def augmentation(h, test, aug):
if aug is None:
aug = not test
if aug:
h = F.image_augmentation(h, (1, 28, 28), (0, 0), 0.9, 1.1, 0.3, 1.3,
0.1, False, False, 0.5, False, 1.5, 0.5,
False, 0.1, 0)
return h
def image_augmentation(image):
h = F.image_augmentation(image,
shape=image.shape,
min_scale=1.0,
max_scale=286.0/256.0, # == 1.1171875
flip_lr=True)
h.persistent = True
return h
def image_augmentation(image):
return F.image_augmentation(
image,
shape=image.shape,
min_scale=1.0,
max_scale=286.0 / 256.0, # == 1.1171875
flip_lr=True,
seed=rng_seed)
def vectorizer(x, maxh=256, test=False, output_hidden=False):
"""
Building discriminator network which maps a (B, 1, 28, 28) input to
a (B, 100).
"""
# Define shortcut functions
def bn(xx):
# Batch normalization
return PF.batch_normalization(xx, batch_stat=not test)
def downsample2(xx, c):
return PF.convolution(xx,
c, (3, 3),
pad=(1, 1),
stride=(2, 2),
with_bias=False)
assert maxh / 8 > 0
with nn.parameter_scope("dis"):
# (1, 28, 28) --> (32, 16, 16)
if not test:
x_ = F.image_augmentation(x, min_scale=0.9, max_scale=1.08)
x2 = F.random_shift(x_, (2, 2))
with nn.parameter_scope("conv1"):
c1 = F.elu(
bn(
PF.convolution(x2,
maxh / 8, (3, 3),
pad=(3, 3),
stride=(2, 2),
with_bias=False)))
else:
with nn.parameter_scope("conv1"):
c1 = F.elu(
bn(
PF.convolution(x,
maxh / 8, (3, 3),
pad=(3, 3),
stride=(2, 2),
with_bias=False)))
# (32, 16, 16) --> (64, 8, 8)
with nn.parameter_scope("conv2"):
c2 = F.elu(bn(downsample2(c1, maxh / 4)))
# (64, 8, 8) --> (128, 4, 4)
with nn.parameter_scope("conv3"):
c3 = F.elu(bn(downsample2(c2, maxh / 2)))
# (128, 4, 4) --> (256, 4, 4)
with nn.parameter_scope("conv4"):
c4 = bn(
PF.convolution(c3, maxh, (3, 3), pad=(1, 1), with_bias=False))
# (256, 4, 4) --> (1,)
with nn.parameter_scope("fc1"):
f = PF.affine(c4, 100)
if output_hidden:
return f, [c1, c2, c3, c4]
return f
def test_image_augmentation_forward(seed, shape, ctx, func_name):
rng = np.random.RandomState(seed)
inputs = [rng.randn(16, 3, 8, 8).astype(np.float32)]
i = nn.Variable(inputs[0].shape)
# NNabla forward
with nn.context_scope(ctx), nn.auto_forward():
o = F.image_augmentation(i)
assert o.d.shape == inputs[0].shape
with nn.context_scope(ctx), nn.auto_forward():
o = F.image_augmentation(i, shape=shape, pad=(2, 2),
min_scale=0.8, max_scale=1.2, angle=0.2,
aspect_ratio=1.1, distortion=0.1,
flip_lr=True, flip_ud=False,
brightness=0.1, brightness_each=True,
contrast=1.1, contrast_center=0.5, contrast_each=True,
noise=0.1, seed=0)
assert o.d.shape == (inputs[0].shape[0],) + shape
def construct_networks(args, ops, arch_dict, image, test):
"""
Construct a network by stacking cells.
input:
args: arguments set by user.
ops: operations used in the network.
arch_dict: a dictionary containing architecture information.
image: Variable. Input images.
test: bool. True if the network is for validation.
"""
num_of_cells = args.num_cells
initial_output_filter = args.output_filter + args.additional_filters_on_retrain
num_class = 10
aux_logits = None
if not test:
image = F.random_crop(F.pad(image, (4, 4, 4, 4)), shape=(image.shape))
image = F.image_augmentation(image, flip_lr=True)
image.need_grad = False
x = image
with nn.parameter_scope("stem_conv1"):
stem_1 = PF.convolution(x,
initial_output_filter, (3, 3), (1, 1),
with_bias=False)
stem_1 = PF.batch_normalization(stem_1, batch_stat=not test)
cell_prev, cell_prev_prev = stem_1, stem_1
output_filter = initial_output_filter
is_reduced_curr, is_reduced_prev = False, False
for i in range(num_of_cells):
if i in [num_of_cells // 3, 2 * num_of_cells // 3]:
output_filter = 2 * output_filter
is_reduced_curr = True
else:
is_reduced_curr = False
y, is_reduced_curr, is_reduced_prev, output_filter = \
constructing_learned_cell(args, ops, arch_dict, i,
cell_prev_prev, cell_prev, output_filter,
is_reduced_curr, is_reduced_prev, test)
if i == 2 * num_of_cells // 3 and args.auxiliary and not test:
print("Using Aux Tower after cell_{}".format(i))
aux_logits = construct_aux_head(y, num_class)
cell_prev, cell_prev_prev = y, cell_prev # shifting
y = F.average_pooling(y, y.shape[2:]) # works as global average pooling
with nn.parameter_scope("fc"):
pred = PF.affine(y, num_class, with_bias=True)
return pred, aux_logits
def test_image_augmentation_forward(seed, ctx, func_name):
rng = np.random.RandomState(seed)
inputs = [rng.randn(16, 3, 8, 8).astype(np.float32)]
i = nn.Variable(inputs[0].shape)
# NNabla forward
with nn.context_scope(ctx), nn.auto_forward():
o = F.image_augmentation(i)
assert o.d.shape == inputs[0].shape
shape = (3, 5, 8)
with nn.context_scope(ctx), nn.auto_forward():
o = F.image_augmentation(i, shape=shape, pad=(2, 2),
min_scale=0.8, max_scale=1.2, angle=0.2,
aspect_ratio=1.1, distortion=0.1,
flip_lr=True, flip_ud=False,
brightness=0.1, brightness_each=True,
contrast=1.1, contrast_center=0.5, contrast_each=True,
noise=0.1, seed=0)
assert o.d.shape == (inputs[0].shape[0],) + shape
def resnet23_prediction(image, test=False, rng=None, ncls=10, nmaps=64, act=F.relu):
"""
Construct ResNet 23
"""
# Residual Unit
def res_unit(x, scope_name, rng, dn=False):
C = x.shape[1]
with nn.parameter_scope(scope_name):
# Conv -> BN -> Nonlinear
with nn.parameter_scope("conv1"):
h = PF.convolution(x, C / 2, kernel=(1, 1), pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = act(h)
# Conv -> BN -> Nonlinear
with nn.parameter_scope("conv2"):
h = PF.convolution(h, C / 2, kernel=(3, 3), pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = act(h)
# Conv -> BN
with nn.parameter_scope("conv3"):
h = PF.convolution(h, C, kernel=(1, 1), pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
# Residual -> Nonlinear
h = act(F.add2(h, x, inplace=True))
# Maxpooling
if dn:
h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
return h
# Conv -> BN -> Nonlinear
with nn.parameter_scope("conv1"):
# Preprocess
if not test:
image = F.image_augmentation(image, contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
h = PF.convolution(image, nmaps, kernel=(3, 3),
pad=(1, 1), with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = act(h)
h = res_unit(h, "conv2", rng, False) # -> 32x32
