receives anchor scores and selects a subset to pass as proposals
to the second stage. Filtering is done based on anchor scores and
non-max suppression to remove overlaps. It also applies bounding
box refinement deltas to anchors.
Inputs:
rpn_probs: [batch, anchors, (bg prob, fg prob)]
rpn_bbox: [batch, anchors, (dy, dx, log(dh), log(dw))]
anchors: [batch, (y1, x1, y2, x2)] anchors in normalized coordinates
Returns:
Proposals in normalized coordinates [batch, rois, (y1, x1, y2, x2)]
"""
def __init__(self, proposal_count, nms_threshold, config=None, **kwargs):
super(ProposalLayer, self).__init__(**kwargs)
self.config = config
self.proposal_count = proposal_count
self.nms_threshold = nms_threshold
def call(self, inputs):
# Box Scores. Use the foreground class confidence. [Batch, num_rois, 1]
scores = inputs[0][:, :, 1]
# Box deltas [batch, num_rois, 4]
deltas = inputs[1]
deltas = deltas * np.reshape(self.config.RPN_BBOX_STD_DEV, [1, 1, 4])
# Anchors
anchors = inputs[2]
# Improve performance by trimming to top anchors by score
# and doing the rest on the smaller subset.
pre_nms_limit = tf.minimum(6000, tf.shape(anchors)[1])
ix = tf.nn.top_k(scores, pre_nms_limit, sorted=True,
name="top_anchors").indices
scores = utils.batch_slice([scores, ix], lambda x, y: tf.gather(x, y),
self.config.IMAGES_PER_GPU)
deltas = utils.batch_slice([deltas, ix], lambda x, y: tf.gather(x, y),
self.config.IMAGES_PER_GPU)
pre_nms_anchors = utils.batch_slice([anchors, ix], lambda a, x: tf.gather(a, x),
self.config.IMAGES_PER_GPU,
names=["pre_nms_anchors"])
# Apply deltas to anchors to get refined anchors.
# [batch, N, (y1, x1, y2, x2)]
boxes = utils.batch_slice([pre_nms_anchors, deltas],
lambda x, y: apply_box_deltas_graph(x, y),
self.config.IMAGES_PER_GPU,
names=["refined_anchors"])
# Clip to image boundaries. Since we're in normalized coordinates,
# clip to 0..1 range. [batch, N, (y1, x1, y2, x2)]
window = np.array([0, 0, 1, 1], dtype=np.float32)
boxes = utils.batch_slice(boxes,
lambda x: clip_boxes_graph(x, window),
self.config.IMAGES_PER_GPU,
names=["refined_anchors_clipped"])
# Filter out small boxes
# According to Xinlei Chen's paper, this reduces detection accuracy
# for small objects, so we're skipping it.
# Non-max suppression
def nms(boxes, scores):
indices = tf.image.non_max_suppression(
boxes, scores, self.proposal_count,
self.nms_threshold, name="rpn_non_max_suppression")
proposals = tf.gather(boxes, indices)
# Pad if needed
padding = tf.maximum(self.proposal_count - tf.shape(proposals)[0], 0)
proposals = tf.pad(proposals, [(0, padding), (0, 0)])
return proposals
proposals = utils.batch_slice([boxes, scores], nms,
self.config.IMAGES_PER_GPU)
return proposals
def compute_output_shape(self, input_shape):
return (None, self.proposal_count, 4)
############################################################
# ROIAlign Layer
############################################################
def log2_graph(x):
"""Implementation of Log2. TF doesn't have a native implementation."""
return tf.log(x) / tf.log(2.0)
class PyramidROIAlign(KE.Layer):
"""Implements ROI Pooling on multiple levels of the feature pyramid.
Params:
- pool_shape: [height, width] of the output pooled regions. Usually [7, 7]
Inputs:
- boxes: [batch, num_boxes, (y1, x1, y2, x2)] in normalized
coordinates. Possibly padded with zeros if not enough
boxes to fill the array.
