Skip to content
This repository has been archived by the owner on Nov 17, 2023. It is now read-only.

Commit

Permalink
finetune faster-rcnn as caffe
Browse files Browse the repository at this point in the history
  • Loading branch information
Hanqing Huang committed Sep 7, 2017
1 parent 9d1501c commit 33d6db8
Show file tree
Hide file tree
Showing 3 changed files with 54 additions and 40 deletions.
13 changes: 6 additions & 7 deletions example/image-classification/symbols/pvanet.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@ def get_symbol(num_classes, **kwargs):

def crelu(data, num_filter, kernel, stride, pad, name=None, suffix=''):
conv1=mx.symbol.Convolution(data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, name='%s%s_conv2d' %(name, suffix))
bn=mx.symbol.BatchNorm(data=conv1, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True)
bn=mx.symbol.BatchNorm(data=conv1, use_global_stats=True, name='%s%s_batchnorm' %(name, suffix))
negative=mx.symbol.negative(data=bn, name='negative')
concat=mx.symbol.concat(bn, negative)
net=scale_and_shift(concat, num_filter)
Expand All @@ -58,7 +58,7 @@ def res_crelu(data, middle_filter, num_filter, kernel, stride, pad, bsr, proj, n
conv1 = mx.sym.Convolution(data=input, num_filter=middle_filter[0], kernel=(1, 1), stride=stride, pad=(0, 0), name='%s%s_1_con2d' %(name, suffix))
bsr = bn_scale_relu(data=conv1, name=name, suffix='group')
conv2 = mx.sym.Convolution(data=bsr, num_filter=middle_filter[1], kernel=kernel, stride=(1, 1), pad=pad, name='%s%s_2_con2d' %(name, suffix))
bn = mx.sym.BatchNorm(data=conv2, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True)
bn = mx.sym.BatchNorm(data=conv2, use_global_stats=True, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True)
negative = mx.sym.negative(data=bn, name='%s%s_negative'%(name, suffix))
concat = mx.sym.concat(bn, negative)
scale = scale_and_shift(data=concat, name=name, suffix=suffix)
Expand All @@ -68,7 +68,7 @@ def res_crelu(data, middle_filter, num_filter, kernel, stride, pad, bsr, proj, n
return act

def bn_scale_relu(data, name=None, suffix=''):
bn = mx.sym.BatchNorm(data=data, name='%s%s_batchnorm' % (name, suffix), fix_gamma=True)
bn = mx.sym.BatchNorm(data=data, use_global_stats=True, name='%s%s_batchnorm' % (name, suffix))
scale = scale_and_shift(data=bn, name=name, suffix=suffix)
act = mx.sym.Activation(data=scale, act_type='relu', name='%s%s_relu' % (name, suffix))
return act
Expand Down Expand Up @@ -120,21 +120,20 @@ def inception_last(data, middle_filter, num_filter, kernel, stride, proj, name,
else:
conv_concat = mx.sym.concat(conv_a, conv_b2, conv_c3)
conv = mx.sym.Convolution(data=conv_concat, num_filter=num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name='%s_conv' %(name))
bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' % (name, suffix), fix_gamma=True)
bn = mx.sym.BatchNorm(data=conv, use_global_stats=True, name='%s%s_batchnorm' % (name, suffix))
scale = scale_and_shift(data=bn, name=name, suffix='last')
output = scale+shortcut
return output


def Conv(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name=None, suffix=''):
conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))
bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True)
bn = mx.sym.BatchNorm(data=conv, use_global_stats=True, name='%s%s_batchnorm' %(name, suffix))
scale = scale_and_shift(bn, name='%s%s_scale_and_shift' %(name, suffix))
act = mx.sym.Activation(data=scale, act_type='relu', name='%s%s_relu' %(name, suffix))
return act
def fullconnection(data, num_hidden, name, suffix):
fc = mx.sym.FullyConnected(data=data, num_hidden=num_hidden, name='%s' %(name))
bn = mx.sym.BatchNorm(data=fc, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True)
bn = mx.sym.BatchNorm(data=fc, use_global_stats=True, name='%s%s_batchnorm' %(name, suffix))
scale = scale_and_shift(data=bn, name='%s%s_scaleshift' %(name, suffix))
dropout = mx.sym.Dropout(data=scale, name= '%s_dropout' %(name))
relu = mx.sym.Activation(data=dropout, act_type='relu', name='%s_relu' %(name))
Expand Down
75 changes: 44 additions & 31 deletions example/rcnn/rcnn/symbol/symbol_pvanet.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -27,12 +27,12 @@ def get_pvanet_conv(data):
upscale = mx.sym.Deconvolution(data=bsr, num_filter=384, kernel=(4, 4), stride=(2, 2), pad=(1, 1), name='deconv') # (33*20)x384/(66*40)x384
crop = mx.sym.Crop(upscale, downscale, name='crop')
concat = mx.sym.concat(downscale, conv4_4, crop, name='concat') # (66*40)x768/(66*40)x768
convf = mx.sym.Convolution(data=concat, num_filter=512, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name='convf') # (66*40)x768/(66*40)x512
return convf
#convf = mx.sym.Convolution(data=concat, num_filter=512, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name='convf') # (66*40)x768/(66*40)x512
return concat