h = res_unit(h, "conv3", rng, True) # -> 16x16
h = res_unit(h, "conv4", rng, False) # -> 16x16
h = res_unit(h, "conv5", rng, True) # -> 8x8
h = res_unit(h, "conv6", rng, False) # -> 8x8
h = res_unit(h, "conv7", rng, True) # -> 4x4
h = res_unit(h, "conv8", rng, False) # -> 4x4
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
pred = PF.affine(h, ncls, rng=rng)
return pred
def test_image_augmentation_forward(seed, shape, ctx, func_name):
rng = np.random.RandomState(seed)
inputs = [rng.randn(16, 3, 8, 8).astype(np.float32)]
i = nn.Variable(inputs[0].shape)
# NNabla forward
with nn.context_scope(ctx), nn.auto_forward():
o = F.image_augmentation(i)
assert o.d.shape == inputs[0].shape
func_kargs = {
'shape': shape,
'pad': (2, 2),
'min_scale': 0.8,
'max_scale': 1.2,
'angle': 0.2,
'aspect_ratio': 1.1,
'distortion': 0.1,
'flip_lr': True,
'flip_ud': False,
'brightness': 0.1,
'brightness_each': True,
'contrast': 1.1,
'contrast_center': 0.5,
'contrast_each': True,
'noise': 0.1,
'seed': 0}
with nn.context_scope(ctx), nn.auto_forward():
o = F.image_augmentation(i, **func_kargs)
assert o.d.shape == (inputs[0].shape[0],) + shape
# Checking recomputation
from nbla_test_utils import recomputation_test
recomputation_test(rng=rng, func=F.image_augmentation, vinputs=[i],
func_args=[], func_kwargs=func_kargs, ctx=ctx)
func_kargs['seed'] = -1
recomputation_test(rng=rng, func=F.image_augmentation, vinputs=[i],
func_args=[], func_kwargs=func_kargs, ctx=ctx)
def cnn_model_003(ctx, h, act=F.elu, do=True, test=False):
with nn.context_scope(ctx):
if not test:
b, c, s, s = h.shape
h = F.image_augmentation(h, (c, s, s),
min_scale=1.0, max_scale=1.5,
angle=0.5, aspect_ratio=1.3, distortion=0.2,
flip_lr=True)
# Convblock0
h = conv_unit(h, "conv00", 128, k=3, s=1, p=1, act=act, test=test)
h = conv_unit(h, "conv01", 128, k=3, s=1, p=1, act=act, test=test)
h = conv_unit(h, "conv02", 128, k=3, s=1, p=1, act=act, test=test)
h = F.max_pooling(h, (2, 2)) # 32 -> 16
with nn.parameter_scope("bn0"):
h = PF.batch_normalization(h, batch_stat=not test)
if not test and do:
h = F.dropout(h)
# Convblock 1
h = conv_unit(h, "conv10", 256, k=3, s=1, p=1, act=act, test=test)
h = conv_unit(h, "conv11", 256, k=3, s=1, p=1, act=act, test=test)
h = conv_unit(h, "conv12", 256, k=3, s=1, p=1, act=act, test=test)
h = F.max_pooling(h, (2, 2)) # 16 -> 8
with nn.parameter_scope("bn1"):
h = PF.batch_normalization(h, batch_stat=not test)
if not test and do:
h = F.dropout(h)
# Convblock 2
h = conv_unit(h, "conv20", 512, k=3, s=1, p=0, act=act, test=test) # 8 -> 6
h = conv_unit(h, "conv21", 256, k=1, s=1, p=0, act=act, test=test)
h = conv_unit(h, "conv22", 128, k=1, s=1, p=0, act=act, test=test)
u = h
# Convblock 3
h = conv_unit(h, "conv23", 10, k=1, s=1, p=0, act=act, test=test)
h = F.average_pooling(h, (6, 6))
with nn.parameter_scope("bn2"):
h = PF.batch_normalization(h, batch_stat=not test)
pred = F.reshape(h, (h.shape[0], np.prod(h.shape[1:])))
# Uncertainty
u = conv_unit(u, "u0", 10, k=1, s=1, p=0, act=act, test=test)
u = F.average_pooling(u, (6, 6))
with nn.parameter_scope("u0bn"):
u = PF.batch_normalization(u, batch_stat=not test)
log_var = F.reshape(u, (u.shape[0], np.prod(u.shape[1:])))
return pred, log_var
def image_preprocess(image, img_size=224, data_size=320, test=False):
h, w = image.shape[2:]
image = image / 255.0
if test:
_img_size = data_size * 0.875 # Ratio of size is 87.5%
hs = (h - _img_size) / 2
ws = (w - _img_size) / 2
he = (h + _img_size) / 2
we = (w + _img_size) / 2
image = F.slice(image, (0, ws, hs), (3, we, he), (1, 1, 1))
image = F.image_augmentation(image, (3, img_size, img_size),
min_scale=0.8,
max_scale=0.8)
else:
size = min(h, w)
min_size = img_size * 1.1
max_size = min_size * 2
min_scale = min_size / size
max_scale = max_size / size
image = F.image_augmentation(image, (3, img_size, img_size),
pad=(0, 0),
min_scale=min_scale,
max_scale=max_scale,
angle=0.5,
aspect_ratio=1.3,
distortion=0.2,
flip_lr=True,
flip_ud=False,
brightness=0.0,
brightness_each=True,
contrast=1.1,
contrast_center=0.5,
contrast_each=True,
noise=0.0)
image = image - 0.5
return image
def image_preprocess(image, img_size=224):
h, w = image.shape[2:]
size = min(h, w)
min_size = img_size * 1.1
max_size = min_size * 2
min_scale = min_size / size
max_scale = max_size / size
image = F.image_augmentation(image, (3, img_size, img_size),
min_scale=min_scale, max_scale=max_scale,
angle=0.5, aspect_ratio=1.3, distortion=0.2,
flip_lr=True, brightness=25.5,
brightness_each=True,
contrast=1.1, contrast_center=128.0,
contrast_each=True, noise=25.5)
image = image - 128
return image
def image_preprocess(image, img_size=224):
h, w = image.shape[2:]
size = min(h, w)
min_size = img_size * 1.1
max_size = min_size * 2
min_scale = min_size / size
max_scale = max_size / size
image = F.image_augmentation(image, (3, img_size, img_size),
min_scale=min_scale, max_scale=max_scale,
angle=0.5, aspect_ratio=1.3, distortion=0.2,
flip_lr=True, brightness=25.5,
brightness_each=True,
contrast=1.1, contrast_center=128.0,
contrast_each=True, noise=25.5)
image = image - 128
return image
def cifar10_resnet2rnn_prediction(image,
maps=64,
unrolls=[3, 3, 4],
res_unit=res_unit_default,
test=False):
"""
Construct ResNet 23 with depth-wise convolution.
References
----------
Qianli Liao and Tomaso Poggio,
"Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex",
https://arxiv.org/abs/1604.03640
"""
ncls = 10
# Conv -> BN -> Relu
with nn.parameter_scope("conv1"):
# Preprocess
image /= 255.0
if not test:
image = F.image_augmentation(image,
contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
h = PF.convolution(image,
maps,
kernel=(3, 3),
pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
# ResUnit2RNN
for i, u in enumerate(unrolls):
for n in range(u):
h = res_unit(h, "block{}".format(i), n, test)
h = F.average_pooling(h, kernel=h.shape[-2:])
pred = PF.affine(h, ncls)
return pred
def construct_architecture(image, num_class, num_cells, num_nodes, both_archs, output_filter, test):
"""
Construct an architecture based on the given lists.
Note that first 2 layers are stem conv and have nothing to do with node operations.
