"""
Mask R-CNN
The main Mask R-CNN model implemenetation.
Copyright (c) 2017 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by Waleed Abdulla
"""
import datetime
import math
import os
import random
import re
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
from torch.autograd import Variable
import utils
import visualize
from nms.nms_wrapper import nms
from roialign.roi_align.crop_and_resize import CropAndResizeFunction
############################################################
# Logging 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 ""))
print(text)
def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):
"""
Call in a loop to create terminal progress bar
@params:
iteration - Required : current iteration (Int)
total - Required : total iterations (Int)
prefix - Optional : prefix string (Str)
suffix - Optional : suffix string (Str)
decimals - Optional : positive number of decimals in percent complete (Int)
length - Optional : character length of bar (Int)
fill - Optional : bar fill character (Str)
"""
percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration / float(total)))
filledLength = int(length * iteration // total)
bar = fill * filledLength + '-' * (length - filledLength)
print('\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\n')
# Print New Line on Complete
if iteration == total:
print()
############################################################
# Pytorch Utility Functions
############################################################
def unique1d(tensor):
if tensor.size()[0] == 0 or tensor.size()[0] == 1:
return tensor
tensor = tensor.sort()[0]
unique_bool = tensor[1:] != tensor [:-1]
first_element = Variable(torch.ByteTensor([True]), requires_grad=False)
if tensor.is_cuda:
first_element = first_element.cuda()
unique_bool = torch.cat((first_element, unique_bool),dim=0)
return tensor[unique_bool.data]
def intersect1d(tensor1, tensor2):
aux = torch.cat((tensor1, tensor2),dim=0)
aux = aux.sort()[0]
return aux[:-1][(aux[1:] == aux[:-1]).data]
def log2(x):
"""Implementatin of Log2. Pytorch doesn't have a native implemenation."""
ln2 = Variable(torch.log(torch.FloatTensor([2.0])), requires_grad=False)
if x.is_cuda:
ln2 = ln2.cuda()
return torch.log(x) / ln2
class SamePad2d(nn.Module):
"""Mimics tensorflow's 'SAME' padding.
"""
def __init__(self, kernel_size, stride):
super(SamePad2d, self).__init__()
self.kernel_size = torch.nn.modules.utils._pair(kernel_size)
self.stride = torch.nn.modules.utils._pair(stride)
def forward(self, input):
in_width = input.size()[2]
in_height = input.size()[3]
out_width = math.ceil(float(in_width) / float(self.stride[0]))
out_height = math.ceil(float(in_height) / float(self.stride[1]))
pad_along_width = ((out_width - 1) * self.stride[0] +
self.kernel_size[0] - in_width)
pad_along_height = ((out_height - 1) * self.stride[1] +
self.kernel_size[1] - in_height)
pad_left = math.floor(pad_along_width / 2)
pad_top = math.floor(pad_along_height / 2)
pad_right = pad_along_width - pad_left
pad_bottom = pad_along_height - pad_top
return F.pad(input, (pad_left, pad_right, pad_top, pad_bottom), 'constant', 0)
def __repr__(self):
return self.__class__.__name__
############################################################
# FPN Graph
############################################################
class TopDownLayer(nn.Module):
def __init__(self, in_channels, out_channels):
super(TopDownLayer, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1)
self.padding2 = SamePad2d(kernel_size=3, stride=1)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1)
def forward(self, x, y):
y = F.upsample(y, scale_factor=2)
x = self.conv1(x)
return self.conv2(self.padding2(x+y))
class FPN(nn.Module):
def __init__(self, C1, C2, C3, C4, C5, out_channels):
super(FPN, self).__init__()
self.out_channels = out_channels
self.C1 = C1
self.C2 = C2
self.C3 = C3
self.C4 = C4
self.C5 = C5
self.P6 = nn.MaxPool2d(kernel_size=1, stride=2)
self.P5_conv1 = nn.Conv2d(2048, self.out_channels, kernel_size=1, stride=1)
self.P5_conv2 = nn.Sequential(
SamePad2d(kernel_size=3, stride=1),
nn.Conv2d(self.out_channels, self.out_channels, kernel_size=3, stride=1),
)
self.P4_conv1 = nn.Conv2d(1024, self.out_channels, kernel_size=1, stride=1)
self.P4_conv2 = nn.Sequential(
SamePad2d(kernel_size=3, stride=1),
nn.Conv2d(self.out_channels, self.out_channels, kernel_size=3, stride=1),
)
self.P3_conv1 = nn.Conv2d(512, self.out_channels, kernel_size=1, stride=1)
self.P3_conv2 = nn.Sequential(
SamePad2d(kernel_size=3, stride=1),
nn.Conv2d(self.out_channels, self.out_channels, kernel_size=3, stride=1),
)
self.P2_conv1 = nn.Conv2d(256, self.out_channels, kernel_size=1, stride=1)
self.P2_conv2 = nn.Sequential(
SamePad2d(kernel_size=3, stride=1),
nn.Conv2d(self.out_channels, self.out_channels, kernel_size=3, stride=1),
)
def forward(self, x):
x = self.C1(x)
x = self.C2(x)
c2_out = x
x = self.C3(x)
c3_out = x
x = self.C4(x)
c4_out = x
x = self.C5(x)
p5_out = self.P5_conv1(x)
p4_out = self.P4_conv1(c4_out) + F.upsample(p5_out, scale_factor=2)
p3_out = self.P3_conv1(c3_out) + F.upsample(p4_out, scale_factor=2)
p2_out = self.P2_conv1(c2_out) + F.upsample(p3_out, scale_factor=2)
p5_out = self.P5_conv2(p5_out)
p4_out = self.P4_conv2(p4_out)
p3_out = self.P3_conv2(p3_out)
p2_out = self.P2_conv2(p2_out)
# P6 is used for the 5th anchor scale in RPN. Generated by
# subsampling from P5 with stride of 2.
p6_out = self.P6(p5_out)
return [p2_out, p3_out, p4_out, p5_out, p6_out]
############################################################
# Resnet Graph
############################################################
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride)
self.bn1 = nn.BatchNorm2d(planes, eps=0.001, momentum=0.01)
self.padding2 = SamePad2d(kernel_size=3, stride=1)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3)
self.bn2 = nn.BatchNorm2d(planes, eps=0.001, momentum=0.01)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1)
self.bn3 = nn.BatchNorm2d(planes * 4, eps=0.001, momentum=0.01)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.padding2(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, architecture, stage5=False):
super(ResNet, self).__init__()
assert architecture in ["resnet50", "resnet101"]
self.inplanes = 64
self.layers = [3, 4, {"resnet50": 6, "resnet101": 23}[architecture], 3]
self.block = Bottleneck
self.stage5 = stage5
self.C1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
nn.BatchNorm2d(64, eps=0.001, momentum=0.01),
nn.ReLU(inplace=True),
SamePad2d(kernel_size=3, stride=2),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.C2 = self.make_layer(self.block, 64, self.layers[0])
self.C3 = self.make_layer(self.block, 128, self.layers[1], stride=2)
self.C4 = self.make_layer(self.block, 256, self.layers[2], stride=2)
if self.stage5:
self.C5 = self.make_layer(self.block, 512, self.layers[3], stride=2)
else:
self.C5 = None
def forward(self, x):
x = self.C1(x)
x = self.C2(x)
x = self.C3(x)
x = self.C4(x)
x = self.C5(x)
return x
def stages(self):
return [self.C1, self.C2, self.C3, self.C4, self.C5]
def make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride),
nn.BatchNorm2d(planes * block.expansion, eps=0.001, momentum=0.01),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
############################################################
# Proposal Layer
############################################################
def apply_box_deltas(boxes, deltas):
"""Applies the given deltas to the given boxes.
boxes: [N, 4] where each row is y1, x1, y2, x2
deltas: [N, 4] where each row is [dy, dx, log(dh), log(dw)]
"""
# 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 *= torch.exp(deltas[:, 2])
width *= torch.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 = torch.stack([y1, x1, y2, x2], dim=1)
return result
def clip_boxes(boxes, window):
"""
boxes: [N, 4] each col is y1, x1, y2, x2
window: [4] in the form y1, x1, y2, x2
"""
boxes = torch.stack( \
[boxes[:, 0].clamp(float(window[0]), float(window[2])),
boxes[:, 1].clamp(float(window[1]), float(window[3])),
boxes[:, 2].clamp(float(window[0]), float(window[2])),
boxes[:, 3].clamp(float(window[1]), float(window[3]))], 1)
return boxes
def proposal_layer(inputs, proposal_count, nms_threshold, anchors, config=None):
"""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 refinment detals to anchors.
Inputs:
rpn_probs: [batch, anchors, (bg prob, fg prob)]
rpn_bbox: [batch, anchors, (dy, dx, log(dh), log(dw))]
Returns:
Proposals in normalized coordinates [batch, rois, (y1, x1, y2, x2)]
"""
# Currently only supports batchsize 1
inputs[0] = inputs[0].squeeze(0)
inputs[1] = inputs[1].squeeze(0)
# Box Scores. Use the foreground class confidence. [Batch, num_rois, 1]
scores = inputs[0][:, 1]
# Box deltas [batch, num_rois, 4]
deltas = inputs[1]
std_dev = Variable(torch.from_numpy(np.reshape(config.RPN_BBOX_STD_DEV, [1, 4])).float(), requires_grad=False)
if config.GPU_COUNT:
std_dev = std_dev.cuda()
deltas = deltas * std_dev
# Improve performance by trimming to top anchors by score
# and doing the rest on the smaller subset.
pre_nms_limit = min(6000, anchors.size()[0])
scores, order = scores.sort(descending=True)
order = order[:pre_nms_limit]
scores = scores[:pre_nms_limit]
deltas = deltas[order.data, :] # TODO: Support batch size > 1 ff.
anchors = anchors[order.data, :]
# Apply deltas to anchors to get refined anchors.
# [batch, N, (y1, x1, y2, x2)]
boxes = apply_box_deltas(anchors, deltas)
# Clip to image boundaries. [batch, N, (y1, x1, y2, x2)]
height, width = config.IMAGE_SHAPE[:2]
window = np.array([0, 0, height, width]).astype(np.float32)
boxes = clip_boxes(boxes, window)
# 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
keep = nms(torch.cat((boxes, scores.unsqueeze(1)), 1).data, nms_threshold)
keep = keep[:proposal_count]
boxes = boxes[keep, :]
# Normalize dimensions to range of 0 to 1.
norm = Variable(torch.from_numpy(np.array([height, width, height, width])).float(), requires_grad=False)
if config.GPU_COUNT:
norm = norm.cuda()
normalized_boxes = boxes / norm
# Add back batch dimension
normalized_boxes = normalized_boxes.unsqueeze(0)
return normalized_boxes
############################################################
# ROIAlign Layer
############################################################
def pyramid_roi_align(inputs, pool_size, image_shape):
"""Implements ROI Pooling on multiple levels of the feature pyramid.
