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resnet_mx_101_e2e_mask.py
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resnet_mx_101_e2e_mask.py
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# --------------------------------------------------------------
# SNIPER: Efficient Multi-Scale Training
# Licensed under The Apache-2.0 License [see LICENSE for details]
# by Mahyar Najibi and Bharat Singh
# --------------------------------------------------------------
import mxnet as mx
from symbols.symbol import Symbol
from operator_py.box_annotator_ohem import *
import numpy as np
def checkpoint_callback(bbox_param_names, prefix, means, stds):
def _callback(iter_no, sym, arg, aux):
weight = arg[bbox_param_names[0]]
bias = arg[bbox_param_names[1]]
stds = np.array([0.1, 0.1, 0.2, 0.2])
arg[bbox_param_names[0]+'_test'] = (weight.T * mx.nd.array(stds)).T
arg[bbox_param_names[1]+'_test'] =bias * mx.nd.array(stds)
mx.model.save_checkpoint(prefix, iter_no + 1, sym, arg, aux)
arg.pop(bbox_param_names[0]+'_test')
arg.pop(bbox_param_names[1]+'_test')
return _callback
class resnet_mx_101_e2e_mask(Symbol):
def __init__(self, n_proposals=400, momentum=0.95, fix_bn=False, test_nbatch=1):
"""
Use __init__ to define parameter network needs
"""
self.momentum = momentum
self.use_global_stats = True
self.workspace = 512
self.units = (3, 4, 23, 3) # use for 101
self.filter_list = [64, 256, 512, 1024, 2048]
self.fix_bn = fix_bn
self.test_nbatch= test_nbatch
def get_bbox_param_names(self):
return ['bbox_pred_weight', 'bbox_pred_bias']
def residual_unit(self, data, num_filter, stride, dim_match, name, bn_mom=0.9, workspace=512, memonger=False,
fix_bn=False):
if fix_bn or self.fix_bn:
bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, use_global_stats=True, name=name + '_bn1')
else:
bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=self.momentum, name=name + '_bn1')
act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
conv1 = mx.sym.Convolution(data=act1, num_filter=int(num_filter * 0.25), kernel=(1, 1), stride=(1, 1),
pad=(0, 0),
no_bias=True, workspace=workspace, name=name + '_conv1')
if fix_bn or self.fix_bn:
bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, use_global_stats=True, name=name + '_bn2')
else:
bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=self.momentum, name=name + '_bn2')
act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2')
conv2 = mx.sym.Convolution(data=act2, num_filter=int(num_filter * 0.25), kernel=(3, 3), stride=stride,
pad=(1, 1),
no_bias=True, workspace=workspace, name=name + '_conv2')
if fix_bn or self.fix_bn:
bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, use_global_stats=True, name=name + '_bn3')
else:
bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=self.momentum, name=name + '_bn3')
act3 = mx.sym.Activation(data=bn3, act_type='relu', name=name + '_relu3')
conv3 = mx.sym.Convolution(data=act3, num_filter=num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0),
no_bias=True,
workspace=workspace, name=name + '_conv3')
if dim_match:
shortcut = data
else:
shortcut = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1, 1), stride=stride, no_bias=True,
workspace=workspace, name=name + '_sc')
if memonger:
shortcut._set_attr(mirror_stage='True')
return conv3 + shortcut
def residual_unit_dilate(self, data, num_filter, stride, dim_match, name, bn_mom=0.9, workspace=512,
memonger=False):
if self.fix_bn:
bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, use_global_stats=True, name=name + '_bn1')
else:
bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=self.momentum, name=name + '_bn1')
act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
conv1 = mx.sym.Convolution(data=act1, num_filter=int(num_filter * 0.25), kernel=(1, 1), stride=(1, 1),
pad=(0, 0),
no_bias=True, workspace=workspace, name=name + '_conv1')
if self.fix_bn:
bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, use_global_stats=True, name=name + '_bn2')
else:
bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=self.momentum, name=name + '_bn2')
act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2')
conv2 = mx.sym.Convolution(data=act2, num_filter=int(num_filter * 0.25), kernel=(3, 3), dilate=(2, 2),
stride=stride, pad=(2, 2),
no_bias=True, workspace=workspace, name=name + '_conv2')
if self.fix_bn:
bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, use_global_stats=True, name=name + '_bn3')
else:
bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=self.momentum, name=name + '_bn3')
act3 = mx.sym.Activation(data=bn3, act_type='relu', name=name + '_relu3')
conv3 = mx.sym.Convolution(data=act3, num_filter=num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0),
no_bias=True,
workspace=workspace, name=name + '_conv3')
if dim_match:
shortcut = data
else:
shortcut = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1, 1), stride=stride, no_bias=True,
workspace=workspace, name=name + '_sc')
if memonger:
shortcut._set_attr(mirror_stage='True')
return conv3 + shortcut
def residual_unit_deform(self, data, num_filter, stride, dim_match, name, bn_mom=0.9, workspace=512,
memonger=False):
if self.fix_bn:
bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, use_global_stats=True, name=name + '_bn1')
else:
bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=self.momentum, name=name + '_bn1')
act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
conv1 = mx.sym.Convolution(data=act1, num_filter=int(num_filter * 0.25), kernel=(1, 1), stride=(1, 1),
pad=(0, 0), no_bias=True, workspace=workspace, name=name + '_conv1')
if self.fix_bn:
bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, use_global_stats=True, name=name + '_bn2')
else:
bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=self.momentum, name=name + '_bn2')
act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2')
offset = mx.symbol.Convolution(name=name + '_offset', data=act2,
num_filter=72, pad=(2, 2), kernel=(3, 3), stride=(1, 1),
dilate=(2, 2), cudnn_off=True)
conv2 = mx.contrib.symbol.DeformableConvolution(name=name + '_conv2', data=act2,
offset=offset,
num_filter=512, pad=(2, 2), kernel=(3, 3),
num_deformable_group=4,
stride=(1, 1), dilate=(2, 2), no_bias=True)
if self.fix_bn:
bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, use_global_stats=True, name=name + '_bn3')
else:
bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=self.momentum, name=name + '_bn3')
act3 = mx.sym.Activation(data=bn3, act_type='relu', name=name + '_relu3')
conv3 = mx.sym.Convolution(data=act3, num_filter=num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0),
no_bias=True, workspace=workspace, name=name + '_conv3')
if dim_match:
shortcut = data
else:
shortcut = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1, 1), stride=stride, no_bias=True,
workspace=workspace, name=name + '_sc')
if memonger:
shortcut._set_attr(mirror_stage='True')
return conv3 + shortcut
def get_rpn(self, conv_feat, num_anchors):
conv_feat = mx.sym.Cast(data=conv_feat, dtype=np.float32)
rpn_conv = mx.sym.Convolution(
data=conv_feat, kernel=(3, 3), pad=(1, 1), num_filter=512, name="rpn_conv_3x3")