"""
conv_arch, reduc_arch = both_archs
aux_logits = None
used_weights = set()
pool_distance = num_cells // 3
pool_layers = [pool_distance - 1, 2*pool_distance - 1]
pool_layers = [_ for _ in pool_layers if _ > 0]
if len(pool_layers) > 0:
aux_head_indices = [pool_layers[-1] + 1]
else:
# this must not be happened. since num_cells needs to be more than 3.
aux_head_indices = [1]
ref_groups, required_indices = get_reference_layers(num_cells, pool_layers)
prev_layers = [list() for _ in range(ref_groups[-1] + 1)]
# Note that this implementation is slightly different from the one written by tensorflow.
if not test:
image = F.image_augmentation(
image, angle=0.25, flip_lr=True) # random_crop, min_scale
image.need_grad = False
x = image
# --------------------------------------- 1st cell ---------------------------------------
with nn.parameter_scope("stem_conv1"):
x = PF.convolution(x, output_filter, (3, 3), (1, 1), with_bias=False)
x = PF.batch_normalization(x, batch_stat=not test)
used_weights.update(
{"stem_conv1/conv/W", "stem_conv1/bn/gamma", "stem_conv1/bn/beta"})
prev_layers[0].append(x) # store to the "unpooled" layer
# spatial reduction (this might be skipped)
for i in range(1, len(required_indices[0])):
curr_scope = "stem1_reduc{}".format(i)
x = factorized_reduction(x, 2*x.shape[1], curr_scope, test)
local_used_weights = get_factorized_weights_name(curr_scope)
used_weights.update(local_used_weights)
prev_layers[i].append(x)
# --------------------------------------- 2nd cell ---------------------------------------
with nn.parameter_scope("stem_conv2"):
x = PF.convolution(
prev_layers[0][-1], output_filter, (3, 3), (1, 1), with_bias=False)
x = PF.batch_normalization(x, batch_stat=not test)
used_weights.update(
{"stem_conv2/conv/W", "stem_conv2/bn/gamma", "stem_conv2/bn/beta"})
prev_layers[0].append(x) # store to the "unpooled" layer
# spatial reduction (this might be skipped)
for i in range(1, len(required_indices[1])):
curr_scope = "stem2_reduc{}".format(i)
x = factorized_reduction(x, 2*x.shape[1], curr_scope, test)
local_used_weights = get_factorized_weights_name(curr_scope)
used_weights.update(local_used_weights)
prev_layers[i].append(x)
# ------------------------------- Normal / Reduction cells -------------------------------
for layer_id in range(2, num_cells):
using_layer_index = ref_groups[layer_id]
required_index = list(required_indices[layer_id])
required_index.sort()
scope = 'w{}'.format(layer_id)
if layer_id in pool_layers:
architecture = reduc_arch
else:
architecture = conv_arch
previous_outputs = prev_layers[using_layer_index]
x, local_used_weights = construct_cell(
previous_outputs, architecture, num_nodes, previous_outputs[-1].shape[1], scope, test)
used_weights.update(local_used_weights)
prev_layers[using_layer_index].append(x)
required_index.remove(using_layer_index) # discard an index used above
# if this output (x) is reused as an input in other cells and
# its shape needs to be changed, apply downsampling in advance
for i in required_index:
curr_scope = "scope{0}_reduc{1}".format(layer_id, i)
x = factorized_reduction(x, 2*x.shape[1], curr_scope, test)
local_used_weights = get_factorized_weights_name(curr_scope)
used_weights.update(local_used_weights)
prev_layers[i].append(x)
# auxiliary head, to use the intermediate output for training
if layer_id in aux_head_indices and not test:
print("Using aux_head at layer {}".format(layer_id))
aux_logits = F.relu(x)
aux_logits = F.average_pooling(aux_logits, (5, 5), (3, 3))
with nn.parameter_scope("proj"):
aux_logits = PF.convolution(
aux_logits, 128, (3, 3), (1, 1), with_bias=False)
aux_logits = PF.batch_normalization(
aux_logits, batch_stat=not test)
aux_logits = F.relu(aux_logits)
used_weights.update(
{"proj/conv/W", "proj/bn/gamma", "proj/bn/beta"})
with nn.parameter_scope("avg_pool"):
aux_logits = PF.convolution(
aux_logits, 768, (3, 3), (1, 1), with_bias=False)
aux_logits = PF.batch_normalization(
aux_logits, batch_stat=not test)
aux_logits = F.relu(aux_logits)
used_weights.update(
{"avg_pool/conv/W", "avg_pool/bn/gamma", "avg_pool/bn/beta"})
with nn.parameter_scope("additional_fc"):
aux_logits = F.global_average_pooling(aux_logits)
aux_logits = PF.affine(aux_logits, num_class, with_bias=False)
used_weights.update({"additional_fc/affine/W"})
x = F.global_average_pooling(prev_layers[-1][-1])
if not test:
dropout_rate = 0.5
x = F.dropout(x, dropout_rate)
with nn.parameter_scope("fc"):
pred = PF.affine(x, num_class, with_bias=False)
used_weights.add("fc/affine/W")
return pred, aux_logits, used_weights
def construct_architecture(image, num_class, operations, output_filter, test,
connect_patterns):
"""
Architecture Construction.
"""
ops = {
0: conv3x3,
1: conv5x5,
2: depthwise_separable_conv3x3,
3: depthwise_separable_conv5x5,
4: max_pool,
5: average_pool
}
used_weights = set()
pool_distance = len(operations) // 3
pool_layers = [pool_distance - 1, 2 * pool_distance - 1]
# exclude negative indices
pool_layers = [idx for idx in pool_layers if idx > 0]
ref_groups = len(operations) * [0]
tmp_list = pool_layers + [len(operations) - 1]
index = 0
for n in range(len(operations)):
if n <= tmp_list[index]:
ref_groups[n] = index
else:
index += 1
ref_groups[n] = index
# elements in ref_groups tell you how many times you need to do pooling.
# e.g. [0, 0, 0, 1, 1, 1, ..., 2] : the 1st layer needs no pooling,
# but the last needs 2 poolings, to get spatially reduced variables.
#required_indices = get_requirement_soft(ref_groups)
required_indices = get_requirement_strict(ref_groups, connect_patterns,
pool_layers)
num_of_pooling = len(pool_layers)
normal_layers = [list()]
pooled_layers = [list() for j in range(num_of_pooling)]
prev_layers = normal_layers + pooled_layers
# prev_layer consists of: [[initial_size_layers], [1x pooled_layers], [2x pooled_layers], ...]
if not test:
image = F.image_augmentation(image, angle=0.25, flip_lr=True)
image.need_grad = False
x = image
# next comes the basic operation. for the first layer,
# just apply a convolution (to make the size of the input the same as that of successors)
with nn.parameter_scope("stem_conv"):
x = PF.convolution(x, output_filter, (3, 3), (1, 1), with_bias=False)
x = PF.batch_normalization(x, batch_stat=not test)
used_weights.update(
{"stem_conv/conv/W", "stem_conv/bn/gamma", "stem_conv/bn/beta"})
prev_layers[0].append(x)
# "unpooled" variable is stored in normal_layers (prev_layers[0]).
# then apply factorized reduction (kind of pooling),
# but ONLY IF the spatially-reduced variable is required.
# for example, when this layer has skip connection with latter layers.
for j in range(1, len(prev_layers)):
if required_indices[0][j]:
nested_scope = "stem_pool_{}".format(j)
reduced_var = factorized_reduction(prev_layers[j - 1][-1],
output_filter, nested_scope,
test)
used_weights.update(get_factorized_weights_name(nested_scope))
else:
# dummy variable. Should never be used.
reduced_var = nn.Variable([1, 1, 1, 1])
prev_layers[j].append(reduced_var)
# reduced (or "pooled") variable is stored in pooled_layers (prev_layers[1:]).
# basically, repeat the same process, for whole layers.
for i, elem in enumerate(operations):
scope = 'w{}_{}'.format(i, elem)
# basic operation (and connects it with previous layers if it has skip connections)
using_layer_index = ref_groups[i]
connect_pattern = connect_patterns[i]
x, local_used_weights = apply_ops_and_connect(
prev_layers[using_layer_index][-1], prev_layers[using_layer_index],
connect_pattern, ops, elem, output_filter, scope, test)
used_weights.update(local_used_weights)
prev_layers[using_layer_index].append(x)
# factorized reduction
for j in range(using_layer_index + 1, len(prev_layers)):
if required_indices[i + 1][j]:
nested_scope = "{0}_pool{1}".format(scope, j)
reduced_var = factorized_reduction(prev_layers[j - 1][-1],
output_filter, nested_scope,
test)
used_weights.update(get_factorized_weights_name(nested_scope))
else:
reduced_var = nn.Variable([1, 1, 1, 1]) # dummy variable.