Params:
- pool_size: [height, width] of the output pooled regions. Usually [7, 7]
- image_shape: [height, width, channels]. Shape of input image in pixels
Inputs:
- boxes: [batch, num_boxes, (y1, x1, y2, x2)] in normalized
coordinates.
- Feature maps: List of feature maps from different levels of the pyramid.
Each is [batch, channels, height, width]
Output:
Pooled regions in the shape: [num_boxes, height, width, channels].
The width and height are those specific in the pool_shape in the layer
constructor.
"""
# Currently only supports batchsize 1
for i in range(len(inputs)):
inputs[i] = inputs[i].squeeze(0)
# Crop boxes [batch, num_boxes, (y1, x1, y2, x2)] in normalized coords
boxes = inputs[0]
# Feature Maps. List of feature maps from different level of the
# feature pyramid. Each is [batch, height, width, channels]
feature_maps = inputs[1:]
# Assign each ROI to a level in the pyramid based on the ROI area.
y1, x1, y2, x2 = boxes.chunk(4, dim=1)
h = y2 - y1
w = x2 - x1
# 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 = Variable(torch.FloatTensor([float(image_shape[0]*image_shape[1])]), requires_grad=False)
if boxes.is_cuda:
image_area = image_area.cuda()
roi_level = 4 + log2(torch.sqrt(h*w)/(224.0/torch.sqrt(image_area)))
roi_level = roi_level.round().int()
roi_level = roi_level.clamp(2,5)
# 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 = roi_level==level
if not ix.any():
continue
ix = torch.nonzero(ix)[:,0]
level_boxes = boxes[ix.data, :]
# Keep track of which box is mapped to which level
box_to_level.append(ix.data)
# Stop gradient propogation to ROI proposals
level_boxes = level_boxes.detach()
# 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]
ind = Variable(torch.zeros(level_boxes.size()[0]),requires_grad=False).int()
if level_boxes.is_cuda:
ind = ind.cuda()
feature_maps[i] = feature_maps[i].unsqueeze(0) #CropAndResizeFunction needs batch dimension
pooled_features = CropAndResizeFunction(pool_size, pool_size, 0)(feature_maps[i], level_boxes, ind)
pooled.append(pooled_features)
# Pack pooled features into one tensor
pooled = torch.cat(pooled, dim=0)
# Pack box_to_level mapping into one array and add another
# column representing the order of pooled boxes
box_to_level = torch.cat(box_to_level, dim=0)
# Rearrange pooled features to match the order of the original boxes
_, box_to_level = torch.sort(box_to_level)
pooled = pooled[box_to_level, :, :]
return pooled
############################################################
# Detection Target Layer
############################################################
def bbox_overlaps(boxes1, boxes2):
"""Computes IoU overlaps between two sets of boxes.
boxes1, boxes2: [N, (y1, x1, y2, x2)].
"""
# 1. Tile boxes2 and repeate boxes1. This allows us to compare
# every boxes1 against every boxes2 without loops.
# TF doesn't have an equivalent to np.repeate() so simulate it
# using tf.tile() and tf.reshape.
boxes1_repeat = boxes2.size()[0]
boxes2_repeat = boxes1.size()[0]
boxes1 = boxes1.repeat(1,boxes1_repeat).view(-1,4)
boxes2 = boxes2.repeat(boxes2_repeat,1)
# 2. Compute intersections
b1_y1, b1_x1, b1_y2, b1_x2 = boxes1.chunk(4, dim=1)
b2_y1, b2_x1, b2_y2, b2_x2 = boxes2.chunk(4, dim=1)
y1 = torch.max(b1_y1, b2_y1)[:, 0]
x1 = torch.max(b1_x1, b2_x1)[:, 0]
y2 = torch.min(b1_y2, b2_y2)[:, 0]
x2 = torch.min(b1_x2, b2_x2)[:, 0]
zeros = Variable(torch.zeros(y1.size()[0]), requires_grad=False)
if y1.is_cuda:
zeros = zeros.cuda()
intersection = torch.max(x2 - x1, zeros) * torch.max(y2 - y1, zeros)
# 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[:,0] + b2_area[:,0] - intersection
# 4. Compute IoU and reshape to [boxes1, boxes2]
iou = intersection / union
overlaps = iou.view(boxes2_repeat, boxes1_repeat)
return overlaps
def detection_target_layer(proposals, gt_class_ids, gt_boxes, gt_masks, config):
"""Subsamples proposals and generates target box refinment, 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 refinments.
target_mask: [batch, TRAIN_ROIS_PER_IMAGE, height, width)
Masks cropped to bbox boundaries and resized to neural
network output size.
"""
# Currently only supports batchsize 1
proposals = proposals.squeeze(0)
gt_class_ids = gt_class_ids.squeeze(0)
gt_boxes = gt_boxes.squeeze(0)
gt_masks = gt_masks.squeeze(0)
# 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.
if torch.nonzero(gt_class_ids < 0).size():
crowd_ix = torch.nonzero(gt_class_ids < 0)[:, 0]
non_crowd_ix = torch.nonzero(gt_class_ids > 0)[:, 0]
crowd_boxes = gt_boxes[crowd_ix.data, :]
crowd_masks = gt_masks[crowd_ix.data, :, :]
gt_class_ids = gt_class_ids[non_crowd_ix.data]
gt_boxes = gt_boxes[non_crowd_ix.data, :]
gt_masks = gt_masks[non_crowd_ix.data, :]
# Compute overlaps with crowd boxes [anchors, crowds]
crowd_overlaps = bbox_overlaps(proposals, crowd_boxes)
crowd_iou_max = torch.max(crowd_overlaps, dim=1)[0]
no_crowd_bool = crowd_iou_max < 0.001
else:
no_crowd_bool = Variable(torch.ByteTensor(proposals.size()[0]*[True]), requires_grad=False)
if config.GPU_COUNT:
no_crowd_bool = no_crowd_bool.cuda()
# Compute overlaps matrix [proposals, gt_boxes]
overlaps = bbox_overlaps(proposals, gt_boxes)
# Determine postive and negative ROIs
roi_iou_max = torch.max(overlaps, dim=1)[0]
# 1. Positive ROIs are those with >= 0.5 IoU with a GT box
positive_roi_bool = roi_iou_max >= 0.5
# Subsample ROIs. Aim for 33% positive
# Positive ROIs
if torch.nonzero(positive_roi_bool).size():
positive_indices = torch.nonzero(positive_roi_bool)[:, 0]
positive_count = int(config.TRAIN_ROIS_PER_IMAGE *
config.ROI_POSITIVE_RATIO)
rand_idx = torch.randperm(positive_indices.size()[0])
rand_idx = rand_idx[:positive_count]
if config.GPU_COUNT:
rand_idx = rand_idx.cuda()
positive_indices = positive_indices[rand_idx]
positive_count = positive_indices.size()[0]
positive_rois = proposals[positive_indices.data,:]
# Assign positive ROIs to GT boxes.
positive_overlaps = overlaps[positive_indices.data,:]
roi_gt_box_assignment = torch.max(positive_overlaps, dim=1)[1]
roi_gt_boxes = gt_boxes[roi_gt_box_assignment.data,:]
roi_gt_class_ids = gt_class_ids[roi_gt_box_assignment.data]
# Compute bbox refinement for positive ROIs
deltas = Variable(utils.box_refinement(positive_rois.data, roi_gt_boxes.data), requires_grad=False)
std_dev = Variable(torch.from_numpy(config.BBOX_STD_DEV).float(), requires_grad=False)
if config.GPU_COUNT:
std_dev = std_dev.cuda()
deltas /= std_dev
# Assign positive ROIs to GT masks
roi_masks = gt_masks[roi_gt_box_assignment.data,:,:]
# Compute mask targets
boxes = positive_rois
if config.USE_MINI_MASK:
# Transform ROI corrdinates from normalized image space
# to normalized mini-mask space.
y1, x1, y2, x2 = positive_rois.chunk(4, dim=1)
gt_y1, gt_x1, gt_y2, gt_x2 = roi_gt_boxes.chunk(4, dim=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 = torch.cat([y1, x1, y2, x2], dim=1)
box_ids = Variable(torch.arange(roi_masks.size()[0]), requires_grad=False).int()
if config.GPU_COUNT:
box_ids = box_ids.cuda()
masks = Variable(CropAndResizeFunction(config.MASK_SHAPE[0], config.MASK_SHAPE[1], 0)(roi_masks.unsqueeze(1), boxes, box_ids).data, requires_grad=False)
masks = masks.squeeze(1)
