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tf compatibility problems #66

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tf compatibility problems #66

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11f08ef
fix api compatibility issue
TITAN-PyCompat May 9, 2019
77271cc
fix api compatibility issue
TITAN-PyCompat May 9, 2019
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12 changes: 8 additions & 4 deletions libs/box_utils/boxes_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,8 @@
import tensorflow as tf
from libs.box_utils.coordinate_convert import forward_convert

tf_major_ver = int(tf.__version__.split(".")[0])
tf_minor_ver = int(tf.__version__.split(".")[1])

def clip_boxes_to_img_boundaries(decode_boxes, img_shape):
'''
Expand Down Expand Up @@ -88,10 +90,12 @@ def padd_boxes_with_zeros(boxes, scores, max_num_of_boxes):

zero_boxes = tf.zeros(shape=[pad_num, 4], dtype=boxes.dtype)
zero_scores = tf.zeros(shape=[pad_num], dtype=scores.dtype)

final_boxes = tf.concat([boxes, zero_boxes], axis=0)

final_scores = tf.concat([scores, zero_scores], axis=0)
if(tf_major_ver<1):
final_boxes = tf.concat([boxes, zero_boxes], concat_dim=0)
final_scores = tf.concat([scores, zero_scores], concat_dim=0)
else:
final_boxes = tf.concat([boxes, zero_boxes], axis=0)
final_scores = tf.concat([scores, zero_scores], axis=0)

return final_boxes, final_scores

Expand Down
30 changes: 23 additions & 7 deletions libs/box_utils/nms_rotate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,8 @@
if cfgs.ROTATE_NMS_USE_GPU:
from libs.box_utils.rotate_polygon_nms import rotate_gpu_nms

tf_major_ver = int(tf.__version__.split(".")[0])
tf_minor_ver = int(tf.__version__.split(".")[1])

def nms_rotate(decode_boxes, scores, iou_threshold, max_output_size,
use_angle_condition=False, angle_threshold=0, use_gpu=True, gpu_id=0):
Expand All @@ -29,11 +31,16 @@ def nms_rotate(decode_boxes, scores, iou_threshold, max_output_size,
angle_gap_threshold=angle_threshold,
use_angle_condition=use_angle_condition,
device_id=gpu_id)

keep = tf.cond(
tf.greater(tf.shape(keep)[0], max_output_size),
true_fn=lambda: tf.slice(keep, [0], [max_output_size]),
false_fn=lambda: keep)
if(tf_major_ver==1 and tf_minor_ver<2) or (tf_major_ver==0):
keep = tf.cond(
tf.greater(tf.shape(keep)[0], max_output_size),
fn1=lambda: tf.slice(keep, [0], [max_output_size]),
fn2=lambda: keep)
else:
keep = tf.cond(
tf.greater(tf.shape(keep)[0], max_output_size),
true_fn=lambda: tf.slice(keep, [0], [max_output_size]),
false_fn=lambda: keep)

else:
keep = tf.py_func(nms_rotate_cpu,
Expand Down Expand Up @@ -87,15 +94,24 @@ def nms_rotate_gpu(boxes_list, scores, iou_threshold, use_angle_condition=False,
if use_angle_condition:
y_c, x_c, h, w, theta = tf.unstack(boxes_list, axis=1)
boxes_list = tf.transpose(tf.stack([x_c, y_c, w, h, theta]))
det_tensor = tf.concat([boxes_list, tf.expand_dims(scores, axis=1)], axis=1)
if(tf_major_ver<1):
det_tensor = tf.concat([boxes_list, tf.expand_dims(scores, axis=1)], concat_dim=1)
else:
det_tensor = tf.concat([boxes_list, tf.expand_dims(scores, axis=1)], axis=1)