prev_layers[j].append(reduced_var)
x = F.global_average_pooling(x)
if not test:
dropout_rate = 0.5
x = F.dropout(x, dropout_rate)
with nn.parameter_scope("fc"):
pred = PF.affine(x, num_class, with_bias=False)
used_weights.add("fc/affine/W")
return pred, used_weights
def resnet56_prediction(image,
test=False,
ncls=10,
nmaps=64,
act=F.relu,
seed=0):
"""
Construct ResNet 56
"""
channels = [16, 32, 64]
# Residual Unit
def res_unit(x, scope_name, c, i):
subsampling = i == 0 and c > 16
strides = (2, 2) if subsampling else (1, 1)
with nn.parameter_scope(scope_name):
# Conv -> BN -> Nonlinear
with nn.parameter_scope("conv1"):
h = PF.convolution(x,
c,
kernel=(3, 3),
pad=(1, 1),
stride=strides,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = act(h)
# Conv -> BN -> Nonlinear
with nn.parameter_scope("conv2"):
h = PF.convolution(h,
c,
kernel=(3, 3),
pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
if subsampling:
# Conv -> BN
with nn.parameter_scope("conv3"):
x = PF.convolution(x,
c,
kernel=(1, 1),
pad=(0, 0),
stride=(2, 2),
with_bias=False)
# Residual -> Nonlinear
h = act(F.add2(h, x))
return h
# Conv -> BN -> Nonlinear
with nn.parameter_scope("conv1"):
# Preprocess
if not test:
image = F.image_augmentation(image,
min_scale=0.8,
max_scale=1.2,
flip_lr=True,
seed=seed)
image.need_grad = False
h = PF.convolution(image,
channels[0],
kernel=(3, 3),
pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = act(h)
for c in channels:
h = res_unit(h, f"{c}_conv2", c, 0)
h = res_unit(h, f"{c}_conv3", c, 1)
h = res_unit(h, f"{c}_conv4", c, 2)
h = res_unit(h, f"{c}_conv5", c, 3)
h = res_unit(h, f"{c}_conv6", c, 4)
h = res_unit(h, f"{c}_conv7", c, 5)
h = res_unit(h, f"{c}_conv8", c, 6)
h = res_unit(h, f"{c}_conv9", c, 7)
h = res_unit(h, f"{c}_conv10", c, 8)
h = F.global_average_pooling(h) # -> 1x1
if test:
h.need_grad = False
pred = PF.affine(h, ncls)
return pred, h
def __call__(self, x, test=False):
h = x if test else F.image_augmentation(x, flip_lr=True, angle=0.26)
h = self.model0(h, test)
h = self.model1(h, test)
return h
def cifar10_resnet23_prediction(image, ctx, test=False):
"""
Construct ResNet 23
"""
# Residual Unit
def res_unit(x, scope_name, rng, dn=False, test=False):
C = x.shape[1]
with nn.parameter_scope(scope_name):
# Conv -> BN -> Relu
with nn.parameter_scope("conv1"):
w_init = UniformInitializer(calc_uniform_lim_glorot(
C, C / 2, kernel=(1, 1)),
rng=rng)
h = PF.convolution(x,
C / 2,
kernel=(1, 1),
pad=(0, 0),
w_init=w_init,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
# Conv -> BN -> Relu
with nn.parameter_scope("conv2"):
w_init = UniformInitializer(calc_uniform_lim_glorot(
C / 2, C / 2, kernel=(3, 3)),
rng=rng)
h = PF.convolution(h,
C / 2,
kernel=(3, 3),
pad=(1, 1),
w_init=w_init,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
# Conv -> BN
with nn.parameter_scope("conv3"):
w_init = UniformInitializer(calc_uniform_lim_glorot(
C / 2, C, kernel=(1, 1)),
rng=rng)
h = PF.convolution(h,
C,
kernel=(1, 1),
pad=(0, 0),
w_init=w_init,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
# Residual -> Relu
h = F.relu(h + x)
# Maxpooling
if dn:
h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
return h
# Random generator for using the same init parameters in all devices
rng = np.random.RandomState(0)
nmaps = 64
ncls = 10
# Conv -> BN -> Relu
with nn.context_scope(ctx):
with nn.parameter_scope("conv1"):
# Preprocess
if not test:
image = F.image_augmentation(image,
contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
w_init = UniformInitializer(calc_uniform_lim_glorot(3,
nmaps,
kernel=(3, 3)),
rng=rng)
h = PF.convolution(image,
nmaps,
kernel=(3, 3),
pad=(1, 1),
w_init=w_init,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
h = res_unit(h, "conv2", rng, False) # -> 32x32
h = res_unit(h, "conv3", rng, True) # -> 16x16
h = res_unit(h, "conv4", rng, False) # -> 16x16
h = res_unit(h, "conv5", rng, True) # -> 8x8
h = res_unit(h, "conv6", rng, False) # -> 8x8
h = res_unit(h, "conv7", rng, True) # -> 4x4
h = res_unit(h, "conv8", rng, False) # -> 4x4
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
w_init = UniformInitializer(calc_uniform_lim_glorot(int(
np.prod(h.shape[1:])),
ncls,
kernel=(1, 1)),
rng=rng)
pred = PF.affine(h, ncls, w_init=w_init)
return pred
def cifar10_shift_prediction(image, maps=64, test=False, p=0, module="sc2"):
"""
Construct ShiftNet
"""
# Shift
def shift(x, ksize=3):
maps = x.shape[1]
cpg = maps // (ksize**2)
x_pad = F.pad(x, (1, 1, 1, 1))
b, c, h, w = x_pad.shape
xs = []
# Bottom shift
i = 0
xs += [x_pad[:, i * cpg:(i + 1) * cpg, :h - 2, 1:w - 1]]
# Top shift
i = 1
xs += [x_pad[:, i * cpg:(i + 1) * cpg, 2:, 1:w - 1]]
# Right shift
i = 2
xs += [x_pad[:, i * cpg:(i + 1) * cpg, 1:h - 1, :w - 2]]
# Left shift
i = 3
xs += [x_pad[:, i * cpg:(i + 1) * cpg, 1:h - 1, 2:]]
# Bottom Right shift
i = 4
xs += [x_pad[:, i * cpg:(i + 1) * cpg, :h - 2, :w - 2]]
# Bottom Left shift
i = 5
xs += [x_pad[:, i * cpg:(i + 1) * cpg, :h - 2, 2:]]
# Top Right shift
i = 6
xs += [x_pad[:, i * cpg:(i + 1) * cpg, 2:, :w - 2]]
# Top Left shift
i = 7
xs += [x_pad[:, i * cpg:(i + 1) * cpg, 2:, 2:]]
i = 8
xs += [x_pad[:, i * cpg:, 1:h - 1, 1:w - 1]]
h = F.concatenate(*xs, axis=1)
return h
# Shift Units
def sc2(x, scope_name, dn=False):
C = x.shape[1]
h = x
with nn.parameter_scope(scope_name):
with nn.parameter_scope("shift1"): # no meaning but semantics
h = shift(h)
with nn.parameter_scope("conv1"):
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h, True)
h = PF.convolution(h,
C,
kernel=(1, 1),
pad=(0, 0),
with_bias=False)
with nn.parameter_scope("shift2"): # no meaning but semantics
h = shift(h)
with nn.parameter_scope("conv2"):
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h, True)
stride = (2, 2) if dn else (1, 1)
if p > 0:
h = F.dropout(h, p=0.5) if not test else h
h = PF.convolution(h,
C,
kernel=(1, 1),
pad=(0, 0),
stride=stride,
with_bias=False)
s = F.average_pooling(x, (2, 2)) if dn else x
return h + s
def csc(x, scope_name, dn=False):
C = x.shape[1]
h = x
with nn.parameter_scope(scope_name):
with nn.parameter_scope("conv1"):
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h, True)
h = PF.convolution(h,
C,
kernel=(1, 1),
pad=(0, 0),
with_bias=False)
with nn.parameter_scope("shift"): # no meaning but semantics
h = shift(h)
with nn.parameter_scope("conv2"):
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h, True)
stride = (2, 2) if dn else (1, 1)
if p > 0:
h = F.dropout(h, p=0.5) if not test else h
h = PF.convolution(h,
C,
kernel=(1, 1),
pad=(0, 0),
stride=stride,
with_bias=False)
s = F.average_pooling(x, (2, 2)) if dn else x
return h + s
def shift_unit(x, scope_name, dn=False):
if module == "sc2":
return sc2(x, scope_name, dn)
if module == "csc":
return csc(x, scope_name, dn)
ncls = 10
# Conv -> BN -> Relu
with nn.parameter_scope("conv1"):
# Preprocess
image /= 255.0
if not test:
image = F.image_augmentation(image,
contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
h = PF.convolution(image,
maps,
kernel=(3, 3),
pad=(1, 1),
with_bias=False)
h = shift_unit(h, "conv2", False) # -> 32x32
h = shift_unit(h, "conv3", True) # -> 16x16
h = shift_unit(h, "conv4", False) # -> 16x16
h = shift_unit(h, "conv5", True) # -> 8x8
h = shift_unit(h, "conv6", False) # -> 8x8
h = shift_unit(h, "conv7", True) # -> 4x4
h = shift_unit(h, "conv8", False) # -> 4x4
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
pred = PF.affine(h, ncls)
return pred
def cifar10_min_max_resnet23_prediction(image,
maps=64,
ql_min=0,
ql_max=255,
p_min_max=False,
a_min_max=False,
a_ema=False,
ste_fine_grained=True,
test=False):
"""
Construct MinMaxNet using resnet23.