# Threshold mask pixels at 0.5 to have GT masks be 0 or 1 to use with
# binary cross entropy loss.
masks = torch.round(masks)
else:
positive_count = 0
# 2. Negative ROIs are those with < 0.5 with every GT box. Skip crowds.
negative_roi_bool = roi_iou_max < 0.5
negative_roi_bool = negative_roi_bool & no_crowd_bool
# Negative ROIs. Add enough to maintain positive:negative ratio.
if torch.nonzero(negative_roi_bool).size() and positive_count>0:
negative_indices = torch.nonzero(negative_roi_bool)[:, 0]
r = 1.0 / config.ROI_POSITIVE_RATIO
negative_count = int(r * positive_count - positive_count)
rand_idx = torch.randperm(negative_indices.size()[0])
rand_idx = rand_idx[:negative_count]
if config.GPU_COUNT:
rand_idx = rand_idx.cuda()
negative_indices = negative_indices[rand_idx]
negative_count = negative_indices.size()[0]
negative_rois = proposals[negative_indices.data, :]
else:
negative_count = 0
# Append negative ROIs and pad bbox deltas and masks that
# are not used for negative ROIs with zeros.
if positive_count > 0 and negative_count > 0:
rois = torch.cat((positive_rois, negative_rois), dim=0)
zeros = Variable(torch.zeros(negative_count), requires_grad=False).int()
if config.GPU_COUNT:
zeros = zeros.cuda()
roi_gt_class_ids = torch.cat([roi_gt_class_ids, zeros], dim=0)
zeros = Variable(torch.zeros(negative_count,4), requires_grad=False)
if config.GPU_COUNT:
zeros = zeros.cuda()
deltas = torch.cat([deltas, zeros], dim=0)
zeros = Variable(torch.zeros(negative_count,config.MASK_SHAPE[0],config.MASK_SHAPE[1]), requires_grad=False)
if config.GPU_COUNT:
zeros = zeros.cuda()
masks = torch.cat([masks, zeros], dim=0)
elif positive_count > 0:
rois = positive_rois
elif negative_count > 0:
rois = negative_rois
zeros = Variable(torch.zeros(negative_count), requires_grad=False)
if config.GPU_COUNT:
zeros = zeros.cuda()
roi_gt_class_ids = zeros
zeros = Variable(torch.zeros(negative_count,4), requires_grad=False).int()
if config.GPU_COUNT:
zeros = zeros.cuda()
deltas = zeros
zeros = Variable(torch.zeros(negative_count,config.MASK_SHAPE[0],config.MASK_SHAPE[1]), requires_grad=False)
if config.GPU_COUNT:
zeros = zeros.cuda()
masks = zeros
else:
rois = Variable(torch.FloatTensor(), requires_grad=False)
roi_gt_class_ids = Variable(torch.IntTensor(), requires_grad=False)
deltas = Variable(torch.FloatTensor(), requires_grad=False)
masks = Variable(torch.FloatTensor(), requires_grad=False)
if config.GPU_COUNT:
rois = rois.cuda()
roi_gt_class_ids = roi_gt_class_ids.cuda()
deltas = deltas.cuda()
masks = masks.cuda()
return rois, roi_gt_class_ids, deltas, masks
############################################################
# Detection Layer
############################################################
def clip_to_window(window, boxes):
"""
window: (y1, x1, y2, x2). The window in the image we want to clip to.
boxes: [N, (y1, x1, y2, x2)]
"""
boxes[:, 0] = boxes[:, 0].clamp(float(window[0]), float(window[2]))
boxes[:, 1] = boxes[:, 1].clamp(float(window[1]), float(window[3]))
boxes[:, 2] = boxes[:, 2].clamp(float(window[0]), float(window[2]))
boxes[:, 3] = boxes[:, 3].clamp(float(window[1]), float(window[3]))
return boxes
def refine_detections(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)]
"""
# Class IDs per ROI
_, class_ids = torch.max(probs, dim=1)
# Class probability of the top class of each ROI
# Class-specific bounding box deltas
idx = torch.arange(class_ids.size()[0]).long()
if config.GPU_COUNT:
idx = idx.cuda()
class_scores = probs[idx, class_ids.data]
deltas_specific = deltas[idx, class_ids.data]
# Apply bounding box deltas
# Shape: [boxes, (y1, x1, y2, x2)] in normalized coordinates
std_dev = Variable(torch.from_numpy(np.reshape(config.RPN_BBOX_STD_DEV, [1, 4])).float(), requires_grad=False)
if config.GPU_COUNT:
std_dev = std_dev.cuda()
refined_rois = apply_box_deltas(rois, deltas_specific * std_dev)
# Convert coordiates to image domain
height, width = config.IMAGE_SHAPE[:2]
scale = Variable(torch.from_numpy(np.array([height, width, height, width])).float(), requires_grad=False)
if config.GPU_COUNT:
scale = scale.cuda()
refined_rois *= scale
# Clip boxes to image window
refined_rois = clip_to_window(window, refined_rois)
# Round and cast to int since we're deadling with pixels now
refined_rois = torch.round(refined_rois)
# TODO: Filter out boxes with zero area
# Filter out background boxes
keep_bool = class_ids>0
# Filter out low confidence boxes
if config.DETECTION_MIN_CONFIDENCE:
keep_bool = keep_bool & (class_scores >= config.DETECTION_MIN_CONFIDENCE)
keep = torch.nonzero(keep_bool)[:,0]
# Apply per-class NMS
pre_nms_class_ids = class_ids[keep.data]
pre_nms_scores = class_scores[keep.data]
pre_nms_rois = refined_rois[keep.data]
for i, class_id in enumerate(unique1d(pre_nms_class_ids)):
# Pick detections of this class
ixs = torch.nonzero(pre_nms_class_ids == class_id)[:,0]
# Sort
ix_rois = pre_nms_rois[ixs.data]
ix_scores = pre_nms_scores[ixs]
ix_scores, order = ix_scores.sort(descending=True)
ix_rois = ix_rois[order.data,:]
class_keep = nms(torch.cat((ix_rois, ix_scores.unsqueeze(1)), dim=1).data, config.DETECTION_NMS_THRESHOLD)
# Map indicies
class_keep = keep[ixs[order[class_keep].data].data]
if i==0:
nms_keep = class_keep
else:
nms_keep = unique1d(torch.cat((nms_keep, class_keep)))
keep = intersect1d(keep, nms_keep)
# Keep top detections
roi_count = config.DETECTION_MAX_INSTANCES
top_ids = class_scores[keep.data].sort(descending=True)[1][:roi_count]
keep = keep[top_ids.data]
# Arrange output as [N, (y1, x1, y2, x2, class_id, score)]
# Coordinates are in image domain.
result = torch.cat((refined_rois[keep.data],
class_ids[keep.data].unsqueeze(1).float(),
class_scores[keep.data].unsqueeze(1)), dim=1)
return result
def detection_layer(config, rois, mrcnn_class, mrcnn_bbox, image_meta):
"""Takes classified proposal boxes and their bounding box deltas and
returns the final detection boxes.
Returns:
[batch, num_detections, (y1, x1, y2, x2, class_score)] in pixels
"""
# Currently only supports batchsize 1
rois = rois.squeeze(0)
_, _, window, _ = parse_image_meta(image_meta)
window = window[0]
detections = refine_detections(rois, mrcnn_class, mrcnn_bbox, window, config)
return detections
############################################################
# Region Proposal Network
############################################################
class RPN(nn.Module):
"""Builds the model of Region Proposal Network.
anchors_per_location: number of anchors per pixel in the feature map
anchor_stride: Controls the density of anchors. Typically 1 (anchors for
every pixel in the feature map), or 2 (every other pixel).
Returns:
rpn_logits: [batch, H, W, 2] Anchor classifier logits (before softmax)
rpn_probs: [batch, W, W, 2] Anchor classifier probabilities.
rpn_bbox: [batch, H, W, (dy, dx, log(dh), log(dw))] Deltas to be
applied to anchors.
"""
def __init__(self, anchors_per_location, anchor_stride, depth):
super(RPN, self).__init__()
self.anchors_per_location = anchors_per_location
self.anchor_stride = anchor_stride
self.depth = depth
self.padding = SamePad2d(kernel_size=3, stride=self.anchor_stride)
self.conv_shared = nn.Conv2d(self.depth, 512, kernel_size=3, stride=self.anchor_stride)
self.relu = nn.ReLU(inplace=True)
self.conv_class = nn.Conv2d(512, 2 * anchors_per_location, kernel_size=1, stride=1)
self.softmax = nn.Softmax(dim=2)
self.conv_bbox = nn.Conv2d(512, 4 * anchors_per_location, kernel_size=1, stride=1)
def forward(self, x):
# Shared convolutional base of the RPN
x = self.relu(self.conv_shared(self.padding(x)))
# Anchor Score. [batch, anchors per location * 2, height, width].
rpn_class_logits = self.conv_class(x)
# Reshape to [batch, 2, anchors]
rpn_class_logits = rpn_class_logits.permute(0,2,3,1)
rpn_class_logits = rpn_class_logits.contiguous()
rpn_class_logits = rpn_class_logits.view(x.size()[0], -1, 2)