- image_meta: [batch, (meta data)] Image details. See compose_image_meta()
- Feature maps: List of feature maps from different levels of the pyramid.
Each is [batch, height, width, channels]
Output:
Pooled regions in the shape: [batch, num_boxes, height, width, channels].
The width and height are those specific in the pool_shape in the layer
constructor.
"""
def __init__(self, pool_shape, **kwargs):
super(PyramidROIAlign, self).__init__(**kwargs)
self.pool_shape = tuple(pool_shape)
def call(self, inputs):
# Crop boxes [batch, num_boxes, (y1, x1, y2, x2)] in normalized coords
boxes = inputs[0]
# Image meta
# Holds details about the image. See compose_image_meta()
image_meta = inputs[1]
# Feature Maps. List of feature maps from different level of the
# feature pyramid. Each is [batch, height, width, channels]
feature_maps = inputs[2:]
# Assign each ROI to a level in the pyramid based on the ROI area.
y1, x1, y2, x2 = tf.split(boxes, 4, axis=2)
h = y2 - y1
w = x2 - x1
# Use shape of first image. Images in a batch must have the same size.
image_shape = parse_image_meta_graph(image_meta)['image_shape'][0]
# Equation 1 in the Feature Pyramid Networks paper. Account for
# the fact that our coordinates are normalized here.
# e.g. a 224x224 ROI (in pixels) maps to P4
image_area = tf.cast(image_shape[0] * image_shape[1], tf.float32)
roi_level = log2_graph(tf.sqrt(h * w) / (224.0 / tf.sqrt(image_area)))
roi_level = tf.minimum(5, tf.maximum(
2, 4 + tf.cast(tf.round(roi_level), tf.int32)))
roi_level = tf.squeeze(roi_level, 2)
# Loop through levels and apply ROI pooling to each. P2 to P5.
pooled = []
box_to_level = []
for i, level in enumerate(range(2, 6)):
ix = tf.where(tf.equal(roi_level, level))
level_boxes = tf.gather_nd(boxes, ix)
# Box indices for crop_and_resize.
box_indices = tf.cast(ix[:, 0], tf.int32)
# Keep track of which box is mapped to which level
box_to_level.append(ix)
# Stop gradient propogation to ROI proposals
level_boxes = tf.stop_gradient(level_boxes)
box_indices = tf.stop_gradient(box_indices)
# Crop and Resize
# From Mask R-CNN paper: "We sample four regular locations, so
# that we can evaluate either max or average pooling. In fact,
# interpolating only a single value at each bin center (without
# pooling) is nearly as effective."
#
# Here we use the simplified approach of a single value per bin,
# which is how it's done in tf.crop_and_resize()
# Result: [batch * num_boxes, pool_height, pool_width, channels]
pooled.append(tf.image.crop_and_resize(
feature_maps[i], level_boxes, box_indices, self.pool_shape,
method="bilinear"))
# Pack pooled features into one tensor
pooled = tf.concat(pooled, axis=0)
# Pack box_to_level mapping into one array and add another
# column representing the order of pooled boxes
box_to_level = tf.concat(box_to_level, axis=0)
box_range = tf.expand_dims(tf.range(tf.shape(box_to_level)[0]), 1)
box_to_level = tf.concat([tf.cast(box_to_level, tf.int32), box_range],
axis=1)
# Rearrange pooled features to match the order of the original boxes
# Sort box_to_level by batch then box index
# TF doesn't have a way to sort by two columns, so merge them and sort.
sorting_tensor = box_to_level[:, 0] * 100000 + box_to_level[:, 1]
ix = tf.nn.top_k(sorting_tensor, k=tf.shape(
box_to_level)[0]).indices[::-1]
ix = tf.gather(box_to_level[:, 2], ix)
pooled = tf.gather(pooled, ix)
# Re-add the batch dimension
pooled = tf.expand_dims(pooled, 0)
return pooled
def compute_output_shape(self, input_shape):
return input_shape[0][:2] + self.pool_shape + (input_shape[2][-1], )
############################################################
# Detection Target Layer
############################################################
def overlaps_graph(boxes1, boxes2):
"""Computes IoU overlaps between two sets of boxes.
boxes1, boxes2: [N, (y1, x1, y2, x2)].