rpn_relu = mx.sym.Activation(data=rpn_conv, act_type="relu", name="rpn_relu")
rpn_cls_score = mx.sym.Convolution(
data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=2 * num_anchors, name="rpn_cls_score")
rpn_bbox_pred = mx.sym.Convolution(
data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=4 * num_anchors, name="rpn_bbox_pred")
return rpn_cls_score, rpn_bbox_pred
def get_symbol_rpn(self, cfg, is_train=True):
num_anchors = cfg.network.NUM_ANCHORS
# input init
if is_train:
data = mx.sym.Variable(name="data")
rpn_label = mx.sym.Variable(name='label')
rpn_bbox_target = mx.sym.Variable(name='bbox_target')
rpn_bbox_weight = mx.sym.Variable(name='bbox_weight')
gt_boxes = mx.sym.Variable(name='gt_boxes')
valid_ranges = mx.sym.Variable(name='valid_ranges')
im_info = mx.sym.Variable(name='im_info')
gt_masks = mx.sym.Variable(name='gt_masks')
else:
data = mx.sym.Variable(name="data")
im_info = mx.sym.Variable(name='im_info')
im_ids = mx.sym.Variable(name='im_ids')
# shared convolutional layers
conv_feat = self.resnetc4(data, fp16=cfg.TRAIN.fp16)
# res5
relut = self.resnetc5(conv_feat, deform=True)
relu1 = mx.symbol.Concat(*[conv_feat, relut], name='cat4')
if cfg.TRAIN.fp16:
relu1 = mx.sym.Cast(data=relu1, dtype=np.float32)
rpn_cls_score, rpn_bbox_pred = self.get_rpn(conv_feat, num_anchors)
rpn_cls_score_reshape = mx.sym.Reshape(data=rpn_cls_score, shape=(0, 2, -1, 0),
name="rpn_cls_score_reshape")
if is_train:
# prepare rpn data
if cfg.TRAIN.fp16 == True:
grad_scale = float(cfg.TRAIN.scale)
else:
grad_scale = 1.0
# classification
rpn_cls_prob = mx.sym.SoftmaxOutput(data=rpn_cls_score_reshape, label=rpn_label, multi_output=True,
normalization='valid', use_ignore=True, ignore_label=-1,
name="rpn_cls_prob", grad_scale=grad_scale)
if cfg.TRAIN.fp16 == True:
grad_scale = float(cfg.TRAIN.scale)
else:
grad_scale = 1.0
# bounding box regression
rpn_bbox_loss_ = rpn_bbox_weight * mx.sym.smooth_l1(name='rpn_bbox_loss_', scalar=1.0,
data=(rpn_bbox_pred - rpn_bbox_target))
rpn_bbox_loss = mx.sym.MakeLoss(name='rpn_bbox_loss', data=rpn_bbox_loss_,
grad_scale=3 * grad_scale / float(
cfg.TRAIN.BATCH_IMAGES * cfg.TRAIN.RPN_BATCH_SIZE))
group = mx.sym.Group([rpn_cls_prob, rpn_bbox_loss])
else:
# ROI Proposal
rpn_cls_prob = mx.sym.SoftmaxActivation(
data=rpn_cls_score_reshape, mode="channel", name="rpn_cls_prob")
rpn_cls_prob_reshape = mx.sym.Reshape(
data=rpn_cls_prob, shape=(0, 2 * num_anchors, -1, 0), name='rpn_cls_prob_reshape')
rois, rpn_scores = mx.sym.MultiProposal(cls_prob=rpn_cls_prob_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info,
name='rois', batch_size=self.test_nbatch,
rpn_pre_nms_top_n=cfg.TEST.RPN_PRE_NMS_TOP_N,
rpn_post_nms_top_n=cfg.TEST.RPN_POST_NMS_TOP_N,
rpn_min_size=cfg.TEST.RPN_MIN_SIZE,
threshold=cfg.TEST.RPN_NMS_THRESH,
feature_stride=cfg.network.RPN_FEAT_STRIDE,
ratios=tuple(cfg.network.ANCHOR_RATIOS),
scales=tuple(cfg.network.ANCHOR_SCALES))
group = mx.sym.Group([rois, rpn_scores, im_ids])
self.sym = group
return group
def get_mask_head(self, feat, num_layers=4, num_classes=80):
mask_relu_out = feat
for i in range(num_layers):
mask_conv_out = mx.sym.Convolution(
data=mask_relu_out, kernel=(3, 3), pad=(1, 1), num_filter=256, name="mask_conv_3x3_{}".format(i+1))
mask_relu_out = mx.sym.Activation(data=mask_conv_out, act_type="relu", name="mask_relu_{}".format(i+1))
mask_relu_out = mx.sym.Cast(data=mask_relu_out, dtype=np.float32)
# Upsample the output
mask_deconv = mx.symbol.Deconvolution(data=mask_relu_out, kernel=(2, 2),
stride=(2, 2), pad=(0, 0), num_filter=256, name="mask_deconv")
