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basemodel.py
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basemodel.py
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# -*- coding: utf-8 -*-
# File: basemodel.py
import numpy as np
from contextlib import ExitStack, contextmanager
import tensorflow as tf
from tensorpack.models import BatchNorm, Conv2D, MaxPooling, layer_register
from tensorpack.tfutils import argscope
from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
from tensorpack.tfutils.varreplace import custom_getter_scope, freeze_variables
from config import config as cfg
@layer_register(log_shape=True)
def GroupNorm(x, group=32, gamma_initializer=tf.constant_initializer(1.)):
shape = x.get_shape().as_list()
ndims = len(shape)
assert ndims == 4, shape
chan = shape[1]
assert chan % group == 0, chan
group_size = chan // group
orig_shape = tf.shape(x)
h, w = orig_shape[2], orig_shape[3]
x = tf.reshape(x, tf.stack([-1, group, group_size, h, w]))
mean, var = tf.nn.moments(x, [2, 3, 4], keep_dims=True)
new_shape = [1, group, group_size, 1, 1]
beta = tf.get_variable('beta', [chan], initializer=tf.constant_initializer())
beta = tf.reshape(beta, new_shape)
gamma = tf.get_variable('gamma', [chan], initializer=gamma_initializer)
gamma = tf.reshape(gamma, new_shape)
out = tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-5, name='output')
return tf.reshape(out, orig_shape, name='output')
def freeze_affine_getter(getter, *args, **kwargs):
# custom getter to freeze affine params inside bn
name = args[0] if len(args) else kwargs.get('name')
if name.endswith('/gamma') or name.endswith('/beta'):
kwargs['trainable'] = False
ret = getter(*args, **kwargs)
tf.add_to_collection(tf.GraphKeys.MODEL_VARIABLES, ret)
else:
ret = getter(*args, **kwargs)
return ret
def maybe_reverse_pad(topleft, bottomright):
if cfg.BACKBONE.TF_PAD_MODE:
return [topleft, bottomright]
return [bottomright, topleft]
@contextmanager
def backbone_scope(freeze):
"""
Args:
freeze (bool): whether to freeze all the variables under the scope
"""
def nonlin(x):
x = get_norm()(x)
return tf.nn.relu(x)
with argscope([Conv2D, MaxPooling, BatchNorm], data_format='channels_first'), \
argscope(Conv2D, use_bias=False, activation=nonlin,
kernel_initializer=tf.variance_scaling_initializer(
scale=2.0, mode='fan_out')), \
ExitStack() as stack:
if cfg.BACKBONE.NORM in ['FreezeBN', 'SyncBN']:
if freeze or cfg.BACKBONE.NORM == 'FreezeBN':
stack.enter_context(argscope(BatchNorm, training=False))
else:
stack.enter_context(argscope(
BatchNorm, sync_statistics='nccl' if cfg.TRAINER == 'replicated' else 'horovod'))
if freeze:
stack.enter_context(freeze_variables(stop_gradient=False, skip_collection=True))
else:
# the layers are not completely freezed, but we may want to only freeze the affine
if cfg.BACKBONE.FREEZE_AFFINE:
stack.enter_context(custom_getter_scope(freeze_affine_getter))
yield
def image_preprocess(image, bgr=True):
with tf.name_scope('image_preprocess'):
if image.dtype.base_dtype != tf.float32:
image = tf.cast(image, tf.float32)
mean = cfg.PREPROC.PIXEL_MEAN
std = np.asarray(cfg.PREPROC.PIXEL_STD)
if bgr:
mean = mean[::-1]
std = std[::-1]
image_mean = tf.constant(mean, dtype=tf.float32)
image_invstd = tf.constant(1.0 / std, dtype=tf.float32)
image = (image - image_mean) * image_invstd
return image
def get_norm(zero_init=False):
if cfg.BACKBONE.NORM == 'None':
return lambda x: x
if cfg.BACKBONE.NORM == 'GN':
Norm = GroupNorm
layer_name = 'gn'
else:
Norm = BatchNorm
layer_name = 'bn'
return lambda x: Norm(layer_name, x, gamma_initializer=tf.zeros_initializer() if zero_init else None)
def resnet_shortcut(l, n_out, stride, activation=tf.identity):
n_in = l.shape[1]
