def testShapeInferenceConvNet(self):
model = model_helper.ModelHelper(name="convtest")
model.NHWC2NCHW("data", "data_nchw")
brew.conv(model,
"data_nchw",
'conv1',
3,
64,
weight_init=("MSRAFill", {}),
kernel=7,
stride=2,
pad=3,
no_bias=0)
brew.spatial_bn(model,
'conv1',
'conv1_spatbn_relu',
64,
epsilon=1e-3,
is_test=False)
brew.relu(model, 'conv1_spatbn_relu', 'conv1_spatbn_relu')
brew.max_pool(model, 'conv1_spatbn_relu', 'pool1', kernel=3, stride=2)
brew.fc(model, 'pool1', 'fc', dim_in=(64 * 56 * 56), dim_out=100)
brew.dropout(model, 'fc', 'fc_drop', is_test=False)
model.Sigmoid('fc_drop', 'fc_sigm')
brew.softmax(model, 'fc_sigm', 'softmax')
model.LabelCrossEntropy(['softmax', 'label'], 'xent')
loss = model.AveragedLoss('xent', 'loss')
model.AddGradientOperators([loss])
LR = model.param_init_net.ConstantFill([], 'LR', shape=[1], value=0.1)
for param in model.GetParams():
param_grad = model.param_to_grad[param]
param_momentum = model.param_init_net.ConstantFill([param],
param +
'_momentum',
value=0.0)
model.net.MomentumSGDUpdate(
[param_grad, param_momentum, LR, param],
[param_grad, param_momentum, param],
)
workspace.FeedBlob(
"data",
np.random.rand(16, 227, 227, 3).astype(np.float32),
)
workspace.FeedBlob(
"label",
(100 * np.random.rand(16)).astype(np.int32),
)
workspace.FeedBlob(
"label",
(100 * np.random.rand(16)).astype(np.int32),
)
# Then do automatic comparison test: run the next once to
# initialize everything
self.InferTensorRunAndCompare(model)
def test_dropout(self):
p = 0.2
X = np.ones((100, 100)).astype(np.float32) - p
workspace.FeedBlob("x", X)
model = ModelHelper(name="test_model")
brew.dropout(model, "x", "out")
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
out = workspace.FetchBlob("out")
self.assertLess(abs(out.mean() - (1 - p)), 0.05)
def test_dropout(self):
p = 0.2
X = np.ones((100, 100)).astype(np.float32) - p
workspace.FeedBlob("x", X)
model = ModelHelper(name="test_model")
brew.dropout(model, "x", "out")
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
out = workspace.FetchBlob("out")
self.assertLess(abs(out.mean() - (1 - p)), 0.05)
def AddMLP_BN(model, data):
'''
Implement MLP model on MNIST
'''
# number of nuerons in fc layer
num_units = 4096
# NCHW: 64 x 1 x 28 x 28 -> 64 x 1 x 28 x 28
drop1 = brew.dropout(model, data, 'drop1', ratio=0.5, is_test=0)
# NCHW: 64 x 1 x 28 x 28 -> 64 x 4096
fc2 = brew.fc(model, drop1, 'fc2', dim_in=1 * 28 * 28, dim_out=num_units)
fc2_reshaped = model.Reshape([fc2], ['fc2_reshaped'], shape=(4, 2))
# bn2 = brew.spatial_bn(model, fc2, 'bn2', num_units, epsilon=1e-3, momentum=0.9, is_test=is_test)
#relu2 = brew.relu(model, fc2, 'relu2')
#fc3 = brew.fc(model, relu2, 'fc3', dim_in=num_units, dim_out=num_units)
# bn3 = brew.spatial_bn(model, fc3, 'bn3', bn_units, epsilon=1e-3, momentum=0.9, is_test=is_test)
#relu3 = brew.relu(model, fc3, 'relu3')
#fc4 = brew.fc(model, relu3, 'fc4', dim_in=num_units, dim_out=num_units)
# bn4 = brew.spatial_bn(model, fc4, 'bn4', bn_units, epsilon=1e-3, momentum=0.9, is_test=is_test)
#relu4 = brew.relu(model, fc4, 'relu4')
#fc5 = brew.fc(model, relu4, 'fc5', dim_in=num_units, dim_out=10) # 10 for 10-classes
# bn5 = brew.spatial_bn(model, fc5, 'bn5', 10, epsilon=1e-3, momentum=0.9, is_test=is_test)
softmax = brew.softmax(model, fc2, 'softmax')
return softmax
def testShapeInferenceConvNet(self):
model = model_helper.ModelHelper(name="convtest")
model.NHWC2NCHW("data", "data_nchw")
brew.conv(model, "data_nchw", 'conv1', 3, 64,
weight_init=("MSRAFill", {}), kernel=7,
stride=2, pad=3, no_bias=0)
brew.spatial_bn(model, 'conv1', 'conv1_spatbn_relu', 64, epsilon=1e-3, is_test=False)