# Softmax on last dimension of BG/FG.
rpn_probs = self.softmax(rpn_class_logits)
# Bounding box refinement. [batch, H, W, anchors per location, depth]
# where depth is [x, y, log(w), log(h)]
rpn_bbox = self.conv_bbox(x)
# Reshape to [batch, 4, anchors]
rpn_bbox = rpn_bbox.permute(0,2,3,1)
rpn_bbox = rpn_bbox.contiguous()
rpn_bbox = rpn_bbox.view(x.size()[0], -1, 4)
return [rpn_class_logits, rpn_probs, rpn_bbox]
############################################################
# Feature Pyramid Network Heads
############################################################
class Classifier(nn.Module):
def __init__(self, depth, pool_size, image_shape, num_classes):
super(Classifier, self).__init__()
self.depth = depth
self.pool_size = pool_size
self.image_shape = image_shape
self.num_classes = num_classes
self.conv1 = nn.Conv2d(self.depth, 1024, kernel_size=self.pool_size, stride=1)
self.bn1 = nn.BatchNorm2d(1024, eps=0.001, momentum=0.01)
self.conv2 = nn.Conv2d(1024, 1024, kernel_size=1, stride=1)
self.bn2 = nn.BatchNorm2d(1024, eps=0.001, momentum=0.01)
self.relu = nn.ReLU(inplace=True)
self.linear_class = nn.Linear(1024, num_classes)
self.softmax = nn.Softmax(dim=1)
self.linear_bbox = nn.Linear(1024, num_classes * 4)
def forward(self, x, rois):
x = pyramid_roi_align([rois]+x, self.pool_size, self.image_shape)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = x.view(-1,1024)
mrcnn_class_logits = self.linear_class(x)
mrcnn_probs = self.softmax(mrcnn_class_logits)
mrcnn_bbox = self.linear_bbox(x)
mrcnn_bbox = mrcnn_bbox.view(mrcnn_bbox.size()[0], -1, 4)
return [mrcnn_class_logits, mrcnn_probs, mrcnn_bbox]
class Mask(nn.Module):
def __init__(self, depth, pool_size, image_shape, num_classes):
super(Mask, self).__init__()
self.depth = depth
self.pool_size = pool_size
self.image_shape = image_shape
self.num_classes = num_classes
self.padding = SamePad2d(kernel_size=3, stride=1)
self.conv1 = nn.Conv2d(self.depth, 256, kernel_size=3, stride=1)
self.bn1 = nn.BatchNorm2d(256, eps=0.001)
self.conv2 = nn.Conv2d(256, 256, kernel_size=3, stride=1)
self.bn2 = nn.BatchNorm2d(256, eps=0.001)
self.conv3 = nn.Conv2d(256, 256, kernel_size=3, stride=1)
self.bn3 = nn.BatchNorm2d(256, eps=0.001)
self.conv4 = nn.Conv2d(256, 256, kernel_size=3, stride=1)
self.bn4 = nn.BatchNorm2d(256, eps=0.001)
self.deconv = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2)
self.conv5 = nn.Conv2d(256, num_classes, kernel_size=1, stride=1)
self.sigmoid = nn.Sigmoid()
self.relu = nn.ReLU(inplace=True)
def forward(self, x, rois):
x = pyramid_roi_align([rois] + x, self.pool_size, self.image_shape)
x = self.conv1(self.padding(x))
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(self.padding(x))
x = self.bn2(x)
x = self.relu(x)
x = self.conv3(self.padding(x))
x = self.bn3(x)
x = self.relu(x)
x = self.conv4(self.padding(x))
x = self.bn4(x)
x = self.relu(x)
x = self.deconv(x)
x = self.relu(x)
x = self.conv5(x)
x = self.sigmoid(x)
return x
############################################################
# Loss Functions
############################################################
def compute_rpn_class_loss(rpn_match, rpn_class_logits):
"""RPN anchor classifier loss.
rpn_match: [batch, anchors, 1]. Anchor match type. 1=positive,
-1=negative, 0=neutral anchor.
rpn_class_logits: [batch, anchors, 2]. RPN classifier logits for FG/BG.
"""
# Squeeze last dim to simplify
rpn_match = rpn_match.squeeze(2)
# Get anchor classes. Convert the -1/+1 match to 0/1 values.
anchor_class = (rpn_match == 1).long()
# Positive and Negative anchors contribute to the loss,
# but neutral anchors (match value = 0) don't.
indices = torch.nonzero(rpn_match != 0)
# Pick rows that contribute to the loss and filter out the rest.
rpn_class_logits = rpn_class_logits[indices.data[:,0],indices.data[:,1],:]
anchor_class = anchor_class[indices.data[:,0],indices.data[:,1]]
# Crossentropy loss
loss = F.cross_entropy(rpn_class_logits, anchor_class)
return loss
def compute_rpn_bbox_loss(target_bbox, rpn_match, rpn_bbox):
"""Return the RPN bounding box loss graph.
target_bbox: [batch, max positive anchors, (dy, dx, log(dh), log(dw))].
Uses 0 padding to fill in unsed bbox deltas.
rpn_match: [batch, anchors, 1]. Anchor match type. 1=positive,
-1=negative, 0=neutral anchor.
rpn_bbox: [batch, anchors, (dy, dx, log(dh), log(dw))]
"""
# Squeeze last dim to simplify
rpn_match = rpn_match.squeeze(2)
# Positive anchors contribute to the loss, but negative and
# neutral anchors (match value of 0 or -1) don't.
indices = torch.nonzero(rpn_match==1)
# Pick bbox deltas that contribute to the loss
rpn_bbox = rpn_bbox[indices.data[:,0],indices.data[:,1]]
# Trim target bounding box deltas to the same length as rpn_bbox.
target_bbox = target_bbox[0,:rpn_bbox.size()[0],:]
# Smooth L1 loss
loss = F.smooth_l1_loss(rpn_bbox, target_bbox)
return loss
def compute_mrcnn_class_loss(target_class_ids, pred_class_logits):
"""Loss for the classifier head of Mask RCNN.
target_class_ids: [batch, num_rois]. Integer class IDs. Uses zero
padding to fill in the array.
pred_class_logits: [batch, num_rois, num_classes]
"""
# Loss
if target_class_ids.size():
loss = F.cross_entropy(pred_class_logits,target_class_ids.long())
else:
loss = Variable(torch.FloatTensor([0]), requires_grad=False)
if target_class_ids.is_cuda:
loss = loss.cuda()
return loss
def compute_mrcnn_bbox_loss(target_bbox, target_class_ids, pred_bbox):
"""Loss for Mask R-CNN bounding box refinement.
target_bbox: [batch, num_rois, (dy, dx, log(dh), log(dw))]
target_class_ids: [batch, num_rois]. Integer class IDs.
pred_bbox: [batch, num_rois, num_classes, (dy, dx, log(dh), log(dw))]
"""
if target_class_ids.size():
# Only positive ROIs contribute to the loss. And only
# the right class_id of each ROI. Get their indicies.
positive_roi_ix = torch.nonzero(target_class_ids > 0)[:, 0]
positive_roi_class_ids = target_class_ids[positive_roi_ix.data].long()
indices = torch.stack((positive_roi_ix,positive_roi_class_ids), dim=1)
# Gather the deltas (predicted and true) that contribute to loss
target_bbox = target_bbox[indices[:,0].data,:]
pred_bbox = pred_bbox[indices[:,0].data,indices[:,1].data,:]
# Smooth L1 loss
loss = F.smooth_l1_loss(pred_bbox, target_bbox)
else:
loss = Variable(torch.FloatTensor([0]), requires_grad=False)
if target_class_ids.is_cuda:
loss = loss.cuda()
return loss
def compute_mrcnn_mask_loss(target_masks, target_class_ids, pred_masks):
"""Mask binary cross-entropy loss for the masks head.
target_masks: [batch, num_rois, height, width].
A float32 tensor of values 0 or 1. Uses zero padding to fill array.
target_class_ids: [batch, num_rois]. Integer class IDs. Zero padded.
pred_masks: [batch, proposals, height, width, num_classes] float32 tensor
with values from 0 to 1.
"""
if target_class_ids.size():
# Only positive ROIs contribute to the loss. And only
# the class specific mask of each ROI.
positive_ix = torch.nonzero(target_class_ids > 0)[:, 0]
positive_class_ids = target_class_ids[positive_ix.data].long()
indices = torch.stack((positive_ix, positive_class_ids), dim=1)
# Gather the masks (predicted and true) that contribute to loss
y_true = target_masks[indices[:,0].data,:,:]
y_pred = pred_masks[indices[:,0].data,indices[:,1].data,:,:]
# Binary cross entropy
loss = F.binary_cross_entropy(y_pred, y_true)
else:
loss = Variable(torch.FloatTensor([0]), requires_grad=False)
if target_class_ids.is_cuda:
loss = loss.cuda()
return loss
def compute_losses(rpn_match, rpn_bbox, rpn_class_logits, rpn_pred_bbox, target_class_ids, mrcnn_class_logits, target_deltas, mrcnn_bbox, target_mask, mrcnn_mask):
rpn_class_loss = compute_rpn_class_loss(rpn_match, rpn_class_logits)
rpn_bbox_loss = compute_rpn_bbox_loss(rpn_bbox, rpn_match, rpn_pred_bbox)
mrcnn_class_loss = compute_mrcnn_class_loss(target_class_ids, mrcnn_class_logits)
mrcnn_bbox_loss = compute_mrcnn_bbox_loss(target_deltas, target_class_ids, mrcnn_bbox)
mrcnn_mask_loss = compute_mrcnn_mask_loss(target_mask, target_class_ids, mrcnn_mask)
return [rpn_class_loss, rpn_bbox_loss, mrcnn_class_loss, mrcnn_bbox_loss, mrcnn_mask_loss]
############################################################
# Data Generator
############################################################
def load_image_gt(dataset, config, image_id, augment=False,
use_mini_mask=False):
"""Load and return ground truth data for an image (image, mask, bounding boxes).
augment: If true, apply random image augmentation. Currently, only
horizontal flipping is offered.
use_mini_mask: If False, returns full-size masks that are the same height
and width as the original image. These can be big, for example
1024x1024x100 (for 100 instances). Mini masks are smaller, typically,
224x224 and are generated by extracting the bounding box of the
object and resizing it to MINI_MASK_SHAPE.
Returns:
image: [height, width, 3]
shape: the original shape of the image before resizing and cropping.
class_ids: [instance_count] Integer class IDs
bbox: [instance_count, (y1, x1, y2, x2)]
mask: [height, width, instance_count]. The height and width are those
of the image unless use_mini_mask is True, in which case they are
defined in MINI_MASK_SHAPE.