def crelu(data, num_filter, kernel, stride, pad, name=None, suffix=''):
conv1=mx.symbol.Convolution(data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, name='%s%s_conv2d' %(name, suffix))
bn=mx.symbol.BatchNorm(data=conv1, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True)
bn=mx.symbol.BatchNorm(data=conv1, use_global_stats=True, name='%s%s_batchnorm' %(name, suffix))
negative=mx.symbol.negative(data=bn, name='negative')
concat=mx.symbol.concat(bn, negative)
net=scale_and_shift(concat, num_filter)
Expand All @@ -59,8 +59,8 @@ def res_crelu(data, middle_filter, num_filter, kernel, stride, pad, bsr, proj, n
conv1 = mx.sym.Convolution(data=input, num_filter=middle_filter[0], kernel=(1, 1), stride=stride, pad=(0, 0), name='%s%s_1_con2d' %(name, suffix))
bsr = bn_scale_relu(data=conv1, name=name, suffix='group')
conv2 = mx.sym.Convolution(data=bsr, num_filter=middle_filter[1], kernel=kernel, stride=(1, 1), pad=pad, name='%s%s_2_con2d' %(name, suffix))
bn = mx.sym.BatchNorm(data=conv2, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True)
negative = mx.sym.negative(data=bn, name='%s%s_negative' %(name, suffix))
bn = mx.sym.BatchNorm(data=conv2, use_global_stats=True, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True)
negative = mx.sym.negative(data=bn, name='%s%s_negative'%(name, suffix))
concat = mx.sym.concat(bn, negative)
scale = scale_and_shift(data=concat, name=name, suffix=suffix)
relu = mx.sym.Activation(data=scale, act_type='relu', name='%s%s_relu' %(name, suffix))
Expand All @@ -69,7 +69,7 @@ def res_crelu(data, middle_filter, num_filter, kernel, stride, pad, bsr, proj, n
return act

def bn_scale_relu(data, name=None, suffix=''):
bn = mx.sym.BatchNorm(data=data, name='%s%s_batchnorm' % (name, suffix), fix_gamma=True)
bn = mx.sym.BatchNorm(data=data, use_global_stats=True, name='%s%s_batchnorm' % (name, suffix))
scale = scale_and_shift(data=bn, name=name, suffix=suffix)
act = mx.sym.Activation(data=scale, act_type='relu', name='%s%s_relu' % (name, suffix))
return act
Expand Down Expand Up @@ -120,14 +120,14 @@ def inception_last(data, middle_filter, num_filter, kernel, stride, proj, name,
else:
conv_concat = mx.sym.concat(conv_a, conv_b2, conv_c3)
conv = mx.sym.Convolution(data=conv_concat, num_filter=num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name='%s_conv' %(name))
bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' % (name, suffix), fix_gamma=True)
bn = mx.sym.BatchNorm(data=conv, use_global_stats=True, name='%s%s_batchnorm' % (name, suffix))
scale = scale_and_shift(data=bn, name=name, suffix='last')
output = scale+shortcut
return output

def Conv(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name=None, suffix=''):
conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))
bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True)
bn = mx.sym.BatchNorm(data=conv, use_global_stats=True, name='%s%s_batchnorm' %(name, suffix))
scale = scale_and_shift(bn, name='%s%s_scale_and_shift' %(name, suffix))
act = mx.sym.Activation(data=scale, act_type='relu', name='%s%s_relu' %(name, suffix))
return act
Expand All @@ -143,11 +143,15 @@ def get_pvanet_test(num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHO
im_info = mx.symbol.Variable(name="im_info")