brew.relu(model, 'conv1_spatbn_relu', 'conv1_spatbn_relu')
brew.max_pool(model, 'conv1_spatbn_relu', 'pool1', kernel=3, stride=2)
brew.fc(model, 'pool1', 'fc', dim_in=(64 * 56 * 56), dim_out=100)
brew.dropout(model, 'fc', 'fc_drop', is_test=False)
model.Sigmoid('fc_drop', 'fc_sigm')
brew.softmax(model, 'fc_sigm', 'softmax')
model.LabelCrossEntropy(['softmax', 'label'], 'xent')
loss = model.AveragedLoss('xent', 'loss')
model.AddGradientOperators([loss])
LR = model.param_init_net.ConstantFill(
[], 'LR', shape=[1], value=0.1
)
for param in model.GetParams():
param_grad = model.param_to_grad[param]
param_momentum = model.param_init_net.ConstantFill(
[param], param + '_momentum', value=0.0
)
model.net.MomentumSGDUpdate(
[param_grad, param_momentum, LR, param],
[param_grad, param_momentum, param],
)
workspace.FeedBlob(
"data",
np.random.rand(16, 227, 227, 3).astype(np.float32),
)
workspace.FeedBlob(
"label",
(100 * np.random.rand(16)).astype(np.int32),
)
workspace.FeedBlob(
"label",
(100 * np.random.rand(16)).astype(np.int32),
)
# Then do automatic comparison test: run the next once to
# initialize everything
self.InferTensorRunAndCompare(model)
def add_dropout(self, prev_blob, ratio, is_test=False):
self.prev_blob = brew.dropout(self.model,
prev_blob,
'%s_dropout_%d' %
(self.block_name, self.layer_num),
ratio=ratio,
is_test=is_test)
self.layer_num += 1
return self.prev_blob
def forward_pass_builder(self, model, loss_scale=1.0):
"""
This function adds the operators, layers to the network. It should return a list
of loss-blobs that are used for computing the loss gradient. This function is
also passed an internally calculated loss_scale parameter that is used to scale
your loss to normalize for the number of GPUs. Signature: function(model, loss_scale)
"""
is_inference = self.phase == 'inference'
v = 'data'
v = brew.conv(model, v, 'conv1', 3, 96, kernel=11, stride=4)
v = brew.relu(model, v, 'relu1')
v = brew.lrn(model, v, 'norm1', size=5, alpha=0.0001, beta=0.75)
v = brew.max_pool(model, v, 'pool1', kernel=3, stride=2)
v = brew.conv(model, v, 'conv2', 96, 256, kernel=5, pad=2, group=1)
v = brew.relu(model, v, 'relu2')
v = brew.lrn(model, v, 'norm2', size=5, alpha=0.0001, beta=0.75)
v = brew.max_pool(model, v, 'pool2', kernel=3, stride=2)
v = brew.conv(model, v, 'conv3', 256, 384, kernel=3, pad=1)
v = brew.relu(model, v, 'relu3')
v = brew.conv(model, v, 'conv4', 384, 384, kernel=3, pad=1, group=1)
v = brew.relu(model, v, 'relu4')
v = brew.conv(model, v, 'conv5', 384, 256, kernel=3, pad=1, group=1)
v = brew.relu(model, v, 'relu5')
v = brew.max_pool(model, v, 'pool5', kernel=3, stride=2)
v = brew.fc(model, v, 'fc6', dim_in=9216, dim_out=4096)
v = brew.relu(model, v, 'relu6')
v = brew.dropout(model, v, 'drop6', ratio=0.5, is_test=is_inference)
v = brew.fc(model, v, 'fc7', dim_in=4096, dim_out=4096)
v = brew.relu(model, v, 'relu7')
v = brew.dropout(model, v, 'drop7', ratio=0.5, is_test=is_inference)
return self.add_head_nodes(model,
v,
4096,
'fc8',
loss_scale=loss_scale)
def AddModel(model, data, device_opts,is_test=False):
with core.DeviceScope(device_opts):
conv1 = brew.conv(
model,
data,
'conv1',
dim_in=IMAGE_CHANNELS,
dim_out=6,
weight_init=('MSRAFill', {}),
kernel=5,
stride=1,
pad=0)
relu1 = brew.relu(model, conv1, 'relu1')
pool1 = brew.max_pool(model, relu1, 'pool1', kernel=2, stride=2)
conv2 = brew.conv(
model,
pool1,
'conv2',
dim_in=6,
dim_out=16,
weight_init=('MSRAFill', {}),
kernel=5,
stride=1,
pad=0)
relu2 = brew.relu(model, conv2, 'relu2')
pool2 = brew.max_pool(model, relu2, 'pool2', kernel=2, stride=2)