keep = tf.py_func(rotate_gpu_nms,
inp=[det_tensor, iou_threshold, device_id],
Tout=tf.int64)
return keep
else:
y_c, x_c, h, w, theta = tf.unstack(boxes_list, axis=1)
boxes_list = tf.transpose(tf.stack([x_c, y_c, w, h, theta]))
det_tensor = tf.concat([boxes_list, tf.expand_dims(scores, axis=1)], axis=1)
if(tf_major_ver<1):
det_tensor = tf.concat([boxes_list, tf.expand_dims(scores, axis=1)], concat_dim=1)
else:
det_tensor = tf.concat([boxes_list, tf.expand_dims(scores, axis=1)], axis=1)

keep = tf.py_func(rotate_gpu_nms,
inp=[det_tensor, iou_threshold, device_id],
Tout=tf.int64)
Expand Down
35 changes: 25 additions & 10 deletions libs/fast_rcnn/build_fast_rcnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,8 @@
import numpy as np

DEBUG = False

tf_major_ver = int(tf.__version__.split(".")[0])
tf_minor_ver = int(tf.__version__.split(".")[1])

class FastRCNN(object):
def __init__(self,
Expand Down Expand Up @@ -133,9 +134,12 @@ def get_rois(self):
[self.roi_pool_kernel_size, self.roi_pool_kernel_size],
stride=self.roi_pool_kernel_size)
all_level_roi_list.append(level_i_rois)

all_level_rois = tf.concat(all_level_roi_list, axis=0)
all_level_proposals = tf.concat(all_level_proposal_list, axis=0)
if(tf_major_ver<1):
all_level_rois = tf.concat(all_level_roi_list, concat_dim=0)
all_level_proposals = tf.concat(all_level_proposal_list, concat_dim=0)
else:
all_level_rois = tf.concat(all_level_roi_list, axis=0)
all_level_proposals = tf.concat(all_level_proposal_list, axis=0)

return all_level_rois, all_level_proposals

Expand Down Expand Up @@ -219,8 +223,11 @@ def fast_rcnn_minibatch(self, reference_boxes):

negative_indices = tf.random_shuffle(negative_indices)
negative_indices = tf.slice(negative_indices, begin=[0], size=[num_of_negatives])

minibatch_indices = tf.concat([positive_indices, negative_indices], axis=0)

if(tf_major_ver<1):
minibatch_indices = tf.concat([positive_indices, negative_indices], concat_dim=0)
else:
minibatch_indices = tf.concat([positive_indices, negative_indices], axis=0)
minibatch_indices = tf.random_shuffle(minibatch_indices)

minibatch_reference_boxes_mattached_gtboxes = tf.gather(reference_boxes_mattached_gtboxes,
Expand Down Expand Up @@ -259,7 +266,10 @@ def fast_rcnn_loss(self):
tmp_class_weights = tf.ones(shape=[tf.shape(minibatch_encode_boxes)[0], 4], dtype=tf.float32)
tmp_class_weights = tmp_class_weights * tf.expand_dims(category_list[i], axis=1)
class_weights_list.append(tmp_class_weights)
class_weights = tf.concat(class_weights_list, axis=1) # [minibatch_size, num_classes*4]
if(tf_major_ver<1):
class_weights = tf.concat(class_weights_list, concat_dim=1) # [minibatch_size, num_classes*4]
else:
class_weights = tf.concat(class_weights_list, axis=1) # [minibatch_size, num_classes*4]