# shared convolutional layers
convf = get_pvanet_conv(data)
slice = mx.sym.slice_axis(data=convf, axis=1, begin=0, end=128, name='slice')
concat = get_pvanet_conv(data)
conf_rpn = mx.symbol.Convolution(data=concat, kernel=(1, 1), pad=(0, 0), num_filter=128, name="conf_rpn")
reluf_rpn = mx.symbol.Activation(data=conf_rpn, act_type="relu", name="reluf_rpn")
conf_2 = mx.symbol.Convolution(data=concat, kernel=(1, 1), pad=(0, 0), num_filter=384, name="conf_2")
reluf_2 = mx.symbol.Activation(data=conf_2, act_type="relu", name="reluf_2")
concat_convf = mx.sym.concat(reluf_rpn, reluf_2)
# RPN
rpn_conv = mx.symbol.Convolution(
data=slice, kernel=(3, 3), pad=(1, 1), num_filter=512, name="rpn_conv_3x3")
data=reluf_rpn, kernel=(3, 3), pad=(1, 1), num_filter=384, name="rpn_conv_3x3")
rpn_relu = mx.symbol.Activation(data=rpn_conv, act_type="relu", name="rpn_relu")
rpn_cls_score = mx.symbol.Convolution(
data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=2 * num_anchors, name="rpn_cls_score")
Expand Down Expand Up @@ -177,21 +181,24 @@ def get_pvanet_test(num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHO

# Fast R-CNN
pool5 = mx.symbol.ROIPooling(
name='roi_pool5', data=convf, rois=rois, pooled_size=(7, 7), spatial_scale=1.0 / config.RCNN_FEAT_STRIDE)
name='roi_pool5', data=concat_convf, rois=rois, pooled_size=(6, 6), spatial_scale=1.0 / config.RCNN_FEAT_STRIDE)
# group 6
flatten = mx.symbol.Flatten(data=pool5, name="flatten")
fc6 = mx.symbol.FullyConnected(data=flatten, num_hidden=4096, name="fc6")
relu6 = mx.symbol.Activation(data=fc6, act_type="relu", name="relu6")
drop6 = mx.symbol.Dropout(data=relu6, p=0.5, name="drop6")
fc6_bn = mx.sym.BatchNorm(data=fc6, name='fc6_batchnorm')
drop6 = mx.symbol.Dropout(data=fc6_bn, p=0.5, name="drop6")
relu6 = mx.symbol.Activation(data=drop6, act_type="relu", name="relu6")
# group 7
fc7 = mx.symbol.FullyConnected(data=drop6, num_hidden=4096, name="fc7")
relu7 = mx.symbol.Activation(data=fc7, act_type="relu", name="relu7")
drop7 = mx.symbol.Dropout(data=relu7, p=0.5, name="drop7")
fc7 = mx.symbol.FullyConnected(data=relu6, num_hidden=4096, name="fc7")
fc7_bn = mx.sym.BatchNorm(data=fc7, name='fc7_batchnorm')
drop7 = mx.symbol.Dropout(data=fc7_bn, p=0.5, name="drop7")
relu7 = mx.symbol.Activation(data=drop7, act_type="relu", name="relu7")

# classification
cls_score = mx.symbol.FullyConnected(name='cls_score', data=drop7, num_hidden=num_classes)
cls_score = mx.symbol.FullyConnected(name='cls_score', data=relu7, num_hidden=num_classes)
cls_prob = mx.symbol.softmax(name='cls_prob', data=cls_score)
# bounding box regression
bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=drop7, num_hidden=num_classes * 4)
bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=relu7, num_hidden=num_classes * 4)

# reshape output
cls_prob = mx.symbol.Reshape(data=cls_prob, shape=(config.TEST.BATCH_IMAGES, -1, num_classes), name='cls_prob_reshape')
Expand All @@ -217,13 +224,16 @@ def get_pvanet_train(num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCH
rpn_bbox_weight = mx.symbol.Variable(name='bbox_weight')

# shared convolutional layers
relu5_3 = get_pvanet_conv(data)

slice = mx.sym.slice_axis(data=relu5_3, axis=1, begin=0, end=128, name='slice')
concat = get_pvanet_conv(data)
conf_rpn = mx.symbol.Convolution(data=concat, kernel=(1, 1), pad=(0, 0), num_filter=128, name="conf_rpn")
reluf_rpn = mx.symbol.Activation(data=conf_rpn, act_type="relu", name="reluf_rpn")
conf_2 = mx.symbol.Convolution(data=concat, kernel=(1, 1), pad=(0, 0), num_filter=384, name="conf_2")
reluf_2 = mx.symbol.Activation(data=conf_2, act_type="relu", name="reluf_2")
concat_convf = mx.sym.concat(reluf_rpn, reluf_2)