# Fully connected layers
fc1 = brew.fc(model, pool2, 'fc1', dim_in=16*4*4, dim_out=120)
relu3 = brew.relu(model, fc1, 'relu3')
dropout1 = brew.dropout(model, relu3, 'dropout1', ratio=0.5, is_test=is_test)
fc2 = brew.fc(model, dropout1, 'fc2', dim_in=120, dim_out=84)
relu4 = brew.relu(model, fc2, 'relu4')
dropout2 = brew.dropout(model, relu4, 'dropout2', ratio=0.5, is_test=is_test)
fc3 = brew.fc(model, dropout2, 'fc3', dim_in=84, dim_out=NUM_CLASSES)
# Softmax layer
softmax = brew.softmax(model, fc3, 'softmax')
return softmax
def AddMLP(model, data, batch_size):
'''
Implement MLP model on MNIST
'''
num_units = 4096 # number of nuerons in fc layer
num_labels = 10 # for 10 classes in mnist
drop1 = brew.dropout(model, data, 'drop1', ratio=0.5, is_test=0)
fc2 = brew.fc(model, drop1, 'fc2', dim_in=1 * 28 * 28, dim_out=num_units)
model.Reshape([fc2], [fc2, 'fc2_old_shape'],
shape=(batch_size, num_units, 1, 1))
bn2 = brew.spatial_bn(model,
fc2,
'bn2',
num_units,
epsilon=1e-4,
momentum=0.9)
relu2 = brew.relu(model, bn2, 'relu2')
fc3 = brew.fc(model, relu2, 'fc3', dim_in=num_units, dim_out=num_units)
model.Reshape([fc3], [fc3, 'fc3_old_shape'],
shape=(batch_size, num_units, 1, 1))
bn3 = brew.spatial_bn(model,
fc3,
'bn3',
num_units,
epsilon=1e-4,
momentum=0.9)
relu3 = brew.relu(model, bn3, 'relu3')
fc4 = brew.fc(model, relu3, 'fc4', dim_in=num_units, dim_out=num_units)
model.Reshape([fc4], [fc4, 'fc4_old_shape'],
shape=(batch_size, num_units, 1, 1))
bn4 = brew.spatial_bn(model,
fc4,
'bn4',
num_units,
epsilon=1e-4,
momentum=0.9)
relu4 = brew.relu(model, bn4, 'relu4')
fc5 = brew.fc(model, relu4, 'fc5', dim_in=num_units, dim_out=num_labels)
model.Reshape([fc5], [fc5, 'fc5_old_shape'],
shape=(batch_size, num_labels, 1, 1))
bn5 = brew.spatial_bn(model,
fc5,
'bn5',
num_labels,
epsilon=1e-4,
momentum=0.9)
softmax = brew.softmax(model, bn5, 'softmax')
return softmax
def fc_module(model, inputs, dim_in, dim_out, module_seq, dropout, is_test):
fc = brew.fc(model,
inputs,
'fc{}'.format(module_seq),
dim_in=dim_in,
dim_out=dim_out)
relu = brew.relu(model, fc, fc)
dropout = brew.dropout(model,
relu,
'pool{}'.format(module_seq),
is_test=is_test,
ratio=dropout)
return dropout
def create_model(m, device_opts) :
with core.DeviceScope(device_opts):
conv1 = brew.conv(m, 'data', 'conv1', dim_in=3, dim_out=50, kernel=3, pad=1, no_gradient_to_input=1)
relu1 = brew.relu(m, conv1, 'relu1')
conv2 = brew.conv(m, relu1, 'conv2', dim_in=50, dim_out=50, kernel=3, pad=1)
pool1 = brew.max_pool(m, conv2, 'pool1', kernel=2, stride=2)
relu2 = brew.relu(m, pool1, 'relu2')
drop1 = brew.dropout(m, relu2, 'drop1', ratio=0.25)
conv3 = brew.conv(m, drop1, 'conv3', dim_in=50, dim_out=100, kernel=3, pad=1)
relu3 = brew.relu(m, conv3, 'relu3')
conv4 = brew.conv(m, relu3, 'conv4', dim_in=100, dim_out=100, kernel=3, pad=1)
pool2 = brew.max_pool(m, conv4, 'pool2', kernel=2, stride=2)
relu4 = brew.relu(m, pool2, 'relu4')
drop2 = brew.dropout(m, relu4, 'drop2', ratio=0.25)
fc1 = brew.fc(m, drop2, 'fc1', dim_in=100 * 8 * 8, dim_out=512)
relu5 = brew.relu(m, fc1, 'relu5')
drop3 = brew.dropout(m, relu5, 'drop3', ratio=0.5)
fc2 = brew.fc(m, drop3, 'fc2', dim_in=512, dim_out=N_CLASSES)
softmax = brew.softmax(m, fc2, 'softmax')
return softmax
def forward_pass_builder(self, model, loss_scale=1.0):
"""
This function adds the operators, layers to the network. It should return
a list of loss-blobs that are used for computing the loss gradient. This
function is also passed an internally calculated loss_scale parameter that
is used to scale your loss to normalize for the number of GPUs.