"""
# 1. Tile boxes2 and repeat boxes1. This allows us to compare
# every boxes1 against every boxes2 without loops.
# TF doesn't have an equivalent to np.repeat() so simulate it
# using tf.tile() and tf.reshape.
b1 = tf.reshape(tf.tile(tf.expand_dims(boxes1, 1),
[1, 1, tf.shape(boxes2)[0]]), [-1, 4])
b2 = tf.tile(boxes2, [tf.shape(boxes1)[0], 1])
# 2. Compute intersections
b1_y1, b1_x1, b1_y2, b1_x2 = tf.split(b1, 4, axis=1)
b2_y1, b2_x1, b2_y2, b2_x2 = tf.split(b2, 4, axis=1)
y1 = tf.maximum(b1_y1, b2_y1)
x1 = tf.maximum(b1_x1, b2_x1)
y2 = tf.minimum(b1_y2, b2_y2)
x2 = tf.minimum(b1_x2, b2_x2)
intersection = tf.maximum(x2 - x1, 0) * tf.maximum(y2 - y1, 0)
# 3. Compute unions
b1_area = (b1_y2 - b1_y1) * (b1_x2 - b1_x1)
b2_area = (b2_y2 - b2_y1) * (b2_x2 - b2_x1)
union = b1_area + b2_area - intersection
# 4. Compute IoU and reshape to [boxes1, boxes2]
iou = intersection / union
overlaps = tf.reshape(iou, [tf.shape(boxes1)[0], tf.shape(boxes2)[0]])
return overlaps
def detection_targets_graph(proposals, gt_class_ids, gt_boxes, gt_masks, config):
"""Generates detection targets for one image. Subsamples proposals and
generates target class IDs, bounding box deltas, and masks for each.
Inputs:
proposals: [N, (y1, x1, y2, x2)] in normalized coordinates. Might
be zero padded if there are not enough proposals.
gt_class_ids: [MAX_GT_INSTANCES] int class IDs
gt_boxes: [MAX_GT_INSTANCES, (y1, x1, y2, x2)] in normalized coordinates.
gt_masks: [height, width, MAX_GT_INSTANCES] of boolean type.
Returns: Target ROIs and corresponding class IDs, bounding box shifts,
and masks.
rois: [TRAIN_ROIS_PER_IMAGE, (y1, x1, y2, x2)] in normalized coordinates
class_ids: [TRAIN_ROIS_PER_IMAGE]. Integer class IDs. Zero padded.
deltas: [TRAIN_ROIS_PER_IMAGE, NUM_CLASSES, (dy, dx, log(dh), log(dw))]
Class-specific bbox refinements.
masks: [TRAIN_ROIS_PER_IMAGE, height, width). Masks cropped to bbox
boundaries and resized to neural network output size.
Note: Returned arrays might be zero padded if not enough target ROIs.
"""
# Assertions
asserts = [
tf.Assert(tf.greater(tf.shape(proposals)[0], 0), [proposals],
name="roi_assertion"),
]
with tf.control_dependencies(asserts):
proposals = tf.identity(proposals)
# Remove zero padding
proposals, _ = trim_zeros_graph(proposals, name="trim_proposals")
gt_boxes, non_zeros = trim_zeros_graph(gt_boxes, name="trim_gt_boxes")
gt_class_ids = tf.boolean_mask(gt_class_ids, non_zeros,
name="trim_gt_class_ids")
gt_masks = tf.gather(gt_masks, tf.where(non_zeros)[:, 0], axis=2,
name="trim_gt_masks")