# loss
with tf.variable_scope('fast_rcnn_classification_loss'):
Expand Down Expand Up @@ -312,10 +322,15 @@ def fast_rcnn_proposals(self, decode_boxes, scores):
tmp_category = tf.gather(category, valid_indices)

category_list.append(tmp_category)
if(tf_major_ver<1):
all_nms_boxes = tf.concat(after_nms_boxes, concat_dim=0)
all_nms_scores = tf.concat(after_nms_scores, concat_dim=0)
all_category = tf.concat(category_list, concat_dim=0)
else:
all_nms_boxes = tf.concat(after_nms_boxes, axis=0)
all_nms_scores = tf.concat(after_nms_scores, axis=0)
all_category = tf.concat(category_list, axis=0)

all_nms_boxes = tf.concat(after_nms_boxes, axis=0)
all_nms_scores = tf.concat(after_nms_scores, axis=0)
all_category = tf.concat(category_list, axis=0)

all_nms_boxes = boxes_utils.clip_boxes_to_img_boundaries(all_nms_boxes,
img_shape=self.img_shape)
Expand Down
51 changes: 36 additions & 15 deletions libs/fast_rcnn/build_fast_rcnn1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,8 @@
from libs.configs import cfgs

DEBUG = False

tf_major_ver = int(tf.__version__.split(".")[0])
tf_minor_ver = int(tf.__version__.split(".")[1])

class FastRCNN(object):
def __init__(self,
Expand Down Expand Up @@ -136,9 +137,12 @@ def get_rois(self):
[self.roi_pool_kernel_size, self.roi_pool_kernel_size],
stride=self.roi_pool_kernel_size)
all_level_roi_list.append(level_i_rois)

all_level_rois = tf.concat(all_level_roi_list, axis=0)
all_level_proposals = tf.concat(all_level_proposal_list, axis=0)
if(tf_major_ver<1):
all_level_rois = tf.concat(all_level_roi_list, concat_dim=0)
all_level_proposals = tf.concat(all_level_proposal_list, concat_dim=0)
else:
all_level_rois = tf.concat(all_level_roi_list, axis=0)
all_level_proposals = tf.concat(all_level_proposal_list, axis=0)
return all_level_rois, all_level_proposals

def fast_rcnn_net(self):
Expand Down Expand Up @@ -240,7 +244,10 @@ def fast_rcnn_minibatch(self, reference_boxes):
negative_indices = tf.random_shuffle(negative_indices)
negative_indices = tf.slice(negative_indices, begin=[0], size=[num_of_negatives])

minibatch_indices = tf.concat([positive_indices, negative_indices], axis=0)
if(tf_major_ver<1):
minibatch_indices = tf.concat([positive_indices, negative_indices], concat_dim=0)
else:
minibatch_indices = tf.concat([positive_indices, negative_indices], axis=0)
minibatch_indices = tf.random_shuffle(minibatch_indices)

minibatch_reference_boxes_mattached_gtboxes = tf.gather(reference_boxes_mattached_gtboxes,
Expand Down Expand Up @@ -298,15 +305,21 @@ def fast_rcnn_loss(self):
tmp_class_weights = tf.ones(shape=[tf.shape(minibatch_encode_boxes)[0], 4], dtype=tf.float32)
tmp_class_weights = tmp_class_weights * tf.expand_dims(category_list[i], axis=1)
class_weights_list.append(tmp_class_weights)
class_weights = tf.concat(class_weights_list, axis=1) # [minibatch_size, num_classes*4]
if(tf_major_ver<1):
class_weights = tf.concat(class_weights_list, concat_dim=1) # [minibatch_size, num_classes*4]
else:
class_weights = tf.concat(class_weights_list, axis=1) # [minibatch_size, num_classes*4]

class_weights_list_rotate = []
category_list_rotate = tf.unstack(minibatch_label_one_hot, axis=1)
for i in range(1, self.num_classes + 1):
tmp_class_weights_rotate = tf.ones(shape=[tf.shape(minibatch_encode_boxes_rotate)[0], 5], dtype=tf.float32)
tmp_class_weights_rotate = tmp_class_weights_rotate * tf.expand_dims(category_list_rotate[i], axis=1)
class_weights_list_rotate.append(tmp_class_weights_rotate)
class_weights_rotate = tf.concat(class_weights_list_rotate, axis=1) # [minibatch_size, num_classes*5]
if(tf_major_ver<1):
class_weights_rotate = tf.concat(class_weights_list_rotate, concat_dim=1) # [minibatch_size, num_classes*5]
else:
class_weights_rotate = tf.concat(class_weights_list_rotate, axis=1) # [minibatch_size, num_classes*5]