mask_deconv_relu = mx.sym.Activation(data=mask_deconv, act_type="relu", name="mask_deconv_relu")
# Add the mask output layer
mask_out = mx.sym.Convolution(
data=mask_deconv_relu, kernel=(1, 1), pad=(0, 0), num_filter=80*2, name="mask_out")
return mask_out
def get_symbol_rcnn(self, cfg, is_train=True):
num_anchors = cfg.network.NUM_ANCHORS
# input init
if is_train:
data = mx.sym.Variable(name="data")
rpn_label = mx.sym.Variable(name='label')
rpn_bbox_target = mx.sym.Variable(name='bbox_target')
rpn_bbox_weight = mx.sym.Variable(name='bbox_weight')
gt_boxes = mx.sym.Variable(name='gt_boxes')
valid_ranges = mx.sym.Variable(name='valid_ranges')
im_info = mx.sym.Variable(name='im_info')
gt_masks = mx.sym.Variable(name='gt_masks')
if cfg.TRAIN.AUTO_FOCUS:
scale_label = mx.sym.Variable(name='scale_label')
else:
data = mx.sym.Variable(name="data")
im_info = mx.sym.Variable(name='im_info')
im_ids = mx.sym.Variable(name='im_ids')
chip_ids = mx.sym.Variable(name='chip_ids')
# shared convolutional layers
conv_feat = self.resnetc4(data, fp16=cfg.TRAIN.fp16)
# res5
relut = self.resnetc5(conv_feat, deform=True)
relu1 = mx.symbol.Concat(*[conv_feat, relut], name='cat4')
if cfg.TRAIN.fp16:
relu1 = mx.sym.Cast(data=relu1, dtype=np.float32)
rpn_cls_score, rpn_bbox_pred = self.get_rpn(conv_feat, num_anchors)
conv_new_1 = mx.sym.Convolution(data=relu1, kernel=(1, 1), num_filter=256, name="conv_new_1")
conv_new_1_relu = mx.sym.Activation(data=conv_new_1, act_type='relu', name='conv_new_1_relu')
if cfg.TRAIN.AUTO_FOCUS or cfg.TEST.AUTO_FOCUS:
conv_new_2 = mx.sym.Convolution(data=relu1, kernel=(3, 3), pad=(1, 1), num_filter=256, name="conv_new_2")
conv_new_2_relu = mx.sym.Activation(data=conv_new_2, act_type='relu', name='conv_new_2_relu')
conv_new_3 = mx.sym.Convolution(data=conv_new_2_relu, kernel=(1, 1), num_filter=256, name="conv_new_3")
conv_new_3_relu = mx.sym.Activation(data=conv_new_3, act_type='relu', name='conv_new_3_relu')
conv_new_out = mx.sym.Convolution(data=conv_new_3_relu, kernel=(1, 1), num_filter=2, name="conv_new_out")
conv_new_out_reshape = mx.sym.Reshape(data=conv_new_out, shape=(0, 2, -1), name="conv_new_out_reshape")
rpn_cls_score_reshape = mx.sym.Reshape(data=rpn_cls_score, shape=(0, 2, -1, 0),
name="rpn_cls_score_reshape")
# The mask branch
if is_train:
# prepare rpn data
if cfg.TRAIN.fp16 == True:
grad_scale = float(cfg.TRAIN.scale)
else:
grad_scale = 1.0
# classification
rpn_cls_prob = mx.sym.SoftmaxOutput(data=rpn_cls_score_reshape, label=rpn_label, multi_output=True,
normalization='valid', use_ignore=True, ignore_label=-1,
name="rpn_cls_prob", grad_scale=grad_scale)
rois, label, bbox_target, bbox_weight, mask_rois, mask_ids = mx.sym.MultiProposalTargetMask(cls_prob=rpn_cls_prob, bbox_pred=rpn_bbox_pred, im_info=im_info,
gt_boxes=gt_boxes, valid_ranges=valid_ranges, batch_size=cfg.TRAIN.BATCH_IMAGES, name='multi_proposal_target_mask')
label = mx.symbol.Reshape(data=label, shape=(-1,), name='label_reshape')
offset_t = mx.contrib.sym.DeformablePSROIPooling(name='offset_t', data=conv_new_1_relu, rois=rois, group_size=1, pooled_size=7,
sample_per_part=4, no_trans=True, part_size=7, output_dim=256, spatial_scale=0.0625)
offset = mx.sym.FullyConnected(name='offset', data=offset_t, num_hidden=7 * 7 * 2, lr_mult=0.01)
offset_reshape = mx.sym.Reshape(data=offset, shape=(-1, 2, 7, 7), name="offset_reshape")
deformable_roi_pool = mx.contrib.sym.DeformablePSROIPooling(name='deformable_roi_pool', data=conv_new_1_relu, rois=rois,
trans=offset_reshape, group_size=1, pooled_size=7, sample_per_part=4,
no_trans=False, part_size=7, output_dim=256, spatial_scale=0.0625, trans_std=0.1)