"""
a_min_max = a_min_max if not test else False
# Residual Unit
def res_unit(x, scope_name, dn=False):
C = x.shape[1]
with nn.parameter_scope(scope_name):
# Conv -> BN -> MinMaxQuantize -> Relu
with nn.parameter_scope("conv1"):
h = PF.min_max_quantized_convolution(
x,
C // 2,
kernel=(1, 1),
pad=(0, 0),
ql_min_w=ql_min,
ql_max_w=ql_max,
w_min_max=p_min_max,
ste_fine_grained_w=ste_fine_grained,
with_bias=False)
h = F.relu(h)
h = PF.batch_normalization(h, batch_stat=not test)
h = PF.min_max_quantize(h,
x_min_max=a_min_max,
ema=a_ema,
ql_min=ql_min,
ql_max=ql_max,
ste_fine_grained=ste_fine_grained)
# Conv -> BN -> MinMaxQuantize -> Relu
with nn.parameter_scope("conv2"):
h = PF.min_max_quantized_convolution(
h,
C // 2,
kernel=(3, 3),
pad=(1, 1),
ql_min_w=ql_min,
ql_max_w=ql_max,
w_min_max=p_min_max,
ste_fine_grained_w=ste_fine_grained,
with_bias=False)
h = F.relu(h)
h = PF.batch_normalization(h, batch_stat=not test)
h = PF.min_max_quantize(h,
x_min_max=a_min_max,
ema=a_ema,
ql_min=ql_min,
ql_max=ql_max,
ste_fine_grained=ste_fine_grained)
# Conv -> BN
with nn.parameter_scope("conv3"):
h = PF.min_max_quantized_convolution(
h,
C,
kernel=(1, 1),
pad=(0, 0),
ql_min_w=ql_min,
ql_max_w=ql_max,
w_min_max=p_min_max,
ste_fine_grained_w=ste_fine_grained,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
# Residual -> MinMaxQuantize -> Relu
h = PF.min_max_quantize(h,
x_min_max=a_min_max,
ema=a_ema,
ql_min=ql_min,
ql_max=ql_max,
ste_fine_grained=ste_fine_grained)
h = F.relu(h + x)
# Maxpooling
if dn:
h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
return h
ncls = 10
# Conv -> BN -> MinMaxQuantize
with nn.parameter_scope("conv1"):
# Preprocess
image /= 255.0
if not test:
image = F.image_augmentation(image,
contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
h = PF.min_max_quantized_convolution(
image,
maps,
kernel=(3, 3),
pad=(1, 1),
ql_min_w=ql_min,
ql_max_w=ql_max,
w_min_max=p_min_max,
ste_fine_grained_w=ste_fine_grained,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = PF.min_max_quantize(h,
x_min_max=a_min_max,
ema=a_ema,
ql_min=ql_min,
ql_max=ql_max,
ste_fine_grained=ste_fine_grained)
h = F.relu(h)
h = res_unit(h, "conv2", False) # -> 32x32
h = res_unit(h, "conv3", True) # -> 16x16
h = res_unit(h, "conv4", False) # -> 16x16
h = res_unit(h, "conv5", True) # -> 8x8
h = res_unit(h, "conv6", False) # -> 8x8
h = res_unit(h, "conv7", True) # -> 4x4
h = res_unit(h, "conv8", False) # -> 4x4
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
pred = PF.min_max_quantized_affine(h,
ncls,
ql_min_w=ql_min,
ql_max_w=ql_max,
w_min_max=p_min_max,
ste_fine_grained_w=ste_fine_grained,
with_bias=False,
quantize_b=True,
ql_min_b=ql_min,
ql_max_b=ql_max,
b_min_max=p_min_max,
ste_fine_grained_b=ste_fine_grained)
return pred
def cifar10_inq_resnet23_prediction(image,
maps=64,
num_bits=4,
inq_iterations=(5000, 6000, 7000, 8000,
9000),
selection_algorithm='largest_abs',
test=False):
"""
Construct INQ Network using resnet23.
"""
# Residual Unit
def res_unit(x, scope_name, dn=False):
C = x.shape[1]
with nn.parameter_scope(scope_name):
# Conv -> BN -> Relu
with nn.parameter_scope("conv1"):
h = PF.inq_convolution(x,
C // 2,
kernel=(1, 1),
pad=(0, 0),
inq_iterations=inq_iterations,
selection_algorithm=selection_algorithm,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
# Conv -> BN -> Relu
with nn.parameter_scope("conv2"):
h = PF.inq_convolution(h,
C // 2,
kernel=(3, 3),
pad=(1, 1),
inq_iterations=inq_iterations,
selection_algorithm=selection_algorithm,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
# Conv -> BN
with nn.parameter_scope("conv3"):
h = PF.inq_convolution(h,
C,
kernel=(1, 1),
pad=(0, 0),
inq_iterations=inq_iterations,
selection_algorithm=selection_algorithm,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
# Residual -> Relu
h = F.relu(h + x)
# Maxpooling
if dn:
h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
return h
ncls = 10
# Conv -> BN
with nn.parameter_scope("conv1"):
# Preprocess
image /= 255.0
if not test:
image = F.image_augmentation(image,
contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
h = PF.inq_convolution(image,
maps,
kernel=(3, 3),
pad=(1, 1),
inq_iterations=inq_iterations,
selection_algorithm=selection_algorithm,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
h = res_unit(h, "conv2", False) # -> 32x32
h = res_unit(h, "conv3", True) # -> 16x16
h = res_unit(h, "conv4", False) # -> 16x16
h = res_unit(h, "conv5", True) # -> 8x8
h = res_unit(h, "conv6", False) # -> 8x8
h = res_unit(h, "conv7", True) # -> 4x4
h = res_unit(h, "conv8", False) # -> 4x4
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
pred = PF.inq_affine(h,
ncls,
inq_iterations=inq_iterations,
selection_algorithm=selection_algorithm)
return pred
def cifar10_pow2_net_resnet23_prediction(image,
maps=64,
n=8,
m=1,
ste_fine_grained=True,
test=False):
"""
Construct Pow2Net using resnet23.
