def inference_resnet(dataset):
dataset_reshaped = tf.reshape(dataset, [-1, 28, 28, 1])
with tf.name_scope("conv1") as scope:
W_conv1 = utils.weight_variable([5, 5, 1, 32], name="W_conv1")
bias1 = utils.bias_variable([32], name="bias1")
tf.histogram_summary("W_conv1", W_conv1)
tf.histogram_summary("bias1", bias1)
h_conv1 = tf.nn.relu(
utils.conv2d_basic(dataset_reshaped, W_conv1, bias1))
h_norm1 = utils.local_response_norm(h_conv1)
bottleneck_1 = utils.bottleneck_unit(h_norm1,
32,
32,
down_stride=True,
name="res1")
bottleneck_2 = utils.bottleneck_unit(bottleneck_1,
64,
64,
down_stride=True,
name="res2")
with tf.name_scope("fc1") as scope:
h_flat = tf.reshape(bottleneck_2, [-1, 7 * 7 * 64])
W_fc1 = utils.weight_variable([7 * 7 * 64, 10], name="W_fc1")
bias_fc1 = utils.bias_variable([10], name="bias_fc1")
tf.histogram_summary("W_fc1", W_fc1)
tf.histogram_summary("bias_fc1", bias_fc1)
logits = tf.matmul(h_flat, W_fc1) + bias_fc1
return logits
def inference_res(input_image):
W1 = utils.weight_variable([3, 3, 3, 32])
b1 = utils.bias_variable([32])
hconv_1 = tf.nn.relu(utils.conv2d_basic(input_image, W1, b1))
h_norm = utils.local_response_norm(hconv_1)
bottleneck_1 = utils.bottleneck_unit(h_norm, 16, 16, down_stride=True, name="res_1")
bottleneck_2 = utils.bottleneck_unit(bottleneck_1, 8, 8, down_stride=True, name="res_2")
bottleneck_3 = utils.bottleneck_unit(bottleneck_2, 16, 16, up_stride=True, name="res_3")
bottleneck_4 = utils.bottleneck_unit(bottleneck_3, 32, 32, up_stride=True, name="res_4")
W5 = utils.weight_variable([3, 3, 32, 3])
b5 = utils.bias_variable([3])
out = tf.nn.tanh(utils.conv2d_basic(bottleneck_4, W5, b5))
return out
def res_net(weights, image):
layers = ('conv1', 'res2a', 'res2b', 'res2c', 'res3a', 'res3b1', 'res3b2',
'res3b3', 'res4a', 'res4b1', 'res4b2', 'res4b3', 'res4b4',
'res4b5', 'res4b6', 'res4b7', 'res4b8', 'res4b9', 'res4b10',
'res4b11', 'res4b12', 'res4b13', 'res4b14', 'res4b15', 'res4b16',
'res4b17', 'res4b18', 'res4b19', 'res4b20', 'res4b21', 'res4b22',
'res5a', 'res5b', 'res5c')
res_branch1_parm = {
#branch1:padding(if 0 then 'VALID' else 'SMAE'),stride
'res2a': [0, 1, 0, 1, 1, 1, 0, 1],
'res3a': [0, 2, 0, 2, 1, 1, 0, 1],
'res4a': [0, 2, 0, 2, 1, 1, 0, 1],
'res5a': [0, 2, 0, 2, 1, 1, 0, 1]
}
net = {}
current = image
n = 0
for i, name in enumerate(layers):
kind = name[:3]
print('-----------------layer: %s-----------------------' % name)
print('Input: ', current.shape)
#convolutional
if kind == 'con':
kernels, bias, n = get_kernel_bias(n, weights, name)
print('kernel size: ', kernels.shape, 'bias size', bias.shape)
# matconvnet: weights are [width, height, in_channels, out_channels]
# tensorflow: weights are [height, width, in_channels, out_channels]
current = utils.conv2d_strided(current, kernels, bias)
current = utils.max_pool_2x2(current, 3)
#resnet
elif kind == 'res':
sub_kind = name[4]
#not blockneck
if sub_kind == 'a':
res_param = res_branch1_parm[name]
branch1_name = '%s_%s' % (name, 'branch1')
branch1_w, branch1_b, out_chan_t, n = get_kernel_bias_res(
n, weights, branch1_name, 2)
print('branch1:kernel size: ', branch1_w.shape, 'bias size',
branch1_b.shape)
else:
res_param = None
branch1_w = None
branch2a_name = '%s_%s' % (name, 'branch2a')
branch2a_w, branch2a_b, out_chan_t2, n = get_kernel_bias_res(
n, weights, branch2a_name, 0)
print('branch2a:kernel size: ', branch2a_w.shape, 'bias size',
branch2a_b.shape)
branch2b_name = '%s_%s' % (name, 'branch2b')
branch2b_w, branch2b_b, _, n = get_kernel_bias_res(
n, weights, branch2b_name, 0)
print('branch2b:kernel size: ', branch2b_w.shape, 'bias size',
branch2b_b.shape)
branch2c_name = '%s_%s' % (name, 'branch2c')
branch2c_w, branch2c_b, out_chan2, n = get_kernel_bias_res(
n, weights, branch2c_name, 3)
print('branch2c:kernel size: ', branch2c_w.shape, 'bias size',
branch2c_b.shape)
if sub_kind == 'a':
out_chan1 = out_chan_t
else:
out_chan1 = out_chan_t2
current = utils.bottleneck_unit(current, res_param, branch1_w,
branch1_b, branch2a_w, branch2a_b,
branch2b_w, branch2b_b, branch2c_w,
branch2c_b, out_chan1, out_chan2,
False, False, name)
print('layer output ', current.shape)
net[name] = current
current = utils.avg_pool(current, 7, 1)
print('resnet final sz ', current.shape)
#return net
return current
