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Saved pre-trained models to S3 with links. Added code for SE ResNeXt

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GeorgeSeif committed Sep 16, 2018
1 parent 791dbc7 commit 4258d0dcfc57af1bb36f0a9166dffaa4192b40c6
Showing with 207 additions and 116 deletions.
  1. +1 −1 builders/frontend_builder.py
  2. +1 −1 builders/model_builder.py
  3. +188 −0 frontends/se_resnext.py
  4. +17 −114 utils/get_pretrained_checkpoints.py
@@ -26,7 +26,7 @@ def build_frontend(inputs, frontend, is_training=True, pretrained_dir="models"):
with slim.arg_scope(mobilenet_v2.training_scope()):
logits, end_points = mobilenet_v2.mobilenet(inputs, is_training=is_training, scope='mobilenet_v2', base_only=True)
frontend_scope='mobilenet_v2'
init_fn = slim.assign_from_checkpoint_fn(model_path=os.path.join(pretrained_dir, 'mobilenet_v2_1.4_224.ckpt'), var_list=slim.get_model_variables('mobilenet_v2'), ignore_missing_vars=True)
init_fn = slim.assign_from_checkpoint_fn(model_path=os.path.join(pretrained_dir, 'mobilenet_v2.ckpt'), var_list=slim.get_model_variables('mobilenet_v2'), ignore_missing_vars=True)
elif frontend == 'InceptionV4':
with slim.arg_scope(inception_v4.inception_v4_arg_scope()):
logits, end_points = inception_v4.inception_v4(inputs, is_training=is_training, scope='inception_v4')
@@ -45,7 +45,7 @@ def build_model(model_name, net_input, num_classes, crop_width, crop_height, fro
download_checkpoints("ResNet101")
if "ResNet152" == frontend and not os.path.isfile("models/resnet_v2_152.ckpt"):
download_checkpoints("ResNet152")
if "MobileNetV2" == frontend and not os.path.isfile("models/mobilenet_v2_1.4_224.ckpt.data-00000-of-00001"):
if "MobileNetV2" == frontend and not os.path.isfile("models/mobilenet_v2.ckpt.data-00000-of-00001"):
download_checkpoints("MobileNetV2")
if "InceptionV4" == frontend and not os.path.isfile("models/inception_v4.ckpt"):
download_checkpoints("InceptionV4")
@@ -0,0 +1,188 @@
import tensorflow as tf
import math
USE_FUSED_BN = True
BN_EPSILON = 9.999999747378752e-06
BN_MOMENTUM = 0.99
VAR_LIST = []
# input image order: BGR, range [0-255]
# mean_value: 104, 117, 123
# only subtract mean is used
def constant_xavier_initializer(shape, group, dtype=tf.float32, uniform=True):
"""Initializer function."""
if not dtype.is_floating:
raise TypeError('Cannot create initializer for non-floating point type.')
# Estimating fan_in and fan_out is not possible to do perfectly, but we try.
# This is the right thing for matrix multiply and convolutions.
if shape:
fan_in = float(shape[-2]) if len(shape) > 1 else float(shape[-1])
fan_out = float(shape[-1])/group
else:
fan_in = 1.0
fan_out = 1.0
for dim in shape[:-2]:
fan_in *= float(dim)
fan_out *= float(dim)
# Average number of inputs and output connections.
n = (fan_in + fan_out) / 2.0
if uniform:
# To get stddev = math.sqrt(factor / n) need to adjust for uniform.
limit = math.sqrt(3.0 * 1.0 / n)
return tf.random_uniform(shape, -limit, limit, dtype, seed=None)
else:
# To get stddev = math.sqrt(factor / n) need to adjust for truncated.
trunc_stddev = math.sqrt(1.3 * 1.0 / n)
return tf.truncated_normal(shape, 0.0, trunc_stddev, dtype, seed=None)
# for root block, use dummy input_filters, e.g. 128 rather than 64 for the first block
def se_bottleneck_block(inputs, input_filters, name_prefix, is_training, group, data_format='channels_last', need_reduce=True, is_root=False, reduced_scale=16):
bn_axis = -1 if data_format == 'channels_last' else 1
strides_to_use = 1
residuals = inputs
if need_reduce:
strides_to_use = 1 if is_root else 2
proj_mapping = tf.layers.conv2d(inputs, input_filters, (1, 1), use_bias=False,
name=name_prefix + '_1x1_proj', strides=(strides_to_use, strides_to_use),
padding='valid', data_format=data_format, activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.zeros_initializer())
residuals = tf.layers.batch_normalization(proj_mapping, momentum=BN_MOMENTUM,
name=name_prefix + '_1x1_proj/bn', axis=bn_axis,
epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN)
reduced_inputs = tf.layers.conv2d(inputs, input_filters // 2, (1, 1), use_bias=False,
name=name_prefix + '_1x1_reduce', strides=(1, 1),
padding='valid', data_format=data_format, activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.zeros_initializer())
reduced_inputs_bn = tf.layers.batch_normalization(reduced_inputs, momentum=BN_MOMENTUM,
name=name_prefix + '_1x1_reduce/bn', axis=bn_axis,
epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN)
reduced_inputs_relu = tf.nn.relu(reduced_inputs_bn, name=name_prefix + '_1x1_reduce/relu')
if data_format == 'channels_first':
reduced_inputs_relu = tf.pad(reduced_inputs_relu, paddings = [[0, 0], [0, 0], [1, 1], [1, 1]])
weight_shape = [3, 3, reduced_inputs_relu.get_shape().as_list()[1]//group, input_filters // 2]
weight_ = tf.Variable(constant_xavier_initializer(weight_shape, group=group, dtype=tf.float32), trainable=is_training, name=name_prefix + '_3x3/kernel')
weight_groups = tf.split(weight_, num_or_size_splits=group, axis=-1, name=name_prefix + '_weight_split')
xs = tf.split(reduced_inputs_relu, num_or_size_splits=group, axis=1, name=name_prefix + '_inputs_split')
else:
reduced_inputs_relu = tf.pad(reduced_inputs_relu, paddings = [[0, 0], [1, 1], [1, 1], [0, 0]])
weight_shape = [3, 3, reduced_inputs_relu.get_shape().as_list()[-1]//group, input_filters // 2]
weight_ = tf.Variable(constant_xavier_initializer(weight_shape, group=group, dtype=tf.float32), trainable=is_training, name=name_prefix + '_3x3/kernel')
weight_groups = tf.split(weight_, num_or_size_splits=group, axis=-1, name=name_prefix + '_weight_split')
xs = tf.split(reduced_inputs_relu, num_or_size_splits=group, axis=-1, name=name_prefix + '_inputs_split')
convolved = [tf.nn.convolution(x, weight, padding='VALID', strides=[strides_to_use, strides_to_use], name=name_prefix + '_group_conv',
data_format=('NCHW' if data_format == 'channels_first' else 'NHWC')) for (x, weight) in zip(xs, weight_groups)]
if data_format == 'channels_first':
conv3_inputs = tf.concat(convolved, axis=1, name=name_prefix + '_concat')
else:
conv3_inputs = tf.concat(convolved, axis=-1, name=name_prefix + '_concat')
conv3_inputs_bn = tf.layers.batch_normalization(conv3_inputs, momentum=BN_MOMENTUM, name=name_prefix + '_3x3/bn',
axis=bn_axis, epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN)
conv3_inputs_relu = tf.nn.relu(conv3_inputs_bn, name=name_prefix + '_3x3/relu')
increase_inputs = tf.layers.conv2d(conv3_inputs_relu, input_filters, (1, 1), use_bias=False,
name=name_prefix + '_1x1_increase', strides=(1, 1),
padding='valid', data_format=data_format, activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.zeros_initializer())
increase_inputs_bn = tf.layers.batch_normalization(increase_inputs, momentum=BN_MOMENTUM,
name=name_prefix + '_1x1_increase/bn', axis=bn_axis,
epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN)
if data_format == 'channels_first':
pooled_inputs = tf.reduce_mean(increase_inputs_bn, [2, 3], name=name_prefix + '_global_pool', keep_dims=True)