"""
# Residual Unit
def res_unit(x, scope_name, dn=False):
C = x.shape[1]
with nn.parameter_scope(scope_name):
# Conv -> BN -> Pow2Quantize -> Relu
with nn.parameter_scope("conv1"):
h = PF.pow2_quantized_convolution(x,
C // 2,
kernel=(1, 1),
pad=(0, 0),
n_w=n,
m_w=m,
n_b=n,
m_b=m,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.pow2_quantize(h,
n=n,
m=m,
ste_fine_grained=ste_fine_grained)
h = F.relu(h)
# Conv -> BN -> Pow2Quantize -> Relu
with nn.parameter_scope("conv2"):
h = PF.pow2_quantized_convolution(h,
C // 2,
kernel=(3, 3),
pad=(1, 1),
n_w=n,
m_w=m,
n_b=n,
m_b=m,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.pow2_quantize(h,
n=n,
m=m,
ste_fine_grained=ste_fine_grained)
h = F.relu(h)
# Conv -> BN
with nn.parameter_scope("conv3"):
h = PF.pow2_quantized_convolution(h,
C,
kernel=(1, 1),
pad=(0, 0),
n_w=n,
m_w=m,
n_b=n,
m_b=m,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
# Residual -> Pow2Quantize -> Relu
h = F.pow2_quantize(h, n=n, m=m, ste_fine_grained=ste_fine_grained)
h = F.relu(h + x)
# Maxpooling
if dn:
h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
return h
ncls = 10
# Conv -> BN -> Pow2Quantize
with nn.parameter_scope("conv1"):
# Preprocess
image /= 255.0
if not test:
image = F.image_augmentation(image,
contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
h = PF.pow2_quantized_convolution(image,
maps,
kernel=(3, 3),
pad=(1, 1),
n_w=n,
m_w=m,
n_b=n,
m_b=m,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.pow2_quantize(h, n=n, m=m, ste_fine_grained=ste_fine_grained)
h = F.relu(h)
h = res_unit(h, "conv2", False) # -> 32x32
h = res_unit(h, "conv3", True) # -> 16x16
h = res_unit(h, "conv4", False) # -> 16x16
h = res_unit(h, "conv5", True) # -> 8x8
h = res_unit(h, "conv6", False) # -> 8x8
h = res_unit(h, "conv7", True) # -> 4x4
h = res_unit(h, "conv8", False) # -> 4x4
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
pred = PF.pow2_quantized_affine(h, ncls, n_w=n, m_w=m, n_b=n, m_b=m)
return pred
def cifar10_shuffle_prediction(image, maps=64, groups=1, test=False):
"""
Construct ShuffleNet
"""
def shuffle(x):
n, c, h, w = x.shape
g = groups
assert c % g == 0
# N, C, H, W -> N, g, C/g, H, W -> N, C/g, g, H, W -> N, C, H, W
x = F.reshape(x, [n, g, c // g, h, w])
x = F.transpose(x, [0, 2, 1, 3, 4])
x = F.reshape(x, [n, c, h, w])
return x
# Shuffle
def shuffle_unit(x, scope_name, dn=False):
"""
Figure. 2 (b) and (c) in https://arxiv.org/pdf/1707.01083.pdf
"""
C = x.shape[1]
h = x
with nn.parameter_scope(scope_name):
with nn.parameter_scope("gconv1"):
h = PF.convolution(h,
C,
kernel=(1, 1),
pad=(0, 0),
group=groups,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h, True)
with nn.parameter_scope("shuffle"): # no meaning but semantics
h = shuffle(h)
with nn.parameter_scope("dconv"):
stride = (2, 2) if dn else (1, 1)
h = PF.depthwise_convolution(h,
kernel=(3, 3),
pad=(1, 1),
stride=stride,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
with nn.parameter_scope("gconv2"):
h = PF.convolution(h,
C,
kernel=(1, 1),
pad=(0, 0),
group=groups,
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
s = F.average_pooling(x, (2, 2)) if dn else x
h = F.concatenate(*[h, s], axis=1) if dn else h + s
h = F.relu(h)
return h
ncls = 10
# Conv -> BN -> Relu
with nn.parameter_scope("conv1"):
# Preprocess
image /= 255.0
if not test:
image = F.image_augmentation(image,
contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
h = PF.convolution(image,
maps,
kernel=(3, 3),
pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
h = shuffle_unit(h, "conv2", False) # -> 32x32
h = shuffle_unit(h, "conv3", True) # -> 16x16
h = shuffle_unit(h, "conv4", False) # -> 16x16
h = shuffle_unit(h, "conv5", True) # -> 8x8
h = shuffle_unit(h, "conv6", False) # -> 8x8
h = shuffle_unit(h, "conv7", True) # -> 4x4
h = shuffle_unit(h, "conv8", False) # -> 4x4
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
pred = PF.affine(h, ncls)
return pred
def cifar10_fp_net_resnet23_prediction(image, maps=64,
test=False):
"""
Construct Fixed-Point Net using resnet23. Fixed-Point Net quantizes weights
and activations using FixedPointQuantize function.