"""
# Load image and mask
image = dataset.load_image(image_id)
mask, class_ids = dataset.load_mask(image_id)
shape = image.shape
image, window, scale, padding = utils.resize_image(
image,
min_dim=config.IMAGE_MIN_DIM,
max_dim=config.IMAGE_MAX_DIM,
padding=config.IMAGE_PADDING)
mask = utils.resize_mask(mask, scale, padding)
# Random horizontal flips.
if augment:
if random.randint(0, 1):
image = np.fliplr(image)
mask = np.fliplr(mask)
# Bounding boxes. Note that some boxes might be all zeros
# if the corresponding mask got cropped out.
# bbox: [num_instances, (y1, x1, y2, x2)]
bbox = utils.extract_bboxes(mask)
# Active classes
# Different datasets have different classes, so track the
# classes supported in the dataset of this image.
active_class_ids = np.zeros([dataset.num_classes], dtype=np.int32)
source_class_ids = dataset.source_class_ids[dataset.image_info[image_id]["source"]]
active_class_ids[source_class_ids] = 1
# Resize masks to smaller size to reduce memory usage
if use_mini_mask:
mask = utils.minimize_mask(bbox, mask, config.MINI_MASK_SHAPE)
# Image meta data
image_meta = compose_image_meta(image_id, shape, window, active_class_ids)
return image, image_meta, class_ids, bbox, mask
def build_rpn_targets(image_shape, anchors, gt_class_ids, gt_boxes, config):
"""Given the anchors and GT boxes, compute overlaps and identify positive
anchors and deltas to refine them to match their corresponding GT boxes.
anchors: [num_anchors, (y1, x1, y2, x2)]
gt_class_ids: [num_gt_boxes] Integer class IDs.
gt_boxes: [num_gt_boxes, (y1, x1, y2, x2)]
Returns:
rpn_match: [N] (int32) matches between anchors and GT boxes.
1 = positive anchor, -1 = negative anchor, 0 = neutral
rpn_bbox: [N, (dy, dx, log(dh), log(dw))] Anchor bbox deltas.
"""
# RPN Match: 1 = positive anchor, -1 = negative anchor, 0 = neutral
rpn_match = np.zeros([anchors.shape[0]], dtype=np.int32)
# RPN bounding boxes: [max anchors per image, (dy, dx, log(dh), log(dw))]
rpn_bbox = np.zeros((config.RPN_TRAIN_ANCHORS_PER_IMAGE, 4))
# 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 = np.where(gt_class_ids < 0)[0]
if crowd_ix.shape[0] > 0:
# Filter out crowds from ground truth class IDs and boxes
non_crowd_ix = np.where(gt_class_ids > 0)[0]
crowd_boxes = gt_boxes[crowd_ix]
gt_class_ids = gt_class_ids[non_crowd_ix]
gt_boxes = gt_boxes[non_crowd_ix]
# Compute overlaps with crowd boxes [anchors, crowds]
crowd_overlaps = utils.compute_overlaps(anchors, crowd_boxes)
crowd_iou_max = np.amax(crowd_overlaps, axis=1)
no_crowd_bool = (crowd_iou_max < 0.001)
else:
# All anchors don't intersect a crowd
no_crowd_bool = np.ones([anchors.shape[0]], dtype=bool)
# Compute overlaps [num_anchors, num_gt_boxes]
overlaps = utils.compute_overlaps(anchors, gt_boxes)
# Match anchors to GT Boxes
# If an anchor overlaps a GT box with IoU >= 0.7 then it's positive.
# If an anchor overlaps a GT box with IoU < 0.3 then it's negative.
# Neutral anchors are those that don't match the conditions above,
# and they don't influence the loss function.
# However, don't keep any GT box unmatched (rare, but happens). Instead,
# match it to the closest anchor (even if its max IoU is < 0.3).
#
# 1. Set negative anchors first. They get overwritten below if a GT box is
# matched to them. Skip boxes in crowd areas.
anchor_iou_argmax = np.argmax(overlaps, axis=1)
anchor_iou_max = overlaps[np.arange(overlaps.shape[0]), anchor_iou_argmax]
rpn_match[(anchor_iou_max < 0.3) & (no_crowd_bool)] = -1
# 2. Set an anchor for each GT box (regardless of IoU value).
# TODO: If multiple anchors have the same IoU match all of them
gt_iou_argmax = np.argmax(overlaps, axis=0)
rpn_match[gt_iou_argmax] = 1
# 3. Set anchors with high overlap as positive.
rpn_match[anchor_iou_max >= 0.7] = 1
# Subsample to balance positive and negative anchors
# Don't let positives be more than half the anchors
ids = np.where(rpn_match == 1)[0]
extra = len(ids) - (config.RPN_TRAIN_ANCHORS_PER_IMAGE // 2)
if extra > 0:
# Reset the extra ones to neutral
ids = np.random.choice(ids, extra, replace=False)
rpn_match[ids] = 0
# Same for negative proposals
ids = np.where(rpn_match == -1)[0]
extra = len(ids) - (config.RPN_TRAIN_ANCHORS_PER_IMAGE -
np.sum(rpn_match == 1))
if extra > 0:
# Rest the extra ones to neutral
ids = np.random.choice(ids, extra, replace=False)
rpn_match[ids] = 0
# For positive anchors, compute shift and scale needed to transform them
# to match the corresponding GT boxes.
ids = np.where(rpn_match == 1)[0]
ix = 0 # index into rpn_bbox
# TODO: use box_refinment() rather than duplicating the code here
for i, a in zip(ids, anchors[ids]):
# Closest gt box (it might have IoU < 0.7)
gt = gt_boxes[anchor_iou_argmax[i]]
# Convert coordinates to center plus width/height.
# GT Box
gt_h = gt[2] - gt[0]
gt_w = gt[3] - gt[1]
gt_center_y = gt[0] + 0.5 * gt_h
gt_center_x = gt[1] + 0.5 * gt_w
# Anchor
a_h = a[2] - a[0]
a_w = a[3] - a[1]
a_center_y = a[0] + 0.5 * a_h
a_center_x = a[1] + 0.5 * a_w
# Compute the bbox refinement that the RPN should predict.
rpn_bbox[ix] = [
(gt_center_y - a_center_y) / a_h,
(gt_center_x - a_center_x) / a_w,
np.log(gt_h / a_h),
np.log(gt_w / a_w),
]
# Normalize
rpn_bbox[ix] /= config.RPN_BBOX_STD_DEV
ix += 1
return rpn_match, rpn_bbox
class Dataset(torch.utils.data.Dataset):
def __init__(self, dataset, config, augment=True):
"""A generator that returns images and corresponding target class ids,
bounding box deltas, and masks.
dataset: The Dataset object to pick data from
config: The model config object
shuffle: If True, shuffles the samples before every epoch
augment: If True, applies image augmentation to images (currently only
horizontal flips are supported)
Returns a Python generator. Upon calling next() on it, the
generator returns two lists, inputs and outputs. The containtes
of the lists differs depending on the received arguments:
inputs list:
- images: [batch, H, W, C]
- image_metas: [batch, size of image meta]
- rpn_match: [batch, N] Integer (1=positive anchor, -1=negative, 0=neutral)
- rpn_bbox: [batch, N, (dy, dx, log(dh), log(dw))] Anchor bbox deltas.
- gt_class_ids: [batch, MAX_GT_INSTANCES] Integer class IDs
- gt_boxes: [batch, MAX_GT_INSTANCES, (y1, x1, y2, x2)]
- gt_masks: [batch, height, width, MAX_GT_INSTANCES]. The height and width
are those of the image unless use_mini_mask is True, in which
case they are defined in MINI_MASK_SHAPE.
outputs list: Usually empty in regular training. But if detection_targets
is True then the outputs list contains target class_ids, bbox deltas,
and masks.
"""
self.b = 0 # batch item index
self.image_index = -1
self.image_ids = np.copy(dataset.image_ids)
self.error_count = 0
self.dataset = dataset
self.config = config
self.augment = augment
# Anchors
# [anchor_count, (y1, x1, y2, x2)]
self.anchors = utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES,
config.RPN_ANCHOR_RATIOS,
config.BACKBONE_SHAPES,
config.BACKBONE_STRIDES,
config.RPN_ANCHOR_STRIDE)
def __getitem__(self, image_index):
# Get GT bounding boxes and masks for image.
image_id = self.image_ids[image_index]
image, image_metas, gt_class_ids, gt_boxes, gt_masks = \
load_image_gt(self.dataset, self.config, image_id, augment=self.augment,
use_mini_mask=self.config.USE_MINI_MASK)
# Skip images that have no instances. This can happen in cases
# where we train on a subset of classes and the image doesn't
# have any of the classes we care about.
if not np.any(gt_class_ids > 0):
return None
# RPN Targets
rpn_match, rpn_bbox = build_rpn_targets(image.shape, self.anchors,
gt_class_ids, gt_boxes, self.config)
# If more instances than fits in the array, sub-sample from them.
if gt_boxes.shape[0] > self.config.MAX_GT_INSTANCES:
ids = np.random.choice(
np.arange(gt_boxes.shape[0]), self.config.MAX_GT_INSTANCES, replace=False)
gt_class_ids = gt_class_ids[ids]
gt_boxes = gt_boxes[ids]
gt_masks = gt_masks[:, :, ids]
# Add to batch
rpn_match = rpn_match[:, np.newaxis]
images = mold_image(image.astype(np.float32), self.config)
# Convert
images = torch.from_numpy(images.transpose(2, 0, 1)).float()
image_metas = torch.from_numpy(image_metas)
rpn_match = torch.from_numpy(rpn_match)
rpn_bbox = torch.from_numpy(rpn_bbox).float()
gt_class_ids = torch.from_numpy(gt_class_ids)
gt_boxes = torch.from_numpy(gt_boxes).float()
gt_masks = torch.from_numpy(gt_masks.astype(int).transpose(2, 0, 1)).float()
return images, image_metas, rpn_match, rpn_bbox, gt_class_ids, gt_boxes, gt_masks
def __len__(self):
return self.image_ids.shape[0]
############################################################
# MaskRCNN Class
############################################################
class MaskRCNN(nn.Module):
"""Encapsulates the Mask RCNN model functionality.