# 2 fc
fc_new_1 = mx.sym.FullyConnected(name='fc_new_1', data=deformable_roi_pool, num_hidden=1024)
fc_new_1_relu = mx.sym.Activation(data=fc_new_1, act_type='relu', name='fc_new_1_relu')
fc_new_2 = mx.sym.FullyConnected(name='fc_new_2', data=fc_new_1_relu, num_hidden=1024)
fc_new_2_relu = mx.sym.Activation(data=fc_new_2, act_type='relu', name='fc_new_2_relu')
num_classes = cfg.dataset.NUM_CLASSES
num_reg_classes = 1
cls_score = mx.sym.FullyConnected(name='cls_score', data=fc_new_2_relu, num_hidden=num_classes)
bbox_pred = mx.sym.FullyConnected(name='bbox_pred', data=fc_new_2_relu, num_hidden=num_reg_classes * 4)
if cfg.TRAIN.fp16 == True:
grad_scale = float(cfg.TRAIN.scale)
else:
grad_scale = 1.0
cls_prob = mx.sym.SoftmaxOutput(name='cls_prob', data=cls_score, label=label, normalization='valid', use_ignore=True, ignore_label=-1,
grad_scale=grad_scale)
if cfg.TRAIN.AUTO_FOCUS:
scale_prob = mx.sym.SoftmaxOutput(name='cls_scale_prob', data=conv_new_out_reshape, label=scale_label, normalization='valid',
multi_output=True, use_ignore=True, ignore_label=-1, grad_scale=grad_scale)
bbox_loss_ = bbox_weight * mx.sym.smooth_l1(name='bbox_loss_', scalar=1.0,
data=(bbox_pred - bbox_target))
bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=grad_scale / (188.0*16.0))
rcnn_label = label
# reshape output
cls_prob = mx.sym.Reshape(data=cls_prob, shape=(cfg.TRAIN.BATCH_IMAGES, -1, num_classes),
name='cls_prob_reshape')
bbox_loss = mx.sym.Reshape(data=bbox_loss, shape=(cfg.TRAIN.BATCH_IMAGES, -1, 4 * num_reg_classes),
name='bbox_loss_reshape')
# bounding box regression
rpn_bbox_loss_ = rpn_bbox_weight * mx.sym.smooth_l1(name='rpn_bbox_loss_', scalar=1.0,
data=(rpn_bbox_pred - rpn_bbox_target))
rpn_bbox_loss = mx.sym.MakeLoss(name='rpn_bbox_loss', data=rpn_bbox_loss_,
grad_scale=3 * grad_scale / float(
cfg.TRAIN.BATCH_IMAGES * cfg.TRAIN.RPN_BATCH_SIZE))
##################################### Mask Branch #######################################
# Pool positive RoIs
mask_offset_t = mx.contrib.sym.DeformablePSROIPooling(name='mask_offset_t', data=conv_new_1_relu, rois=mask_rois,
group_size=1, pooled_size=14, sample_per_part=4,
no_trans=True, part_size=14, output_dim=256, spatial_scale=0.0625)
mask_offset = mx.sym.FullyConnected(name='mask_offset', data=mask_offset_t, num_hidden=14 * 14 * 2, lr_mult=0.01)
mask_offset_reshape = mx.sym.Reshape(data=mask_offset, shape=(-1, 2, 14, 14), name="mask_offset_reshape")
mask_deformable_roi_pool = mx.contrib.sym.DeformablePSROIPooling(name='mask_deformable_roi_pool', data=conv_new_1_relu,
rois=mask_rois, trans=mask_offset_reshape, group_size=1,
pooled_size=14, sample_per_part=4, no_trans=False, part_size=14,
output_dim=256, spatial_scale=0.0625, trans_std=0.1)
mask_deformable_roi_pool = mx.sym.Cast(data=mask_deformable_roi_pool, dtype=np.float16)
# Put the Mask head
mask_pred = self.get_mask_head(mask_deformable_roi_pool)
# Compute the mask targets
mask_targets, mask_ncls_ids = mx.sym.MaskRcnnTarget(rois=mask_rois, mask_polys=gt_masks, mask_ids=mask_ids,
batch_size=cfg.TRAIN.BATCH_IMAGES, mask_size=28, num_proposals=50, max_polygon_len=500, max_num_gts=100,
num_classes=80)
mask_pcls_ids = mask_ncls_ids + 80
pmask_pred = mx.sym.pick(mask_pred, index=mask_pcls_ids, axis=1, keepdims=True)
nmask_pred = mx.sym.pick(mask_pred, index=mask_ncls_ids, axis=1, keepdims=True)
fmask_pred = mx.sym.Concat(*[nmask_pred, pmask_pred], name="pred_cat")
mask_prob = mx.sym.SoftmaxOutput(data=fmask_pred, label=mask_targets, multi_output=True,
normalization='valid', use_ignore=True, ignore_label=-1,