Signature: function(model, loss_scale)
"""
is_inference = self.phase == 'inference'
layers, filters = VGG.specs[self.__model]['specs']
v = 'data'
dim_in = self.input_shape[0]
for i, num in enumerate(layers):
for j in range(num):
v = brew.conv(model,
v,
'conv%d_%d' % (i + 1, j + 1),
dim_in,
filters[i],
kernel=3,
pad=1)
v = brew.relu(model, v, 'relu%d_%d' % (i + 1, j + 1))
dim_in = filters[i]
v = brew.max_pool(model, v, 'pool%d' % (i + 1), kernel=2, stride=2)
dim_in = 25088 # 512 * 7 * 7 (output tensor of previous max pool layer)
for i in range(2):
v = brew.fc(model,
v,
'fc%d' % (6 + i),
dim_in=dim_in,
dim_out=4096)
v = brew.relu(model, v, 'relu%d' % (6 + i))
v = brew.dropout(model,
v,
'drop%d' % (6 + i),
ratio=0.5,
is_test=is_inference)
dim_in = 4096
return self.add_head_nodes(model,
v,
4096,
'fc8',
loss_scale=loss_scale)
def MakeForwardPassOps(
model: ModelHelper,
model_id: str,
input_blob: str,
output_blob: str,
weights: List[str],
biases: List[str],
activations: List[str],
layers: List[int],
dropout_ratio: float,
is_test: bool = False,
) -> None:
"""
Performs a forward pass of a multi-layer perceptron.
:param model: The ModelHelper object whose net will execute this pass
:param model_id: A unique string for this model that is used to hold
activation levels
:param input_blob: The blob containing the input data
:param output_blob: The blob where the output data will be placed
:param weights: A list of blobs containing the weights
:param biases: A list of blobs containing the bias nodes
:param activations: A list of strings describing the activation functions
Currently only 'linear' and 'relu' are supported
:param layers: A list of integers describing the layer sizes
:param dropout_ratio: The fraction of nodes to drop out during training.
:param is_test: Indicates whether or not this forward pass should skip
node dropout.