# RPN layers
rpn_conv = mx.symbol.Convolution(
data=slice, kernel=(3, 3), pad=(1, 1), num_filter=512, name="rpn_conv_3x3")
data=reluf_rpn, kernel=(3, 3), pad=(1, 1), num_filter=384, name="rpn_conv_3x3")
rpn_relu = mx.symbol.Activation(data=rpn_conv, act_type="relu", name="rpn_relu")
rpn_cls_score = mx.symbol.Convolution(
data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=2 * num_anchors, name="rpn_cls_score")
Expand Down Expand Up @@ -272,21 +282,24 @@ def get_pvanet_train(num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCH

# Fast R-CNN
pool5 = mx.symbol.ROIPooling(
name='roi_pool5', data=relu5_3, rois=rois, pooled_size=(7, 7), spatial_scale=1.0 / config.RCNN_FEAT_STRIDE)
name='roi_pool5', data=concat_convf, rois=rois, pooled_size=(6, 6), spatial_scale=1.0 / config.RCNN_FEAT_STRIDE)
# group 6
flatten = mx.symbol.Flatten(data=pool5, name="flatten")
fc6 = mx.symbol.FullyConnected(data=flatten, num_hidden=4096, name="fc6")
relu6 = mx.symbol.Activation(data=fc6, act_type="relu", name="relu6")
drop6 = mx.symbol.Dropout(data=relu6, p=0.5, name="drop6")
fc6_bn = mx.sym.BatchNorm(data=fc6, name='fc6_batchnorm')
drop6 = mx.symbol.Dropout(data=fc6_bn, p=0.5, name="drop6")
relu6 = mx.symbol.Activation(data=drop6, act_type="relu", name="relu6")
# group 7
fc7 = mx.symbol.FullyConnected(data=drop6, num_hidden=4096, name="fc7")
relu7 = mx.symbol.Activation(data=fc7, act_type="relu", name="relu7")
drop7 = mx.symbol.Dropout(data=relu7, p=0.5, name="drop7")
fc7 = mx.symbol.FullyConnected(data=relu6, num_hidden=4096, name="fc7")
fc7_bn = mx.sym.BatchNorm(data=fc7, name='fc7_batchnorm')
drop7 = mx.symbol.Dropout(data=fc7_bn, p=0.5, name="drop7")
relu7 = mx.symbol.Activation(data=drop7, act_type="relu", name="relu7")

# classification
cls_score = mx.symbol.FullyConnected(name='cls_score', data=drop7, num_hidden=num_classes)
cls_score = mx.symbol.FullyConnected(name='cls_score', data=relu7, num_hidden=num_classes)
cls_prob = mx.symbol.SoftmaxOutput(name='cls_prob', data=cls_score, label=label, normalization='batch')
# bounding box regression
bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=drop7, num_hidden=num_classes * 4)
bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=relu7, num_hidden=num_classes * 4)
bbox_loss_ = bbox_weight * mx.symbol.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=1.0 / config.TRAIN.BATCH_ROIS)

Expand Down
6 changes: 4 additions & 2 deletions example/rcnn/train_end2end.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -99,8 +99,10 @@ def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch,
arg_params['fc6_bias'] = mx.nd.zeros(shape=arg_shape_dict['fc6_bias'])
arg_params['fc7_weight'] = mx.random.normal(0, 0.001, shape=arg_shape_dict['fc7_weight'])
arg_params['fc7_bias'] = mx.nd.zeros(shape=arg_shape_dict['fc7_bias'])
arg_params['convf_weight'] = mx.random.normal(0, 0.001, shape=arg_shape_dict['convf_weight'])
arg_params['convf_bias'] = mx.nd.zeros(shape=arg_shape_dict['convf_bias'])
arg_params['conf_rpn_weight'] = mx.random.normal(0, 0.001, shape=arg_shape_dict['conf_rpn_weight'])
arg_params['conf_rpn_bias'] = mx.nd.zeros(shape=arg_shape_dict['conf_rpn_bias'])
arg_params['conf_2_weight'] = mx.random.normal(0, 0.001, shape=arg_shape_dict['conf_2_weight'])
arg_params['conf_2_bias'] = mx.nd.zeros(shape=arg_shape_dict['conf_2_bias'])

# check parameter shapes
for k in sym.list_arguments():
Expand Down

0 comments on commit 33d6db8

Please sign in to comment.