name="mask_cls_prob", grad_scale=grad_scale)
if cfg.TRAIN.AUTO_FOCUS:
group = mx.sym.Group([rpn_cls_prob, rpn_bbox_loss, scale_prob, cls_prob, bbox_loss, mx.sym.BlockGrad(rcnn_label),
mask_prob, mx.sym.BlockGrad(mask_targets)])
else:
group = mx.sym.Group([rpn_cls_prob, rpn_bbox_loss, cls_prob, bbox_loss, mx.sym.BlockGrad(rcnn_label),
mask_prob, mx.sym.BlockGrad(mask_targets)])
else:
# ROI Proposal
rpn_cls_prob = mx.sym.SoftmaxActivation(
data=rpn_cls_score_reshape, mode="channel", name="rpn_cls_prob")
rpn_cls_prob_reshape = mx.sym.Reshape(
data=rpn_cls_prob, shape=(0, 2 * num_anchors, -1, 0), name='rpn_cls_prob_reshape')
rois, _ = mx.sym.MultiProposal(cls_prob=rpn_cls_prob_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info,
name='rois', batch_size=self.test_nbatch,
rpn_pre_nms_top_n=cfg.TEST.RPN_PRE_NMS_TOP_N,
rpn_post_nms_top_n=cfg.TEST.RPN_POST_NMS_TOP_N,
rpn_min_size=cfg.TEST.RPN_MIN_SIZE,
threshold=cfg.TEST.RPN_NMS_THRESH,
feature_stride=cfg.network.RPN_FEAT_STRIDE,
ratios=tuple(cfg.network.ANCHOR_RATIOS),
scales=tuple(cfg.network.ANCHOR_SCALES))
offset_t = mx.contrib.sym.DeformablePSROIPooling(name='offset_t', data=conv_new_1_relu, rois=rois,
group_size=1, pooled_size=7,
sample_per_part=4, no_trans=True, part_size=7,
output_dim=256, spatial_scale=0.0625)
offset = mx.sym.FullyConnected(name='offset', data=offset_t, num_hidden=7 * 7 * 2, lr_mult=0.01)
offset_reshape = mx.sym.Reshape(data=offset, shape=(-1, 2, 7, 7), name="offset_reshape")
deformable_roi_pool = mx.contrib.sym.DeformablePSROIPooling(name='deformable_roi_pool',
data=conv_new_1_relu, rois=rois,
trans=offset_reshape, group_size=1,
pooled_size=7, sample_per_part=4,
no_trans=False, part_size=7, output_dim=256,
spatial_scale=0.0625, trans_std=0.1)
# 2 fc
fc_new_1 = mx.sym.FullyConnected(name='fc_new_1', data=deformable_roi_pool, num_hidden=1024)
fc_new_1_relu = mx.sym.Activation(data=fc_new_1, act_type='relu', name='fc_new_1_relu')
fc_new_2 = mx.sym.FullyConnected(name='fc_new_2', data=fc_new_1_relu, num_hidden=1024)
fc_new_2_relu = mx.sym.Activation(data=fc_new_2, act_type='relu', name='fc_new_2_relu')
num_classes = cfg.dataset.NUM_CLASSES
num_reg_classes = 1
cls_score = mx.sym.FullyConnected(name='cls_score', data=fc_new_2_relu, num_hidden=num_classes)
bbox_pred = mx.sym.FullyConnected(name='bbox_pred', data=fc_new_2_relu, num_hidden=num_reg_classes * 4)
cls_prob = mx.sym.SoftmaxActivation(name='cls_prob', data=cls_score)
cls_prob = mx.sym.Reshape(data=cls_prob, shape=(self.test_nbatch, -1, num_classes),
name='cls_prob_reshape')
bbox_pred = mx.sym.Reshape(data=bbox_pred, shape=(self.test_nbatch, -1, 4 * num_reg_classes),
name='bbox_pred_reshape')
if cfg.TEST.AUTO_FOCUS:
scale_prob = mx.sym.SoftmaxActivation(name='scale_prob', data=conv_new_out, mode='channel')
group = mx.sym.Group([rois, cls_prob, bbox_pred, im_ids, scale_prob, im_info, chip_ids])
else:
group = mx.sym.Group([rois, cls_prob, bbox_pred, im_ids, im_info, chip_ids])
self.sym = group
return group
def resnetc4(self, data, fp16=False):
units = self.units
filter_list = self.filter_list
bn_mom = self.momentum
workspace = self.workspace
num_stage = len(units)
memonger = False
data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, use_global_stats=True, name='bn_data')
body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2, 2), pad=(3, 3),
no_bias=True, name="conv0", workspace=workspace)
if fp16:
body = mx.sym.Cast(data=body, dtype=np.float16)
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, use_global_stats=True, name='bn0')