"""
def __init__(self, config, model_dir):
"""
config: A Sub-class of the Config class
model_dir: Directory to save training logs and trained weights
"""
super(MaskRCNN, self).__init__()
self.config = config
self.model_dir = model_dir
self.set_log_dir()
self.build(config=config)
self.initialize_weights()
self.loss_history = []
self.val_loss_history = []
def build(self, config):
"""Build Mask R-CNN architecture.
"""
# Image size must be dividable by 2 multiple times
h, w = config.IMAGE_SHAPE[:2]
if h / 2**6 != int(h / 2**6) or w / 2**6 != int(w / 2**6):
raise Exception("Image size must be dividable by 2 at least 6 times "
"to avoid fractions when downscaling and upscaling."
"For example, use 256, 320, 384, 448, 512, ... etc. ")
# Build the shared convolutional layers.
# Bottom-up Layers
# Returns a list of the last layers of each stage, 5 in total.
# Don't create the thead (stage 5), so we pick the 4th item in the list.
resnet = ResNet("resnet101", stage5=True)
C1, C2, C3, C4, C5 = resnet.stages()
# Top-down Layers
# TODO: add assert to varify feature map sizes match what's in config
self.fpn = FPN(C1, C2, C3, C4, C5, out_channels=256)
# Generate Anchors
self.anchors = Variable(torch.from_numpy(utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES,
config.RPN_ANCHOR_RATIOS,
config.BACKBONE_SHAPES,
config.BACKBONE_STRIDES,
config.RPN_ANCHOR_STRIDE)).float(), requires_grad=False)
if self.config.GPU_COUNT:
self.anchors = self.anchors.cuda()
# RPN
self.rpn = RPN(len(config.RPN_ANCHOR_RATIOS), config.RPN_ANCHOR_STRIDE, 256)
# FPN Classifier
self.classifier = Classifier(256, config.POOL_SIZE, config.IMAGE_SHAPE, config.NUM_CLASSES)
# FPN Mask
self.mask = Mask(256, config.MASK_POOL_SIZE, config.IMAGE_SHAPE, config.NUM_CLASSES)
# Fix batch norm layers
def set_bn_fix(m):
classname = m.__class__.__name__
if classname.find('BatchNorm') != -1:
for p in m.parameters(): p.requires_grad = False
self.apply(set_bn_fix)
def initialize_weights(self):
"""Initialize model weights.
"""
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform(m.weight)
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.01)
m.bias.data.zero_()
def set_trainable(self, layer_regex, model=None, indent=0, verbose=1):
"""Sets model layers as trainable if their names match
the given regular expression.
"""
for param in self.named_parameters():
layer_name = param[0]
trainable = bool(re.fullmatch(layer_regex, layer_name))
if not trainable:
param[1].requires_grad = False
def set_log_dir(self, model_path=None):
"""Sets the model log directory and epoch counter.
model_path: If None, or a format different from what this code uses
then set a new log directory and start epochs from 0. Otherwise,
extract the log directory and the epoch counter from the file
name.
"""
# Set date and epoch counter as if starting a new model
self.epoch = 0
now = datetime.datetime.now()
# If we have a model path with date and epochs use them
if model_path:
# Continue from we left of. Get epoch and date from the file name
# A sample model path might look like:
# /path/to/logs/coco20171029T2315/mask_rcnn_coco_0001.h5
regex = r".*/\w+(\d{4})(\d{2})(\d{2})T(\d{2})(\d{2})/mask\_rcnn\_\w+(\d{4})\.pth"
m = re.match(regex, model_path)
if m:
now = datetime.datetime(int(m.group(1)), int(m.group(2)), int(m.group(3)),
int(m.group(4)), int(m.group(5)))
self.epoch = int(m.group(6))
# Directory for training logs
self.log_dir = os.path.join(self.model_dir, "{}{:%Y%m%dT%H%M}".format(
self.config.NAME.lower(), now))
# Path to save after each epoch. Include placeholders that get filled by Keras.
self.checkpoint_path = os.path.join(self.log_dir, "mask_rcnn_{}_*epoch*.pth".format(
self.config.NAME.lower()))
self.checkpoint_path = self.checkpoint_path.replace(
"*epoch*", "{:04d}")
def find_last(self):
"""Finds the last checkpoint file of the last trained model in the
model directory.
Returns:
log_dir: The directory where events and weights are saved
checkpoint_path: the path to the last checkpoint file
"""
# Get directory names. Each directory corresponds to a model
dir_names = next(os.walk(self.model_dir))[1]
key = self.config.NAME.lower()
dir_names = filter(lambda f: f.startswith(key), dir_names)
dir_names = sorted(dir_names)
if not dir_names:
return None, None
# Pick last directory
dir_name = os.path.join(self.model_dir, dir_names[-1])
# Find the last checkpoint
checkpoints = next(os.walk(dir_name))[2]
checkpoints = filter(lambda f: f.startswith("mask_rcnn"), checkpoints)
checkpoints = sorted(checkpoints)
if not checkpoints:
return dir_name, None
checkpoint = os.path.join(dir_name, checkpoints[-1])
return dir_name, checkpoint
def load_weights(self, filepath):
"""Modified version of the correspoding Keras function with
the addition of multi-GPU support and the ability to exclude
some layers from loading.
exlude: list of layer names to excluce
"""
if os.path.exists(filepath):
state_dict = torch.load(filepath)
self.load_state_dict(state_dict, strict=False)
else:
print("Weight file not found ...")
# Update the log directory
self.set_log_dir(filepath)
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
def detect(self, images):
"""Runs the detection pipeline.
images: List of images, potentially of different sizes.
Returns a list of dicts, one dict per image. The dict contains:
rois: [N, (y1, x1, y2, x2)] detection bounding boxes
class_ids: [N] int class IDs
scores: [N] float probability scores for the class IDs
masks: [H, W, N] instance binary masks
"""
# Mold inputs to format expected by the neural network
molded_images, image_metas, windows = self.mold_inputs(images)
# Convert images to torch tensor
molded_images = torch.from_numpy(molded_images.transpose(0, 3, 1, 2)).float()
# To GPU
if self.config.GPU_COUNT:
molded_images = molded_images.cuda()
# Wrap in variable
molded_images = Variable(molded_images, volatile=True)
# Run object detection
detections, mrcnn_mask = self.predict([molded_images, image_metas], mode='inference')
# Convert to numpy
detections = detections.data.cpu().numpy()
mrcnn_mask = mrcnn_mask.permute(0, 1, 3, 4, 2).data.cpu().numpy()
# Process detections
results = []
for i, image in enumerate(images):
final_rois, final_class_ids, final_scores, final_masks =\
self.unmold_detections(detections[i], mrcnn_mask[i],
image.shape, windows[i])
results.append({
"rois": final_rois,
"class_ids": final_class_ids,
"scores": final_scores,
"masks": final_masks,
})
return results
def predict(self, input, mode):
molded_images = input[0]
image_metas = input[1]
if mode == 'inference':
self.eval()
elif mode == 'training':
self.train()
# Set batchnorm always in eval mode during training
def set_bn_eval(m):
classname = m.__class__.__name__
if classname.find('BatchNorm') != -1:
m.eval()
self.apply(set_bn_eval)
# Feature extraction
[p2_out, p3_out, p4_out, p5_out, p6_out] = self.fpn(molded_images)
# Note that P6 is used in RPN, but not in the classifier heads.
rpn_feature_maps = [p2_out, p3_out, p4_out, p5_out, p6_out]
mrcnn_feature_maps = [p2_out, p3_out, p4_out, p5_out]
# Loop through pyramid layers
layer_outputs = [] # list of lists
for p in rpn_feature_maps:
layer_outputs.append(self.rpn(p))
# Concatenate layer outputs
# Convert from list of lists of level outputs to list of lists
# of outputs across levels.
# e.g. [[a1, b1, c1], [a2, b2, c2]] => [[a1, a2], [b1, b2], [c1, c2]]
outputs = list(zip(*layer_outputs))
outputs = [torch.cat(list(o), dim=1) for o in outputs]
rpn_class_logits, rpn_class, rpn_bbox = outputs
# Generate proposals
# Proposals are [batch, N, (y1, x1, y2, x2)] in normalized coordinates
# and zero padded.
proposal_count = self.config.POST_NMS_ROIS_TRAINING if mode == "training" \
else self.config.POST_NMS_ROIS_INFERENCE
rpn_rois = proposal_layer([rpn_class, rpn_bbox],
proposal_count=proposal_count,
nms_threshold=self.config.RPN_NMS_THRESHOLD,
anchors=self.anchors,
config=self.config)
if mode == 'inference':
# Network Heads
# Proposal classifier and BBox regressor heads
mrcnn_class_logits, mrcnn_class, mrcnn_bbox = self.classifier(mrcnn_feature_maps, rpn_rois)
# Detections
# output is [batch, num_detections, (y1, x1, y2, x2, class_id, score)] in image coordinates
detections = detection_layer(self.config, rpn_rois, mrcnn_class, mrcnn_bbox, image_metas)
# Convert boxes to normalized coordinates
# TODO: let DetectionLayer return normalized coordinates to avoid
# unnecessary conversions
h, w = self.config.IMAGE_SHAPE[:2]
scale = Variable(torch.from_numpy(np.array([h, w, h, w])).float(), requires_grad=False)
if self.config.GPU_COUNT:
scale = scale.cuda()
detection_boxes = detections[:, :4] / scale
# Add back batch dimension
detection_boxes = detection_boxes.unsqueeze(0)
# Create masks for detections
mrcnn_mask = self.mask(mrcnn_feature_maps, detection_boxes)
# Add back batch dimension
detections = detections.unsqueeze(0)
mrcnn_mask = mrcnn_mask.unsqueeze(0)
return [detections, mrcnn_mask]
elif mode == 'training':
gt_class_ids = input[2]
gt_boxes = input[3]
gt_masks = input[4]
# Normalize coordinates
h, w = self.config.IMAGE_SHAPE[:2]
scale = Variable(torch.from_numpy(np.array([h, w, h, w])).float(), requires_grad=False)
if self.config.GPU_COUNT:
scale = scale.cuda()