# loss
with tf.variable_scope('fast_rcnn_classification_loss'):
Expand Down Expand Up @@ -371,10 +384,14 @@ def fast_rcnn_proposals(self, decode_boxes, scores):
tmp_category = tf.gather(category, valid_indices)

category_list.append(tmp_category)

all_nms_boxes = tf.concat(after_nms_boxes, axis=0)
all_nms_scores = tf.concat(after_nms_scores, axis=0)
all_category = tf.concat(category_list, axis=0)
if(tf_major_ver<1):
all_nms_boxes = tf.concat(after_nms_boxes, concat_dim=0)
all_nms_scores = tf.concat(after_nms_scores, concat_dim=0)
all_category = tf.concat(category_list, concat_dim=0)
else:
all_nms_boxes = tf.concat(after_nms_boxes, axis=0)
all_nms_scores = tf.concat(after_nms_scores, axis=0)
all_category = tf.concat(category_list, axis=0)

all_nms_boxes = boxes_utils.clip_boxes_to_img_boundaries(all_nms_boxes,
img_shape=self.img_shape)
Expand Down Expand Up @@ -429,10 +446,14 @@ def fast_rcnn_proposals_rotate(self, decode_boxes, scores):
tmp_category = tf.gather(category, valid_indices)

category_list.append(tmp_category)

all_nms_boxes = tf.concat(after_nms_boxes, axis=0)
all_nms_scores = tf.concat(after_nms_scores, axis=0)
all_category = tf.concat(category_list, axis=0)
if(tf_major_ver<1):
all_nms_boxes = tf.concat(after_nms_boxes, concat_dim=0)
all_nms_scores = tf.concat(after_nms_scores, concat_dim=0)
all_category = tf.concat(category_list, concat_dim=0)
else:
all_nms_boxes = tf.concat(after_nms_boxes, axis=0)
all_nms_scores = tf.concat(after_nms_scores, axis=0)
all_category = tf.concat(category_list, axis=0)

# all_nms_boxes = boxes_utils.clip_boxes_to_img_boundaries(all_nms_boxes,
# img_shape=self.img_shape)
Expand Down
26 changes: 19 additions & 7 deletions libs/networks/nets/vggnet16.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,8 @@
from libs.configs import cfgs

VGG_MEAN = [103.939, 116.779, 123.68]

tf_major_ver = int(tf.__version__.split(".")[0])
tf_minor_ver = int(tf.__version__.split(".")[1])

class Vgg16:
def __init__(self, vgg16_npy_path=cfgs.VGG16_WEIGHT_PATH):
Expand Down Expand Up @@ -72,18 +73,29 @@ def build(self, rgb, rgb2gbr=False):
if rgb2gbr:
# Convert RGB to BGR
red, green, blue = tf.split(self.color, num_or_size_splits=3, axis=3)
self.color = tf.concat([blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2]], axis=3)
if(tf_major_ver<1):
self.color = tf.concat([blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2]], concat_dim=3)
else:
self.color = tf.concat([blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2]], axis=3)

self.conv1_1 = self.conv_op(input_op=self.color, name="conv1_1", kh=3, kw=3,
n_out=64, dh=1, dw=1)

else:

blue, green, red = tf.split(self.color, num_or_size_splits=3, axis=3)
self.color = tf.concat([blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2]], axis=3)
if(tf_major_ver<1):
self.color = tf.concat([blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2]], concat_dim=3)
else:
self.color = tf.concat([blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2]], axis=3)
self.conv1_1 = self.conv_op(input_op=self.color, name="conv1_1", kh=3, kw=3,
n_out=64, dh=1, dw=1)

Expand Down
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