# Handle COCO crowds
# A crowd box in COCO is a bounding box around several instances. Exclude
# them from training. A crowd box is given a negative class ID.
crowd_ix = tf.where(gt_class_ids < 0)[:, 0]
non_crowd_ix = tf.where(gt_class_ids > 0)[:, 0]
crowd_boxes = tf.gather(gt_boxes, crowd_ix)
crowd_masks = tf.gather(gt_masks, crowd_ix, axis=2)
gt_class_ids = tf.gather(gt_class_ids, non_crowd_ix)
gt_boxes = tf.gather(gt_boxes, non_crowd_ix)
gt_masks = tf.gather(gt_masks, non_crowd_ix, axis=2)
# Compute overlaps matrix [proposals, gt_boxes]
overlaps = overlaps_graph(proposals, gt_boxes)
# Compute overlaps with crowd boxes [anchors, crowds]
crowd_overlaps = overlaps_graph(proposals, crowd_boxes)
crowd_iou_max = tf.reduce_max(crowd_overlaps, axis=1)
no_crowd_bool = (crowd_iou_max < 0.001)
# Determine positive and negative ROIs
roi_iou_max = tf.reduce_max(overlaps, axis=1)
# 1. Positive ROIs are those with >= 0.5 IoU with a GT box
positive_roi_bool = (roi_iou_max >= 0.5)
positive_indices = tf.where(positive_roi_bool)[:, 0]
# 2. Negative ROIs are those with < 0.5 with every GT box. Skip crowds.
negative_indices = tf.where(tf.logical_and(roi_iou_max < 0.5, no_crowd_bool))[:, 0]
# Subsample ROIs. Aim for 33% positive
# Positive ROIs
positive_count = int(config.TRAIN_ROIS_PER_IMAGE *
config.ROI_POSITIVE_RATIO)
positive_indices = tf.random_shuffle(positive_indices)[:positive_count]
positive_count = tf.shape(positive_indices)[0]
# Negative ROIs. Add enough to maintain positive:negative ratio.
r = 1.0 / config.ROI_POSITIVE_RATIO
negative_count = tf.cast(r * tf.cast(positive_count, tf.float32), tf.int32) - positive_count
negative_indices = tf.random_shuffle(negative_indices)[:negative_count]
# Gather selected ROIs
positive_rois = tf.gather(proposals, positive_indices)
negative_rois = tf.gather(proposals, negative_indices)
# Assign positive ROIs to GT boxes.
positive_overlaps = tf.gather(overlaps, positive_indices)
roi_gt_box_assignment = tf.argmax(positive_overlaps, axis=1)
# roi_gt_box_assignment = tf.cond(
# tf.greater(tf.shape(positive_overlaps)[1], 0),
# true_fn = lambda: tf.argmax(positive_overlaps, axis=1),
# false_fn = lambda: tf.cast(tf.constant([]),tf.int64)
# )
roi_gt_boxes = tf.gather(gt_boxes, roi_gt_box_assignment)
roi_gt_class_ids = tf.gather(gt_class_ids, roi_gt_box_assignment)
# Compute bbox refinement for positive ROIs
deltas = utils.box_refinement_graph(positive_rois, roi_gt_boxes)
deltas /= config.BBOX_STD_DEV
# Assign positive ROIs to GT masks
# Permute masks to [N, height, width, 1]
transposed_masks = tf.expand_dims(tf.transpose(gt_masks, [2, 0, 1]), -1)
# Pick the right mask for each ROI
roi_masks = tf.gather(transposed_masks, roi_gt_box_assignment)
# Compute mask targets
boxes = positive_rois
if config.USE_MINI_MASK:
# Transform ROI coordinates from normalized image space
# to normalized mini-mask space.
y1, x1, y2, x2 = tf.split(positive_rois, 4, axis=1)
gt_y1, gt_x1, gt_y2, gt_x2 = tf.split(roi_gt_boxes, 4, axis=1)
gt_h = gt_y2 - gt_y1
gt_w = gt_x2 - gt_x1
y1 = (y1 - gt_y1) / gt_h
x1 = (x1 - gt_x1) / gt_w
y2 = (y2 - gt_y1) / gt_h
x2 = (x2 - gt_x1) / gt_w
boxes = tf.concat([y1, x1, y2, x2], 1)
box_ids = tf.range(0, tf.shape(roi_masks)[0])
masks = tf.image.crop_and_resize(tf.cast(roi_masks, tf.float32), boxes,
box_ids,
config.MASK_SHAPE)