gt_boxes = gt_boxes / scale
# Generate detection targets
# Subsamples proposals and generates target outputs for training
# Note that proposal class IDs, gt_boxes, and gt_masks are zero
# padded. Equally, returned rois and targets are zero padded.
rois, target_class_ids, target_deltas, target_mask = \
detection_target_layer(rpn_rois, gt_class_ids, gt_boxes, gt_masks, self.config)
if not rois.size():
mrcnn_class_logits = Variable(torch.FloatTensor())
mrcnn_class = Variable(torch.IntTensor())
mrcnn_bbox = Variable(torch.FloatTensor())
mrcnn_mask = Variable(torch.FloatTensor())
if self.config.GPU_COUNT:
mrcnn_class_logits = mrcnn_class_logits.cuda()
mrcnn_class = mrcnn_class.cuda()
mrcnn_bbox = mrcnn_bbox.cuda()
mrcnn_mask = mrcnn_mask.cuda()
else:
# Network Heads
# Proposal classifier and BBox regressor heads
mrcnn_class_logits, mrcnn_class, mrcnn_bbox = self.classifier(mrcnn_feature_maps, rois)
# Create masks for detections
mrcnn_mask = self.mask(mrcnn_feature_maps, rois)
return [rpn_class_logits, rpn_bbox, target_class_ids, mrcnn_class_logits, target_deltas, mrcnn_bbox, target_mask, mrcnn_mask]
def train_model(self, train_dataset, val_dataset, learning_rate, epochs, layers):
"""Train the model.
train_dataset, val_dataset: Training and validation Dataset objects.
learning_rate: The learning rate to train with
epochs: Number of training epochs. Note that previous training epochs
are considered to be done alreay, so this actually determines
the epochs to train in total rather than in this particaular
call.
layers: Allows selecting wich layers to train. It can be:
- A regular expression to match layer names to train
- One of these predefined values:
heaads: The RPN, classifier and mask heads of the network
all: All the layers
3+: Train Resnet stage 3 and up
4+: Train Resnet stage 4 and up
5+: Train Resnet stage 5 and up
"""
# Pre-defined layer regular expressions
layer_regex = {
# all layers but the backbone
"heads": r"(fpn.P5\_.*)|(fpn.P4\_.*)|(fpn.P3\_.*)|(fpn.P2\_.*)|(rpn.*)|(classifier.*)|(mask.*)",
# From a specific Resnet stage and up
"3+": r"(fpn.C3.*)|(fpn.C4.*)|(fpn.C5.*)|(fpn.P5\_.*)|(fpn.P4\_.*)|(fpn.P3\_.*)|(fpn.P2\_.*)|(rpn.*)|(classifier.*)|(mask.*)",
"4+": r"(fpn.C4.*)|(fpn.C5.*)|(fpn.P5\_.*)|(fpn.P4\_.*)|(fpn.P3\_.*)|(fpn.P2\_.*)|(rpn.*)|(classifier.*)|(mask.*)",
"5+": r"(fpn.C5.*)|(fpn.P5\_.*)|(fpn.P4\_.*)|(fpn.P3\_.*)|(fpn.P2\_.*)|(rpn.*)|(classifier.*)|(mask.*)",
# All layers
"all": ".*",
}
if layers in layer_regex.keys():
layers = layer_regex[layers]
# Data generators
train_set = Dataset(train_dataset, self.config, augment=True)
train_generator = torch.utils.data.DataLoader(train_set, batch_size=1, shuffle=True, num_workers=4)
val_set = Dataset(val_dataset, self.config, augment=True)
val_generator = torch.utils.data.DataLoader(val_set, batch_size=1, shuffle=True, num_workers=4)
# Train
log("\nStarting at epoch {}. LR={}\n".format(self.epoch+1, learning_rate))
log("Checkpoint Path: {}".format(self.checkpoint_path))
self.set_trainable(layers)
# Optimizer object
# Add L2 Regularization
# Skip gamma and beta weights of batch normalization layers.
trainables_wo_bn = [param for name, param in self.named_parameters() if param.requires_grad and not 'bn' in name]
trainables_only_bn = [param for name, param in self.named_parameters() if param.requires_grad and 'bn' in name]
optimizer = optim.SGD([
{'params': trainables_wo_bn, 'weight_decay': self.config.WEIGHT_DECAY},
{'params': trainables_only_bn}
], lr=learning_rate, momentum=self.config.LEARNING_MOMENTUM)
for epoch in range(self.epoch+1, epochs+1):
log("Epoch {}/{}.".format(epoch,epochs))
# Training
loss, loss_rpn_class, loss_rpn_bbox, loss_mrcnn_class, loss_mrcnn_bbox, loss_mrcnn_mask = self.train_epoch(train_generator, optimizer, self.config.STEPS_PER_EPOCH)
# Validation
val_loss, val_loss_rpn_class, val_loss_rpn_bbox, val_loss_mrcnn_class, val_loss_mrcnn_bbox, val_loss_mrcnn_mask = self.valid_epoch(val_generator, self.config.VALIDATION_STEPS)
# Statistics
self.loss_history.append([loss, loss_rpn_class, loss_rpn_bbox, loss_mrcnn_class, loss_mrcnn_bbox, loss_mrcnn_mask])
self.val_loss_history.append([val_loss, val_loss_rpn_class, val_loss_rpn_bbox, val_loss_mrcnn_class, val_loss_mrcnn_bbox, val_loss_mrcnn_mask])
visualize.plot_loss(self.loss_history, self.val_loss_history, save=True, log_dir=self.log_dir)
# Save model
torch.save(self.state_dict(), self.checkpoint_path.format(epoch))
self.epoch = epochs
def train_epoch(self, datagenerator, optimizer, steps):
batch_count = 0
loss_sum = 0
loss_rpn_class_sum = 0
loss_rpn_bbox_sum = 0
loss_mrcnn_class_sum = 0
loss_mrcnn_bbox_sum = 0
loss_mrcnn_mask_sum = 0
step = 0
optimizer.zero_grad()
for inputs in datagenerator:
batch_count += 1
images = inputs[0]
image_metas = inputs[1]
rpn_match = inputs[2]
rpn_bbox = inputs[3]
gt_class_ids = inputs[4]
gt_boxes = inputs[5]
gt_masks = inputs[6]
# image_metas as numpy array
image_metas = image_metas.numpy()
# Wrap in variables
images = Variable(images)
rpn_match = Variable(rpn_match)
rpn_bbox = Variable(rpn_bbox)
gt_class_ids = Variable(gt_class_ids)
gt_boxes = Variable(gt_boxes)
gt_masks = Variable(gt_masks)
# To GPU
if self.config.GPU_COUNT:
images = images.cuda()
rpn_match = rpn_match.cuda()
rpn_bbox = rpn_bbox.cuda()
gt_class_ids = gt_class_ids.cuda()
gt_boxes = gt_boxes.cuda()
gt_masks = gt_masks.cuda()
# Run object detection
rpn_class_logits, rpn_pred_bbox, target_class_ids, mrcnn_class_logits, target_deltas, mrcnn_bbox, target_mask, mrcnn_mask = \
self.predict([images, image_metas, gt_class_ids, gt_boxes, gt_masks], mode='training')
# Compute losses
rpn_class_loss, rpn_bbox_loss, mrcnn_class_loss, mrcnn_bbox_loss, mrcnn_mask_loss = compute_losses(rpn_match, rpn_bbox, rpn_class_logits, rpn_pred_bbox, target_class_ids, mrcnn_class_logits, target_deltas, mrcnn_bbox, target_mask, mrcnn_mask)
loss = rpn_class_loss + rpn_bbox_loss + mrcnn_class_loss + mrcnn_bbox_loss + mrcnn_mask_loss
# Backpropagation
loss.backward()
torch.nn.utils.clip_grad_norm(self.parameters(), 5.0)
if (batch_count % self.config.BATCH_SIZE) == 0:
optimizer.step()
optimizer.zero_grad()
batch_count = 0
# Progress
printProgressBar(step + 1, steps, prefix="\t{}/{}".format(step + 1, steps),
suffix="Complete - loss: {:.5f} - rpn_class_loss: {:.5f} - rpn_bbox_loss: {:.5f} - mrcnn_class_loss: {:.5f} - mrcnn_bbox_loss: {:.5f} - mrcnn_mask_loss: {:.5f}".format(
loss.data.cpu()[0], rpn_class_loss.data.cpu()[0], rpn_bbox_loss.data.cpu()[0],
mrcnn_class_loss.data.cpu()[0], mrcnn_bbox_loss.data.cpu()[0],
mrcnn_mask_loss.data.cpu()[0]), length=10)
# Statistics
loss_sum += loss.data.cpu()[0]/steps
loss_rpn_class_sum += rpn_class_loss.data.cpu()[0]/steps
loss_rpn_bbox_sum += rpn_bbox_loss.data.cpu()[0]/steps
loss_mrcnn_class_sum += mrcnn_class_loss.data.cpu()[0]/steps
loss_mrcnn_bbox_sum += mrcnn_bbox_loss.data.cpu()[0]/steps
loss_mrcnn_mask_sum += mrcnn_mask_loss.data.cpu()[0]/steps
# Break after 'steps' steps
if step==steps-1:
break
step += 1
return loss_sum, loss_rpn_class_sum, loss_rpn_bbox_sum, loss_mrcnn_class_sum, loss_mrcnn_bbox_sum, loss_mrcnn_mask_sum
def valid_epoch(self, datagenerator, steps):
step = 0
loss_sum = 0
loss_rpn_class_sum = 0
loss_rpn_bbox_sum = 0
loss_mrcnn_class_sum = 0
loss_mrcnn_bbox_sum = 0
loss_mrcnn_mask_sum = 0
for inputs in datagenerator:
images = inputs[0]
image_metas = inputs[1]
rpn_match = inputs[2]
rpn_bbox = inputs[3]
gt_class_ids = inputs[4]
gt_boxes = inputs[5]
gt_masks = inputs[6]
# image_metas as numpy array
image_metas = image_metas.numpy()
# Wrap in variables
images = Variable(images, volatile=True)
rpn_match = Variable(rpn_match, volatile=True)
rpn_bbox = Variable(rpn_bbox, volatile=True)