body = mx.sym.Activation(data=body, act_type='relu', name='relu0')
body = mx.symbol.Pooling(data=body, kernel=(3, 3), stride=(2, 2), pad=(1, 1), pool_type='max')
for i in range(num_stage - 1):
body = self.residual_unit(body, filter_list[i + 1], (1 if i == 0 else 2, 1 if i == 0 else 2), False,
name='stage%d_unit%d' % (i + 1, 1), workspace=workspace,
memonger=memonger, fix_bn=(i == 0))
for j in range(units[i] - 1):
body = self.residual_unit(body, filter_list[i + 1], (1, 1), True,
name='stage%d_unit%d' % (i + 1, j + 2),
workspace=workspace, memonger=memonger, fix_bn=(i == 0))
return body
def resnetc5(self, body, deform):
units = self.units
filter_list = self.filter_list
workspace = self.workspace
num_stage = len(units)
memonger = False
i = num_stage - 1
if deform:
body = self.residual_unit_deform(body, filter_list[i + 1], (1, 1), False,
name='stage%d_unit%d' % (i + 1, 1), workspace=workspace,
memonger=memonger)
else:
body = self.residual_unit_dilate(body, filter_list[i + 1], (1, 1), False,
name='stage%d_unit%d' % (i + 1, 1), workspace=workspace,
memonger=memonger)
for j in range(units[i] - 1):
if deform:
body = self.residual_unit_deform(body, filter_list[i + 1], (1, 1), True,
name='stage%d_unit%d' % (i + 1, j + 2),
workspace=workspace, memonger=memonger)
else:
body = self.residual_unit_dilate(body, filter_list[i + 1], (1, 1), True,
name='stage%d_unit%d' % (i + 1, j + 2),
workspace=workspace, memonger=memonger)
return body
def init_weight_mask(self, cfg, arg_params, aux_params):
#initializer=mx.init.MSRAPrelu()
arg_params['mask_out_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['mask_out_weight'])
#initializer(mx.init.InitDesc('mask_out_weight'), arg_params['mask_out_weight'])
for i in range(4):
arg_params['mask_conv_3x3_{}_weight'.format(i+1)] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['mask_conv_3x3_{}_weight'.format(i+1)])
#initializer(mx.init.InitDesc('mask_conv_3x3_{}_weight'.format(i+1)), arg_params['mask_conv_3x3_{}_weight'.format(i+1)])
arg_params['mask_conv_3x3_{}_bias'.format(i+1)] = mx.nd.zeros(shape=self.arg_shape_dict['mask_conv_3x3_{}_bias'.format(i+1)])
#init = mx.init.Initializer()
arg_params['mask_deconv_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['mask_deconv_weight'])
#init._init_bilinear('mask_deconv_weight', arg_params['mask_deconv_weight'])
arg_params['mask_out_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['mask_out_bias'])
arg_params['mask_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['mask_offset_weight'])
arg_params['mask_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['mask_offset_bias'])
def init_weight_rcnn(self, cfg, arg_params, aux_params):
arg_params['stage4_unit1_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['stage4_unit1_offset_weight'])
arg_params['stage4_unit1_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['stage4_unit1_offset_bias'])
arg_params['stage4_unit2_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['stage4_unit2_offset_weight'])
arg_params['stage4_unit2_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['stage4_unit2_offset_bias'])
arg_params['stage4_unit3_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['stage4_unit3_offset_weight'])
arg_params['stage4_unit3_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['stage4_unit3_offset_bias'])
arg_params['rpn_conv_3x3_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['rpn_conv_3x3_weight'])
arg_params['rpn_conv_3x3_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['rpn_conv_3x3_bias'])
arg_params['rpn_cls_score_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['rpn_cls_score_weight'])