# Remove the extra dimension from masks.
masks = tf.squeeze(masks, axis=3)
# Threshold mask pixels at 0.5 to have GT masks be 0 or 1 to use with
# binary cross entropy loss.
masks = tf.round(masks)
# Append negative ROIs and pad bbox deltas and masks that
# are not used for negative ROIs with zeros.
rois = tf.concat([positive_rois, negative_rois], axis=0)
N = tf.shape(negative_rois)[0]
P = tf.maximum(config.TRAIN_ROIS_PER_IMAGE - tf.shape(rois)[0], 0)
rois = tf.pad(rois, [(0, P), (0, 0)])
roi_gt_boxes = tf.pad(roi_gt_boxes, [(0, N + P), (0, 0)])
roi_gt_class_ids = tf.pad(roi_gt_class_ids, [(0, N + P)])
deltas = tf.pad(deltas, [(0, N + P), (0, 0)])
masks = tf.pad(masks, [[0, N + P], (0, 0), (0, 0)])
return rois, roi_gt_class_ids, deltas, masks
class DetectionTargetLayer(KE.Layer):
"""Subsamples proposals and generates target box refinement, class_ids,
and masks for each.
Inputs:
proposals: [batch, N, (y1, x1, y2, x2)] in normalized coordinates. Might
be zero padded if there are not enough proposals.
gt_class_ids: [batch, MAX_GT_INSTANCES] Integer class IDs.
gt_boxes: [batch, MAX_GT_INSTANCES, (y1, x1, y2, x2)] in normalized
coordinates.
gt_masks: [batch, height, width, MAX_GT_INSTANCES] of boolean type
Returns: Target ROIs and corresponding class IDs, bounding box shifts,
and masks.
rois: [batch, TRAIN_ROIS_PER_IMAGE, (y1, x1, y2, x2)] in normalized
coordinates
target_class_ids: [batch, TRAIN_ROIS_PER_IMAGE]. Integer class IDs.
target_deltas: [batch, TRAIN_ROIS_PER_IMAGE, NUM_CLASSES,
(dy, dx, log(dh), log(dw), class_id)]
Class-specific bbox refinements.
target_mask: [batch, TRAIN_ROIS_PER_IMAGE, height, width)
Masks cropped to bbox boundaries and resized to neural
network output size.
Note: Returned arrays might be zero padded if not enough target ROIs.
"""
def __init__(self, config, **kwargs):
super(DetectionTargetLayer, self).__init__(**kwargs)
self.config = config
def call(self, inputs):
proposals = inputs[0]
gt_class_ids = inputs[1]
gt_boxes = inputs[2]
gt_masks = inputs[3]
# Slice the batch and run a graph for each slice
# TODO: Rename target_bbox to target_deltas for clarity
names = ["rois", "target_class_ids", "target_bbox", "target_mask"]
outputs = utils.batch_slice(
[proposals, gt_class_ids, gt_boxes, gt_masks],
lambda w, x, y, z: detection_targets_graph(
w, x, y, z, self.config),
self.config.IMAGES_PER_GPU, names=names)
return outputs
def compute_output_shape(self, input_shape):
return [
(None, self.config.TRAIN_ROIS_PER_IMAGE, 4), # rois
(None, 1), # class_ids
(None, self.config.TRAIN_ROIS_PER_IMAGE, 4), # deltas
(None, self.config.TRAIN_ROIS_PER_IMAGE, self.config.MASK_SHAPE[0],
self.config.MASK_SHAPE[1]) # masks
]
def compute_mask(self, inputs, mask=None):
return [None, None, None, None]
############################################################
# Detection Layer
############################################################
def refine_detections_graph(rois, probs, deltas, window, config):
"""Refine classified proposals and filter overlaps and return final
detections.