gt_class_ids = Variable(gt_class_ids, volatile=True)
gt_boxes = Variable(gt_boxes, volatile=True)
gt_masks = Variable(gt_masks, volatile=True)
# To GPU
if self.config.GPU_COUNT:
images = images.cuda()
rpn_match = rpn_match.cuda()
rpn_bbox = rpn_bbox.cuda()
gt_class_ids = gt_class_ids.cuda()
gt_boxes = gt_boxes.cuda()
gt_masks = gt_masks.cuda()
# Run object detection
rpn_class_logits, rpn_pred_bbox, target_class_ids, mrcnn_class_logits, target_deltas, mrcnn_bbox, target_mask, mrcnn_mask = \
self.predict([images, image_metas, gt_class_ids, gt_boxes, gt_masks], mode='training')
if not target_class_ids.size():
continue
# Compute losses
rpn_class_loss, rpn_bbox_loss, mrcnn_class_loss, mrcnn_bbox_loss, mrcnn_mask_loss = compute_losses(rpn_match, rpn_bbox, rpn_class_logits, rpn_pred_bbox, target_class_ids, mrcnn_class_logits, target_deltas, mrcnn_bbox, target_mask, mrcnn_mask)
loss = rpn_class_loss + rpn_bbox_loss + mrcnn_class_loss + mrcnn_bbox_loss + mrcnn_mask_loss
# Progress
printProgressBar(step + 1, steps, prefix="\t{}/{}".format(step + 1, steps),
suffix="Complete - loss: {:.5f} - rpn_class_loss: {:.5f} - rpn_bbox_loss: {:.5f} - mrcnn_class_loss: {:.5f} - mrcnn_bbox_loss: {:.5f} - mrcnn_mask_loss: {:.5f}".format(
loss.data.cpu()[0], rpn_class_loss.data.cpu()[0], rpn_bbox_loss.data.cpu()[0],
mrcnn_class_loss.data.cpu()[0], mrcnn_bbox_loss.data.cpu()[0],
mrcnn_mask_loss.data.cpu()[0]), length=10)
# Statistics
loss_sum += loss.data.cpu()[0]/steps
loss_rpn_class_sum += rpn_class_loss.data.cpu()[0]/steps
loss_rpn_bbox_sum += rpn_bbox_loss.data.cpu()[0]/steps
loss_mrcnn_class_sum += mrcnn_class_loss.data.cpu()[0]/steps
loss_mrcnn_bbox_sum += mrcnn_bbox_loss.data.cpu()[0]/steps
loss_mrcnn_mask_sum += mrcnn_mask_loss.data.cpu()[0]/steps
# Break after 'steps' steps
if step==steps-1:
break
step += 1
return loss_sum, loss_rpn_class_sum, loss_rpn_bbox_sum, loss_mrcnn_class_sum, loss_mrcnn_bbox_sum, loss_mrcnn_mask_sum
def mold_inputs(self, images):
"""Takes a list of images and modifies them to the format expected
as an input to the neural network.
images: List of image matricies [height,width,depth]. Images can have
different sizes.
Returns 3 Numpy matricies:
molded_images: [N, h, w, 3]. Images resized and normalized.
image_metas: [N, length of meta data]. Details about each image.
windows: [N, (y1, x1, y2, x2)]. The portion of the image that has the
original image (padding excluded).
"""
molded_images = []
image_metas = []
windows = []
for image in images:
# Resize image to fit the model expected size
# TODO: move resizing to mold_image()
molded_image, window, scale, padding = utils.resize_image(
image,
min_dim=self.config.IMAGE_MIN_DIM,
max_dim=self.config.IMAGE_MAX_DIM,
padding=self.config.IMAGE_PADDING)
molded_image = mold_image(molded_image, self.config)
# Build image_meta
image_meta = compose_image_meta(
0, image.shape, window,
np.zeros([self.config.NUM_CLASSES], dtype=np.int32))
# Append
molded_images.append(molded_image)
windows.append(window)
image_metas.append(image_meta)
# Pack into arrays
molded_images = np.stack(molded_images)
image_metas = np.stack(image_metas)
windows = np.stack(windows)
return molded_images, image_metas, windows
def unmold_detections(self, detections, mrcnn_mask, image_shape, window):
"""Reformats the detections of one image from the format of the neural
network output to a format suitable for use in the rest of the
application.
detections: [N, (y1, x1, y2, x2, class_id, score)]
mrcnn_mask: [N, height, width, num_classes]
image_shape: [height, width, depth] Original size of the image before resizing
window: [y1, x1, y2, x2] Box in the image where the real image is
excluding the padding.
Returns:
boxes: [N, (y1, x1, y2, x2)] Bounding boxes in pixels
class_ids: [N] Integer class IDs for each bounding box
scores: [N] Float probability scores of the class_id
masks: [height, width, num_instances] Instance masks
"""
# How many detections do we have?
# Detections array is padded with zeros. Find the first class_id == 0.
zero_ix = np.where(detections[:, 4] == 0)[0]
N = zero_ix[0] if zero_ix.shape[0] > 0 else detections.shape[0]
# Extract boxes, class_ids, scores, and class-specific masks
boxes = detections[:N, :4]
class_ids = detections[:N, 4].astype(np.int32)
scores = detections[:N, 5]
masks = mrcnn_mask[np.arange(N), :, :, class_ids]
# Compute scale and shift to translate coordinates to image domain.
h_scale = image_shape[0] / (window[2] - window[0])
w_scale = image_shape[1] / (window[3] - window[1])
scale = min(h_scale, w_scale)
shift = window[:2] # y, x
scales = np.array([scale, scale, scale, scale])
shifts = np.array([shift[0], shift[1], shift[0], shift[1]])
# Translate bounding boxes to image domain
boxes = np.multiply(boxes - shifts, scales).astype(np.int32)
# Filter out detections with zero area. Often only happens in early
# stages of training when the network weights are still a bit random.
exclude_ix = np.where(
(boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) <= 0)[0]
if exclude_ix.shape[0] > 0:
boxes = np.delete(boxes, exclude_ix, axis=0)
class_ids = np.delete(class_ids, exclude_ix, axis=0)
scores = np.delete(scores, exclude_ix, axis=0)
masks = np.delete(masks, exclude_ix, axis=0)
N = class_ids.shape[0]
# Resize masks to original image size and set boundary threshold.
full_masks = []
for i in range(N):
# Convert neural network mask to full size mask
full_mask = utils.unmold_mask(masks[i], boxes[i], image_shape)
full_masks.append(full_mask)
full_masks = np.stack(full_masks, axis=-1)\
if full_masks else np.empty((0,) + masks.shape[1:3])
return boxes, class_ids, scores, full_masks
############################################################
# Data Formatting
############################################################
def compose_image_meta(image_id, image_shape, window, active_class_ids):
"""Takes attributes of an image and puts them in one 1D array. Use
parse_image_meta() to parse the values back.
image_id: An int ID of the image. Useful for debugging.
image_shape: [height, width, channels]
window: (y1, x1, y2, x2) in pixels. The area of the image where the real
image is (excluding the padding)
active_class_ids: List of class_ids available in the dataset from which
the image came. Useful if training on images from multiple datasets
where not all classes are present in all datasets.
"""
meta = np.array(
[image_id] + # size=1
list(image_shape) + # size=3
list(window) + # size=4 (y1, x1, y2, x2) in image cooredinates
list(active_class_ids) # size=num_classes
)
return meta
# Two functions (for Numpy and TF) to parse image_meta tensors.
def parse_image_meta(meta):
"""Parses an image info Numpy array to its components.
See compose_image_meta() for more details.
"""
image_id = meta[:, 0]
image_shape = meta[:, 1:4]
window = meta[:, 4:8] # (y1, x1, y2, x2) window of image in in pixels
active_class_ids = meta[:, 8:]
return image_id, image_shape, window, active_class_ids
def parse_image_meta_graph(meta):
"""Parses a tensor that contains image attributes to its components.
See compose_image_meta() for more details.
meta: [batch, meta length] where meta length depends on NUM_CLASSES
"""
image_id = meta[:, 0]
image_shape = meta[:, 1:4]
window = meta[:, 4:8]
active_class_ids = meta[:, 8:]
return [image_id, image_shape, window, active_class_ids]
def mold_image(images, config):
"""Takes RGB images with 0-255 values and subtraces
the mean pixel and converts it to float. Expects image
colors in RGB order.
"""
return images.astype(np.float32) - config.MEAN_PIXEL
def unmold_image(normalized_images, config):
"""Takes a image normalized with mold() and returns the original."""
return (normalized_images + config.MEAN_PIXEL).astype(np.uint8)