"""
model.net.NanCheck([input_blob], [input_blob])
num_layer_connections = len(layers) - 1
for x in six.moves.range(num_layer_connections):
inputs = None
outputs = None
if x == 0:
inputs = input_blob
else:
inputs = "ModelState_" + str(x) + "_" + model_id
if x + 1 == num_layer_connections:
outputs = output_blob
else:
outputs = "ModelState_" + str(x + 1) + "_" + model_id
activation = activations[x]
dim_in = layers[x]
dim_out = layers[x + 1]
weight_name = weights[x]
bias_name = biases[x]
brew.fc_explicit_param_names( # type: ignore
model,
inputs,
outputs,
dim_in=dim_in,
dim_out=dim_out,
bias_name=bias_name,
weight_name=weight_name,
weight_init=("GivenTensorFill", {
'values': workspace.FetchBlob(weight_name)
}),
bias_init=("GivenTensorFill", {
'values': workspace.FetchBlob(bias_name)
}))
if activation == 'relu':
brew.relu(model, outputs, outputs)
elif activation == 'linear':
pass
else:
raise Exception("Unknown activation function")
if dropout_ratio > 0.01:
brew.dropout(model,
outputs,
outputs,
ratio=dropout_ratio,
is_test=is_test)
model.net.NanCheck([output_blob], [output_blob])
def Dropout(self, *args, **kwargs):
return brew.dropout(self,
*args,
order=self.order,
use_cudnn=self.use_cudnn,
**kwargs)
def VGG_Net(model, loss_scale):
#----- 3 x 224 x 224 --> 64 x 224 x 224 -----#
conv1_1 = brew.conv(model,
'data',
'conv1_1',
3,
64,
3,
pad=1,
weight_init=('GaussianFill', {
'mean': 0.0,
'std': 1e-2
}))
relu1_1 = brew.relu(model, conv1_1, 'relu1_1')
#----- 64 x 224 x 224 --> 64 x 224 x 224 -----#
conv1_2 = brew.conv(model,
relu1_1,
'conv1_2',
64,
64,
3,
pad=1,
weight_init=('GaussianFill', {
'mean': 0.0,
'std': 1e-2
}))
relu1_2 = brew.relu(model, conv1_2, 'relu1_2')
#----- 64 x 224 x 224 --> 64 x 112 x 112 -----#
pool1 = brew.max_pool(model, relu1_2, 'pool1', kernel=2, stride=2)
#----- 64 x 112 x 112 --> 128 x 112 x 112 -----#
conv2_1 = brew.conv(model,
pool1,
'conv2_1',
64,
128,
3,
pad=1,
weight_init=('GaussianFill', {
'mean': 0.0,
'std': 1e-2
}))
relu2_1 = brew.relu(model, conv2_1, 'relu2_1')
#----- 128 x 112 x 112 --> 128 x 112 x 112 -----#
conv2_2 = brew.conv(model,
relu2_1,
'conv2_2',
128,
128,
3,
pad=1,
weight_init=('GaussianFill', {
'mean': 0.0,
'std': 1e-2
}))
relu2_2 = brew.relu(model, conv2_2, 'relu2_2')
#----- 128 x 112 x 112 --> 128 x 56 x 56 -----#
pool2 = brew.max_pool(model, relu2_2, 'pool2', kernel=2, stride=2)
#----- 128 x 56 x 56 --> 256 x 56 x 56 -----#
conv3_1 = brew.conv(model,
pool2,
'conv3_1',
128,
256,
3,
pad=1,
weight_init=('GaussianFill', {
'mean': 0.0,
'std': 1e-2
}))
relu3_1 = brew.relu(model, conv3_1, 'relu3_1')
#----- 256 x 56 x 56 --> 256 x 56 x 56 -----#
conv3_2 = brew.conv(model,
relu3_1,
'conv3_2',
256,
256,
3,
pad=1,
weight_init=('GaussianFill', {
'mean': 0.0,
'std': 1e-2
}))
relu3_2 = brew.relu(model, conv3_2, 'relu3_2')
#----- 256 x 56 x 56 --> 256 x 56 x 56 -----#
conv3_3 = brew.conv(model,
relu3_2,
'conv3_3',
256,
256,
3,
pad=1,
weight_init=('GaussianFill', {
'mean': 0.0,
'std': 1e-2
}))
relu3_3 = brew.relu(model, conv3_3, 'relu3_3')
#----- 256 x 56 x 56 --> 256 x 28 x 28 -----#
pool3 = brew.max_pool(model, relu3_3, 'pool3', kernel=2, stride=2)
#----- 256 x 28 x 28 --> 512 x 28 x 28 -----#
conv4_1 = brew.conv(model,
pool3,
'conv4_1',
256,
512,
3,
pad=1,
weight_init=('GaussianFill', {
'mean': 0.0,
'std': 1e-2
}))
relu4_1 = brew.relu(model, conv4_1, 'relu4_1')
#----- 512 x 28 x 28 --> 512 x 28 x 28 -----#
conv4_2 = brew.conv(model,