if n_in != n_out: # change dimension when channel is not the same
# TF's SAME mode output ceil(x/stride), which is NOT what we want when x is odd and stride is 2
# In FPN mode, the images are pre-padded already.
if not cfg.MODE_FPN and stride == 2:
l = l[:, :, :-1, :-1]
return Conv2D('convshortcut', l, n_out, 1,
strides=stride, activation=activation)
else:
return l
def resnet_bottleneck(l, ch_out, stride):
shortcut = l
if cfg.BACKBONE.STRIDE_1X1:
if stride == 2:
l = l[:, :, :-1, :-1]
l = Conv2D('conv1', l, ch_out, 1, strides=stride)
l = Conv2D('conv2', l, ch_out, 3, strides=1)
else:
l = Conv2D('conv1', l, ch_out, 1, strides=1)
if stride == 2:
l = tf.pad(l, [[0, 0], [0, 0], maybe_reverse_pad(0, 1), maybe_reverse_pad(0, 1)])
l = Conv2D('conv2', l, ch_out, 3, strides=2, padding='VALID')
else:
l = Conv2D('conv2', l, ch_out, 3, strides=stride)
if cfg.BACKBONE.NORM != 'None':
l = Conv2D('conv3', l, ch_out * 4, 1, activation=get_norm(zero_init=True))
else:
l = Conv2D('conv3', l, ch_out * 4, 1, activation=tf.identity,
kernel_initializer=tf.constant_initializer())
ret = l + resnet_shortcut(shortcut, ch_out * 4, stride, activation=get_norm(zero_init=False))
return tf.nn.relu(ret, name='output')
def resnet_group(name, l, block_func, features, count, stride):
with tf.variable_scope(name):
for i in range(0, count):
with tf.variable_scope('block{}'.format(i)):
l = block_func(l, features, stride if i == 0 else 1)
return l
def resnet_c4_backbone(image, num_blocks):
assert len(num_blocks) == 3
freeze_at = cfg.BACKBONE.FREEZE_AT
with backbone_scope(freeze=freeze_at > 0):
l = tf.pad(image, [[0, 0], [0, 0], maybe_reverse_pad(2, 3), maybe_reverse_pad(2, 3)])
l = Conv2D('conv0', l, 64, 7, strides=2, padding='VALID')
l = tf.pad(l, [[0, 0], [0, 0], maybe_reverse_pad(0, 1), maybe_reverse_pad(0, 1)])
l = MaxPooling('pool0', l, 3, strides=2, padding='VALID')
with backbone_scope(freeze=freeze_at > 1):
c2 = resnet_group('group0', l, resnet_bottleneck, 64, num_blocks[0], 1)
with backbone_scope(freeze=False):
c3 = resnet_group('group1', c2, resnet_bottleneck, 128, num_blocks[1], 2)
c4 = resnet_group('group2', c3, resnet_bottleneck, 256, num_blocks[2], 2)
# 16x downsampling up to now
return c4
@auto_reuse_variable_scope
def resnet_conv5(image, num_block):
with backbone_scope(freeze=False):
l = resnet_group('group3', image, resnet_bottleneck, 512, num_block, 2)
return l
def resnet_fpn_backbone(image, num_blocks):
freeze_at = cfg.BACKBONE.FREEZE_AT
shape2d = tf.shape(image)[2:]
mult = float(cfg.FPN.RESOLUTION_REQUIREMENT)
new_shape2d = tf.cast(tf.ceil(tf.cast(shape2d, tf.float32) / mult) * mult, tf.int32)
pad_shape2d = new_shape2d - shape2d
assert len(num_blocks) == 4, num_blocks
with backbone_scope(freeze=freeze_at > 0):
chan = image.shape[1]
pad_base = maybe_reverse_pad(2, 3)
l = tf.pad(image, tf.stack(
[[0, 0], [0, 0],
[pad_base[0], pad_base[1] + pad_shape2d[0]],
[pad_base[0], pad_base[1] + pad_shape2d[1]]]))
l.set_shape([None, chan, None, None])
l = Conv2D('conv0', l, 64, 7, strides=2, padding='VALID')
l = tf.pad(l, [[0, 0], [0, 0], maybe_reverse_pad(0, 1), maybe_reverse_pad(0, 1)])
l = MaxPooling('pool0', l, 3, strides=2, padding='VALID')
with backbone_scope(freeze=freeze_at > 1):
c2 = resnet_group('group0', l, resnet_bottleneck, 64, num_blocks[0], 1)
with backbone_scope(freeze=freeze_at > 2):
c3 = resnet_group('group1', c2, resnet_bottleneck, 128, num_blocks[1], 2)
c4 = resnet_group('group2', c3, resnet_bottleneck, 256, num_blocks[2], 2)
c5 = resnet_group('group3', c4, resnet_bottleneck, 512, num_blocks[3], 2)
# 32x downsampling up to now
# size of c5: ceil(input/32)
return c2, c3, c4, c5