"""
# Residual Unit
def res_unit(x, scope_name, rng, dn=False, test=False):
C = x.shape[1]
with nn.parameter_scope(scope_name):
# Conv -> BN -> FixedPointQuantize -> Relu
with nn.parameter_scope("conv1"):
h = PF.fixed_point_quantized_convolution(x, C / 2, kernel=(1, 1), pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.fixed_point_quantize(h)
h = F.relu(h)
# Conv -> BN -> FixedPointQuantize -> Relu
with nn.parameter_scope("conv2"):
h = PF.fixed_point_quantized_convolution(h, C / 2, kernel=(3, 3), pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.fixed_point_quantize(h)
h = F.relu(h)
# Conv -> BN
with nn.parameter_scope("conv3"):
h = PF.fixed_point_quantized_convolution(h, C, kernel=(1, 1), pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
# Residual -> FixedPointQuantize -> Relu
h = F.fixed_point_quantize(h)
h = F.relu(h + x)
# Maxpooling
if dn:
h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
return h
ncls = 10
# Conv -> BN -> FixedPointQuantize
with nn.parameter_scope("conv1"):
# Preprocess
image /= 255.0
if not test:
image = F.image_augmentation(image, contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
h = PF.fixed_point_quantized_convolution(image, maps, kernel=(3, 3), pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.fixed_point_quantize(h)
h = res_unit(h, "conv2", False) # -> 32x32
h = res_unit(h, "conv3", True) # -> 16x16
h = res_unit(h, "conv4", False) # -> 16x16
h = res_unit(h, "conv5", True) # -> 8x8
h = res_unit(h, "conv6", False) # -> 8x8
h = res_unit(h, "conv7", True) # -> 4x4
h = res_unit(h, "conv8", False) # -> 4x4
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
pred = PF.fixed_point_quantized_affine(h, ncls)
return pred
def cifar10_svd_factorized_resnet23_prediction(image,
maps=64,
test=False,
compression_ratio=0.0):
"""
Construct Resnet23 with factorized affine and convolution
"""
# SVD affine
def svd_affine(x, n_outputs, cr):
W = get_parameter('affine/W')
if W is None:
UV = None
else:
UV = W.d
b = get_parameter('affine/b')
# compute rank (size of intermediate activations)
# to obtained desired reduction
inshape = np.prod(x.shape[1:])
outshape = np.prod(n_outputs)
rank = int(
np.floor((1 - cr) * inshape * outshape / (inshape + outshape)))
# Initialize bias to existing b in affine if exists
if b is not None:
b_new = get_parameter_or_create('svd_affine/b',
b.d.shape,
need_grad=b.need_grad)
b_new.d = b.d.copy()
logger.info(
"SVD affine created: input_shape = {}; output_shape = {}; compression = {}; rank = {};"
.format(inshape, outshape, cr, rank))
# create svd_affine initialized from W in current context if it exists
return PF.svd_affine(x, n_outputs, rank, uv_init=UV)
# SVD convolution
def svd_convolution(x, n_outputs, kernel, pad, with_bias, cr):
W = get_parameter('conv/W')
if W is None:
UV = None
else:
UV = W.d
b = get_parameter('conv/b')
# compute rank (size of intermediate activations)
# to obtained desired reduction
inmaps = x.shape[1]
outmaps = n_outputs
Ksize = np.prod(kernel)
rank = int(
np.floor((1 - cr) * inmaps * outmaps * Ksize /
(inmaps * Ksize + inmaps * outmaps)))
# Initialize bias to existing b in affine if exists
if b is not None:
b_new = get_parameter_or_create('svd_conv/b',
b.d.shape,
need_grad=b.need_grad)
b_new.d = b.d.copy()
logger.info(
"SVD convolution created: inmaps = {}; outmaps = {}; compression = {}; rank = {};"
.format(inmaps, outmaps, cr, rank))
# create svd_convolution initialized from W in current context if it exists
return PF.svd_convolution(x,
n_outputs,
kernel=kernel,
r=rank,
pad=pad,
with_bias=with_bias,
uv_init=UV)
# Residual Unit
def res_unit(x, scope_name, dn=False):
C = x.shape[1]
with nn.parameter_scope(scope_name):
# Conv -> BN -> Relu
with nn.parameter_scope("conv1"):
h = PF.convolution(x,
C // 2,
kernel=(1, 1),
pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
# Conv -> BN -> Relu
with nn.parameter_scope("conv2"):
h = svd_convolution(h,
C // 2,
kernel=(3, 3),
pad=(1, 1),
with_bias=False,
cr=compression_ratio)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
# Conv -> BN
with nn.parameter_scope("conv3"):
h = PF.convolution(h,
C,
kernel=(1, 1),
pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
# Residual -> Relu
h = F.relu(h + x)
# Maxpooling
if dn:
h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
return h
ncls = 10
# Conv -> BN -> Relu
with nn.parameter_scope("conv1"):
# Preprocess
image /= 255.0
if not test:
image = F.image_augmentation(image,
contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
h = svd_convolution(image,
maps,
kernel=(3, 3),
pad=(1, 1),
with_bias=False,
cr=compression_ratio)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
h = res_unit(h, "conv2", False) # -> 32x32
h = res_unit(h, "conv3", True) # -> 16x16
h = res_unit(h, "conv4", False) # -> 16x16
h = res_unit(h, "conv5", True) # -> 8x8
h = res_unit(h, "conv6", False) # -> 8x8
h = res_unit(h, "conv7", True) # -> 4x4
h = res_unit(h, "conv8", False) # -> 4x4
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
pred = svd_affine(h, ncls, compression_ratio)
return pred
def resnet_prediction(image, test=False, ncls=2, nmaps=128, act=F.relu):
# Residual Unit
def res_unit(x, scope_name, dn=False):
C = x.shape[1]
with nn.parameter_scope(scope_name):
# Conv -> BN -> Nonlinear
with nn.parameter_scope("conv1"):
h = PF.convolution(x,
C,
kernel=(1, 1),
pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = act(h)
# Conv -> BN -> Nonlinear
with nn.parameter_scope("conv2"):
h = PF.convolution(h,
C,
kernel=(3, 3),
pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = act(h)
# Conv -> BN
with nn.parameter_scope("conv3"):
h = PF.convolution(h,
C,
kernel=(1, 1),
pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
# Residual -> Nonlinear
h = act(F.add2(h, x, inplace=False))
# Maxpooling
if dn:
h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
return h
# Conv -> BN -> Nonlinear
with nn.parameter_scope("conv1"):
# Preprocess
if not test:
image = F.image_augmentation(image,
contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
h = PF.convolution(image,
nmaps,
kernel=(3, 3),
pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = act(h)
image_size = image.shape[-1]
for i in range(int(np.log2(image_size)) - 1):
h = res_unit(h, f'conv{i*2+2}', False)
if i != np.log2(image_size) - 2:
h = res_unit(h, f'conv{i*2+3}', True)
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
pred = PF.affine(h, ncls)
return pred
def cifar10_resnet23_prediction(image, net="teacher", maps=64, test=False):
"""
Construct ResNet 23
"""
# Residual Unit
def res_unit(x, scope_name, dn=False):
C = x.shape[1]
with nn.parameter_scope(scope_name):
# Conv -> BN -> Relu
with nn.parameter_scope("conv1"):
h = PF.convolution(x,
C / 2,
kernel=(1, 1),
pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
# Conv -> BN -> Relu
with nn.parameter_scope("conv2"):
h = PF.convolution(h,
C / 2,
kernel=(3, 3),
pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
# Conv -> BN
with nn.parameter_scope("conv3"):
h = PF.convolution(h,
C,
kernel=(1, 1),
pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
# Residual -> Relu
h = F.relu(h + x)
# Maxpooling
if dn:
h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
return h
ncls = 10
with nn.parameter_scope(net):
# Conv -> BN -> Relu
with nn.parameter_scope("conv1"):
# Preprocess
image /= 255.0
if not test:
image = F.image_augmentation(image,
contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
h = PF.convolution(image,
maps,
kernel=(3, 3),
pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
h = res_unit(h, "conv2", False) # -> 32x32
h = res_unit(h, "conv3", True) # -> 16x16
h = res_unit(h, "conv4", False) # -> 16x16
h = res_unit(h, "conv5", True) # -> 8x8
h = res_unit(h, "conv6", False) # -> 8x8
h = res_unit(h, "conv7", True) # -> 4x4
h = res_unit(h, "conv8", False) # -> 4x4
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
pred = PF.affine(h, ncls)
return pred
def cifar100_resnet23_prediction(image,
ctx, test=False):
"""
Construct ResNet 23