Inputs:
rois: [N, (y1, x1, y2, x2)] in normalized coordinates
probs: [N, num_classes]. Class probabilities.
deltas: [N, num_classes, (dy, dx, log(dh), log(dw))]. Class-specific
bounding box deltas.
window: (y1, x1, y2, x2) in image coordinates. The part of the image
that contains the image excluding the padding.
Returns detections shaped: [N, (y1, x1, y2, x2, class_id, score)] where
coordinates are normalized.
"""
# Class IDs per ROI
class_ids = tf.argmax(probs, axis=1, output_type=tf.int32)
# Class probability of the top class of each ROI
indices = tf.stack([tf.range(probs.shape[0]), class_ids], axis=1)
class_scores = tf.gather_nd(probs, indices)
# Class-specific bounding box deltas
deltas_specific = tf.gather_nd(deltas, indices)
# Apply bounding box deltas
# Shape: [boxes, (y1, x1, y2, x2)] in normalized coordinates
refined_rois = apply_box_deltas_graph(
rois, deltas_specific * config.BBOX_STD_DEV)
# Clip boxes to image window
refined_rois = clip_boxes_graph(refined_rois, window)
# TODO: Filter out boxes with zero area
# Filter out background boxes
if config.FILTER_BACKGROUND:
keep = tf.where(class_ids > 0)[:, 0]
else:
keep = tf.where(class_ids >= 0)[:, 0]
# Filter out low confidence boxes
if config.DETECTION_MIN_CONFIDENCE:
conf_keep = tf.where(class_scores >= config.DETECTION_MIN_CONFIDENCE)[:, 0]
keep = tf.sets.set_intersection(tf.expand_dims(keep, 0),
tf.expand_dims(conf_keep, 0))
keep = tf.sparse_tensor_to_dense(keep)[0]
# Apply per-class NMS
# 1. Prepare variables
pre_nms_class_ids = tf.gather(class_ids, keep)
pre_nms_scores = tf.gather(class_scores, keep)
pre_nms_rois = tf.gather(refined_rois, keep)
unique_pre_nms_class_ids = tf.unique(pre_nms_class_ids)[0]
def nms_keep_map(class_id):
"""Apply Non-Maximum Suppression on ROIs of the given class."""
# Indices of ROIs of the given class
ixs = tf.where(tf.equal(pre_nms_class_ids, class_id))[:, 0]
# Apply NMS
class_keep = tf.image.non_max_suppression(
tf.gather(pre_nms_rois, ixs),
tf.gather(pre_nms_scores, ixs),
max_output_size=config.DETECTION_MAX_INSTANCES,
iou_threshold=config.DETECTION_NMS_THRESHOLD)
# Map indices
class_keep = tf.gather(keep, tf.gather(ixs, class_keep))
# Pad with -1 so returned tensors have the same shape
gap = config.DETECTION_MAX_INSTANCES - tf.shape(class_keep)[0]
class_keep = tf.pad(class_keep, [(0, gap)],
mode='CONSTANT', constant_values=-1)
# Set shape so map_fn() can infer result shape
class_keep.set_shape([config.DETECTION_MAX_INSTANCES])
return class_keep
# 2. Map over class IDs
nms_keep = tf.map_fn(nms_keep_map, unique_pre_nms_class_ids,
dtype=tf.int64)
# 3. Merge results into one list, and remove -1 padding
nms_keep = tf.reshape(nms_keep, [-1])
nms_keep = tf.gather(nms_keep, tf.where(nms_keep > -1)[:, 0])
# 4. Compute intersection between keep and nms_keep
keep = tf.sets.set_intersection(tf.expand_dims(keep, 0),
tf.expand_dims(nms_keep, 0))
keep = tf.sparse_tensor_to_dense(keep)[0]
# Keep top detections
roi_count = config.DETECTION_MAX_INSTANCES
class_scores_keep = tf.gather(class_scores, keep)
num_keep = tf.minimum(tf.shape(class_scores_keep)[0], roi_count)
top_ids = tf.nn.top_k(class_scores_keep, k=num_keep, sorted=True)[1]
keep = tf.gather(keep, top_ids)