arg_params['rpn_cls_score_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['rpn_cls_score_bias'])
arg_params['rpn_bbox_pred_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['rpn_bbox_pred_weight'])
arg_params['rpn_bbox_pred_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['rpn_bbox_pred_bias'])
arg_params['conv_new_1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['conv_new_1_weight'])
arg_params['conv_new_1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['conv_new_1_bias'])
if cfg.TRAIN.AUTO_FOCUS:
arg_params['conv_new_2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['conv_new_2_weight'])
arg_params['conv_new_2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['conv_new_2_bias'])
arg_params['conv_new_3_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['conv_new_3_weight'])
arg_params['conv_new_3_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['conv_new_3_bias'])
arg_params['conv_new_out_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['conv_new_out_weight'])
arg_params['conv_new_out_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['conv_new_out_bias'])
arg_params['offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['offset_weight'])
arg_params['offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['offset_bias'])
arg_params['fc_new_1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_1_weight'])
arg_params['fc_new_1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_1_bias'])
arg_params['fc_new_2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_2_weight'])
arg_params['fc_new_2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_2_bias'])
arg_params['bbox_pred_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['bbox_pred_weight'])
arg_params['bbox_pred_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['bbox_pred_bias'])
arg_params['cls_score_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['cls_score_weight'])
arg_params['cls_score_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['cls_score_bias'])
####### INIT WEIGHTS MASK
self.init_weight_mask(cfg, arg_params, aux_params)
def init_weight_rpn(self, cfg, arg_params, aux_params):
arg_params['stage4_unit1_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['stage4_unit1_offset_weight'])
arg_params['stage4_unit1_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['stage4_unit1_offset_bias'])
arg_params['stage4_unit2_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['stage4_unit2_offset_weight'])
arg_params['stage4_unit2_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['stage4_unit2_offset_bias'])
arg_params['stage4_unit3_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['stage4_unit3_offset_weight'])
arg_params['stage4_unit3_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['stage4_unit3_offset_bias'])
arg_params['rpn_conv_3x3_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['rpn_conv_3x3_weight'])
arg_params['rpn_conv_3x3_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['rpn_conv_3x3_bias'])
arg_params['rpn_cls_score_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['rpn_cls_score_weight'])
arg_params['rpn_cls_score_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['rpn_cls_score_bias'])
arg_params['rpn_bbox_pred_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['rpn_bbox_pred_weight'])
arg_params['rpn_bbox_pred_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['rpn_bbox_pred_bias'])
def init_weight(self, cfg, arg_params, aux_params):
self.init_weight_rcnn(cfg, arg_params, aux_params)