"""
# Residual Unit
def res_unit(x, scope_name, rng, dn=False, test=False):
C = x.shape[1]
with nn.parameter_scope(scope_name):
# Conv -> BN -> Relu
with nn.parameter_scope("conv1"):
w_init = UniformInitializer(
calc_uniform_lim_glorot(C, C / 2, kernel=(1, 1)),
rng=rng)
h = PF.convolution(x, C / 2, kernel=(1, 1), pad=(0, 0),
w_init=w_init, with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
# Conv -> BN -> Relu
with nn.parameter_scope("conv2"):
w_init = UniformInitializer(
calc_uniform_lim_glorot(C / 2, C / 2, kernel=(3, 3)),
rng=rng)
h = PF.convolution(h, C / 2, kernel=(3, 3), pad=(1, 1),
w_init=w_init, with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
# Conv -> BN
with nn.parameter_scope("conv3"):
w_init = UniformInitializer(
calc_uniform_lim_glorot(C / 2, C, kernel=(1, 1)),
rng=rng)
h = PF.convolution(h, C, kernel=(1, 1), pad=(0, 0),
w_init=w_init, with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
# Residual -> Relu
h = F.relu(h + x)
# Maxpooling
if dn:
h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
return h
# Random generator for using the same init parameters in all devices
rng = np.random.RandomState(0)
nmaps = 384
ncls = 100
# Conv -> BN -> Relu
with nn.context_scope(ctx):
with nn.parameter_scope("conv1"):
# Preprocess
if not test:
image = F.image_augmentation(image, contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
w_init = UniformInitializer(
calc_uniform_lim_glorot(3, nmaps, kernel=(3, 3)),
rng=rng)
h = PF.convolution(image, nmaps, kernel=(3, 3), pad=(1, 1),
w_init=w_init, with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
h = res_unit(h, "conv2", rng, False) # -> 32x32
h = res_unit(h, "conv3", rng, True) # -> 16x16
h = res_unit(h, "conv4", rng, False) # -> 16x16
h = res_unit(h, "conv5", rng, True) # -> 8x8
h = res_unit(h, "conv6", rng, False) # -> 8x8
h = res_unit(h, "conv7", rng, True) # -> 4x4
h = res_unit(h, "conv8", rng, False) # -> 4x4
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
w_init = UniformInitializer(
calc_uniform_lim_glorot(int(np.prod(h.shape[1:])), ncls, kernel=(1, 1)), rng=rng)
pred = PF.affine(h, ncls, w_init=w_init)
return pred
def vgg16_prediction(image, test=False, ncls=10, seed=0):
# Preprocess
if not test:
image = F.image_augmentation(image,
min_scale=0.8,
max_scale=1.2,
flip_lr=True,
seed=seed)
image.need_grad = False
# Convolution layers
h = PF.convolution(image,
64, (3, 3),
pad=(1, 1),
stride=(1, 1),
name="block1_conv1")
h = F.relu(h)
h = PF.convolution(h,
64, (3, 3),
pad=(1, 1),
stride=(1, 1),
name="block1_conv2")
h = F.relu(h)
h = F.max_pooling(h, (2, 2), stride=(2, 2))
h = PF.convolution(h,
128, (3, 3),
pad=(1, 1),
stride=(1, 1),
name="block2_conv1")
h = F.relu(h)
h = PF.convolution(h,
128, (3, 3),
pad=(1, 1),
stride=(1, 1),
name="block2_conv2")
h = F.relu(h)
h = F.max_pooling(h, (2, 2), stride=(2, 2))
h = PF.convolution(h,
256, (3, 3),
pad=(1, 1),
stride=(1, 1),
name="block3_conv1")
h = F.relu(h)
h = PF.convolution(h,
256, (3, 3),
pad=(1, 1),
stride=(1, 1),
name="block3_conv2")
h = F.relu(h)
h = PF.convolution(h,
256, (3, 3),
pad=(1, 1),
stride=(1, 1),
name="block3_conv3")
h = F.relu(h)
h = F.max_pooling(h, (2, 2), stride=(2, 2))
h = PF.convolution(h,
512, (3, 3),
pad=(1, 1),
stride=(1, 1),
name="block4_conv1")
h = F.relu(h)
h = PF.convolution(h,
512, (3, 3),
pad=(1, 1),
stride=(1, 1),
name="block4_conv2")
h = F.relu(h)
h = PF.convolution(h,
512, (3, 3),
pad=(1, 1),
stride=(1, 1),
name="block4_conv3")
h = F.relu(h)
h = F.max_pooling(h, (2, 2), stride=(2, 2))
h = PF.convolution(h,
512, (3, 3),
pad=(1, 1),
stride=(1, 1),
name="block5_conv1")
h = F.relu(h)
h = PF.convolution(h,
512, (3, 3),
pad=(1, 1),
stride=(1, 1),
name="block5_conv2")
h = F.relu(h)
h = PF.convolution(h,
512, (3, 3),
pad=(1, 1),
stride=(1, 1),
name="block5_conv3")
h = F.relu(h)
hidden = F.max_pooling(h, (2, 2), stride=(2, 2))
# Fully-Connected layers
h = PF.affine(hidden, 4096, name="fc1")
h = F.relu(h)
h = PF.affine(h, 4096, name="fc2")
h = F.relu(h)
pred = PF.affine(h, ncls, name="fc3")
return pred, h
def cifar10_binary_net_resnet23_prediction(image, maps=64,
test=False):
"""
Construct BianryNet using resnet23. Binary Net binaries weights and activations.
References:
Courbariaux Matthieu, Bengio Yoshua, David Jean-Pierre,
"BinaryConnect: Training Deep Neural Networks with binary weights during propagations",
Advances in Neural Information Processing Systems 28 (NIPS 2015)
"""
# Residual Unit
def res_unit(x, scope_name, rng, dn=False, test=False):
C = x.shape[1]
with nn.parameter_scope(scope_name):
# Conv -> BN -> BinaryTanh
with nn.parameter_scope("conv1"):
h = PF.binary_connect_convolution(x, C / 2, kernel=(1, 1), pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.binary_tanh(h)
# Conv -> BN -> BinaryTanh
with nn.parameter_scope("conv2"):
h = PF.binary_connect_convolution(h, C / 2, kernel=(3, 3), pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.binary_tanh(h)
# Conv -> BN
with nn.parameter_scope("conv3"):
h = PF.binary_connect_convolution(h, C, kernel=(1, 1), pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
# Residual -> BinaryTanh
h = F.binary_tanh(h + x)
# Maxpooling
if dn:
h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
return h
ncls = 10
# Conv -> BN -> Binary_tanh
with nn.parameter_scope("conv1"):
# Preprocess
image /= 255.0
if not test:
image = F.image_augmentation(image, contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
h = PF.binary_connect_convolution(image, maps, kernel=(3, 3), pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.binary_tanh(h)
h = res_unit(h, "conv2", False) # -> 32x32
h = res_unit(h, "conv3", True) # -> 16x16
h = res_unit(h, "conv4", False) # -> 16x16
h = res_unit(h, "conv5", True) # -> 8x8
h = res_unit(h, "conv6", False) # -> 8x8
h = res_unit(h, "conv7", True) # -> 4x4
h = res_unit(h, "conv8", False) # -> 4x4
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
pred = PF.binary_connect_affine(h, ncls)
return pred
def cifar10_binary_weight_resnet23_prediction(image, maps=64,
test=False):
"""
Construct BianryWeight using resnet23. Binary Weight binaries weights, but
use the approximate coefficients to alleviate the binary quantization.
References:
Rastegari Mohammad, Ordonez Vicente, Redmon Joseph, and Farhadi Ali,
"XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks",
arXiv:1603.05279
"""
# Residual Unit
def res_unit(x, scope_name, rng, dn=False, test=False):
C = x.shape[1]
with nn.parameter_scope(scope_name):
# Conv -> BN -> Relu
with nn.parameter_scope("conv1"):
h = PF.binary_weight_convolution(x, C / 2, kernel=(1, 1), pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
# Conv -> BN -> Relu
with nn.parameter_scope("conv2"):
h = PF.binary_weight_convolution(h, C / 2, kernel=(3, 3), pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
# Conv -> BN
with nn.parameter_scope("conv3"):
h = PF.binary_weight_convolution(h, C, kernel=(1, 1), pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
# Residual -> Relu
h = F.relu(h + x)
# Maxpooling
if dn:
h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
return h
ncls = 10
# Conv -> BN -> Relu
with nn.parameter_scope("conv1"):
# Preprocess
image /= 255.0
if not test:
image = F.image_augmentation(image, contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
h = PF.binary_weight_convolution(image, maps, kernel=(3, 3), pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.relu(h)
h = res_unit(h, "conv2", False) # -> 32x32
h = res_unit(h, "conv3", True) # -> 16x16
h = res_unit(h, "conv4", False) # -> 16x16
h = res_unit(h, "conv5", True) # -> 8x8
h = res_unit(h, "conv6", False) # -> 8x8
h = res_unit(h, "conv7", True) # -> 4x4
h = res_unit(h, "conv8", False) # -> 4x4
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
pred = PF.binary_weight_affine(h, ncls)
return pred
def single_image_augment(image):
image = F.image_augmentation(image, contrast=1.0, angle=0.0, flip_lr=True)
image = F.random_shift(image, shifts=(4, 4), border_mode="reflect")
return image