else:
pooled_inputs = tf.reduce_mean(increase_inputs_bn, [1, 2], name=name_prefix + '_global_pool', keep_dims=True)
down_inputs = tf.layers.conv2d(pooled_inputs, input_filters // reduced_scale, (1, 1), use_bias=True,
name=name_prefix + '_1x1_down', strides=(1, 1),
padding='valid', data_format=data_format, activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.zeros_initializer())
down_inputs_relu = tf.nn.relu(down_inputs, name=name_prefix + '_1x1_down/relu')
up_inputs = tf.layers.conv2d(down_inputs_relu, input_filters, (1, 1), use_bias=True,
name=name_prefix + '_1x1_up', strides=(1, 1),
padding='valid', data_format=data_format, activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.zeros_initializer())
prob_outputs = tf.nn.sigmoid(up_inputs, name=name_prefix + '_prob')
rescaled_feat = tf.multiply(prob_outputs, increase_inputs_bn, name=name_prefix + '_mul')
pre_act = tf.add(residuals, rescaled_feat, name=name_prefix + '_add')
return tf.nn.relu(pre_act, name=name_prefix + '/relu')
#return tf.nn.relu(residuals + prob_outputs * increase_inputs_bn, name=name_prefix + '/relu')
def se_resnext(input_image, scope, is_training = False, group=16, data_format='channels_last', net_depth=50):
end_points = dict()
bn_axis = -1 if data_format == 'channels_last' else 1
# the input image should in BGR order, note that this is not the common case in Tensorflow
# convert from RGB to BGR
if data_format == 'channels_last':
image_channels = tf.unstack(input_image, axis=-1)
swaped_input_image = tf.stack([image_channels[2], image_channels[1], image_channels[0]], axis=-1)
else:
image_channels = tf.unstack(input_image, axis=1)
swaped_input_image = tf.stack([image_channels[2], image_channels[1], image_channels[0]], axis=1)
#swaped_input_image = input_image
if net_depth not in [50, 101]:
raise TypeError('Only ResNeXt50 or ResNeXt101 are currently supported.')
input_depth = [256, 512, 1024, 2048] # the input depth of the the first block is dummy input
num_units = [3, 4, 6, 3] if net_depth==50 else [3, 4, 23, 3]
block_name_prefix = ['conv2_{}', 'conv3_{}', 'conv4_{}', 'conv5_{}']
if data_format == 'channels_first':
swaped_input_image = tf.pad(swaped_input_image, paddings = [[0, 0], [0, 0], [3, 3], [3, 3]])
else:
swaped_input_image = tf.pad(swaped_input_image, paddings = [[0, 0], [3, 3], [3, 3], [0, 0]])
inputs_features = tf.layers.conv2d(swaped_input_image, input_depth[0]//4, (7, 7), use_bias=False,
name='conv1/7x7_s2', strides=(2, 2),
padding='valid', data_format=data_format, activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.zeros_initializer())
VAR_LIST.append('conv1/7x7_s2')
inputs_features = tf.layers.batch_normalization(inputs_features, momentum=BN_MOMENTUM,
name='conv1/7x7_s2/bn', axis=bn_axis,
epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN)
inputs_features = tf.nn.relu(inputs_features, name='conv1/relu_7x7_s2')
inputs_features = tf.layers.max_pooling2d(inputs_features, [3, 3], [2, 2], padding='same', data_format=data_format, name='pool1/3x3_s2')
is_root = True
for ind, num_unit in enumerate(num_units):
need_reduce = True
for unit_index in range(1, num_unit+1):
inputs_features = se_bottleneck_block(inputs_features, input_depth[ind], block_name_prefix[ind].format(unit_index), is_training=is_training, group=group, data_format=data_format, need_reduce=need_reduce, is_root=is_root)
need_reduce = False
end_points['pool' + str(ind)] = inputs_features
is_root = False
if data_format == 'channels_first':
pooled_inputs = tf.reduce_mean(inputs_features, [2, 3], name='pool5/7x7_s1', keep_dims=True)
else:
pooled_inputs = tf.reduce_mean(inputs_features, [1, 2], name='pool5/7x7_s1', keep_dims=True)
pooled_inputs = tf.layers.flatten(pooled_inputs)
# logits_output = tf.layers.dense(pooled_inputs, num_classes,
# kernel_initializer=tf.contrib.layers.xavier_initializer(),
# bias_initializer=tf.zeros_initializer(), use_bias=True)
logits_output = None
return logits_output, end_points, VAR_LIST
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