relu4_1,
'conv4_2',
512,
512,
3,
pad=1,
weight_init=('GaussianFill', {
'mean': 0.0,
'std': 1e-2
}))
relu4_2 = brew.relu(model, conv4_2, 'relu4_2')
#----- 512 x 28 x 28 --> 512 x 28 x 28 -----#
conv4_3 = brew.conv(model,
relu4_2,
'conv4_3',
512,
512,
3,
pad=1,
weight_init=('GaussianFill', {
'mean': 0.0,
'std': 1e-2
}))
relu4_3 = brew.relu(model, conv4_3, 'relu4_3')
#----- 512 x 28 x 28 --> 512 x 14 x 14 -----#
pool4 = brew.max_pool(model, relu4_3, 'pool4', kernel=2, stride=2)
#----- 512 x 14 x 14 --> 512 x 14 x 14 -----#
conv5_1 = brew.conv(model,
pool4,
'conv5_1',
512,
512,
3,
pad=1,
weight_init=('GaussianFill', {
'mean': 0.0,
'std': 1e-2
}))
relu5_1 = brew.relu(model, conv5_1, 'relu5_1')
#----- 512 x 14 x 14 --> 512 x 14 x 14 -----#
conv5_2 = brew.conv(model,
relu5_1,
'conv5_2',
512,
512,
3,
pad=1,
weight_init=('GaussianFill', {
'mean': 0.0,
'std': 1e-2
}))
relu5_2 = brew.relu(model, conv5_2, 'relu5_2')
#----- 512 x 14 x 14 --> 512 x 14 x 14 -----#
conv5_3 = brew.conv(model,
relu5_2,
'conv5_3',
512,
512,
3,
pad=1,
weight_init=('GaussianFill', {
'mean': 0.0,
'std': 1e-2
}))
relu5_3 = brew.relu(model, conv5_3, 'relu5_3')
#----- 512 x 14 x 14 --> 512 x 7 x 7 -----#
pool5 = brew.max_pool(model, relu5_3, 'pool5', kernel=2, stride=2)
fc6 = brew.fc(model, pool5, 'fc6', 25088, 4096)
relu6 = brew.relu(model, fc6, 'relu6')
drop6 = brew.dropout(model, relu6, 'drop6', ratio=0.5, is_test=0)
fc7 = brew.fc(model, drop6, 'fc7', 4096, 4096)
relu7 = brew.relu(model, fc7, 'relu7')
drop7 = brew.dropout(model, relu7, 'drop7', ratio=0.5, is_test=0)
fc8 = brew.fc(model, drop7, 'fc8', 4096, 2622)
softmax = brew.softmax(model, fc8, 'softmax')
xent = model.LabelCrossEntropy([softmax, 'label'], 'xent')
# compute the expected loss
loss = model.AveragedLoss(xent, "loss")
# track the accuracy of the model
AddAccuracy(model, softmax)
loss = model.Scale(loss, "loss", scale=loss_scale)
return [loss]
def forward_pass_builder(self, model, loss_scale=1.0):
v = 'data' # 3x299x299
#
conv1 = self.conv_factory(model, v, 3, num_filters=32, kernel=3, stride=2, name='conv1') #32x149x149
conv2 = self.conv_factory(model, conv1, 32, 32, kernel=3, name='conv2') #32x147x147
conv3 = self.conv_factory(model, conv2, 32, 64, kernel=3, pad=1, name='conv3') #64x147x147
pool1 = brew.max_pool(model, conv3, 'pool1', kernel=3, stride=2) #64x73x73
#
conv4r = self.conv_factory(model, pool1, 64, 80, kernel=1, name='conv4_reduce') #80x73x73
conv4 = self.conv_factory(model, conv4r, 80, 192, kernel=3, name='conv4') #192x71x71
pool2 = brew.max_pool(model, conv4, 'pool2', kernel=3, stride=2) #192x35x35
#
conv5 = [None, None, None, None]
conv5[0] = self.conv_factory(model, pool2, 192, 96, kernel=1, name='conv5_1_1') #96x35x35
conv5[1] = self.conv_factory(model, pool2, 192, 48, kernel=1, name='conv5_2_1') #48x35x35
conv5[1] = self.conv_factory(model, conv5[1], 48, 64, kernel=5, pad=2, name='conv5_2_2') #64x35x35
conv5[2] = self.conv_factory(model, pool2, 192, 64, kernel=1, name='conv5_3_1') #64x35x35
conv5[2] = self.conv_factory(model, conv5[2], 64, 96, kernel=3, pad=1, name='conv5_3_2') #96x35x35
conv5[2] = self.conv_factory(model, conv5[2], 96, 96, kernel=3, pad=1, name='conv5_3_3') #96x35x35
conv5[3] = brew.average_pool(model, pool2, 'conv5_4_1_pool', kernel=3, stride=1, pad=1) #192x35x35
conv5[3] = self.conv_factory(model, conv5[3], 192, 64, kernel=1, name='conv5_4_2') #64x35x35
conv5 = brew.concat(model, conv5, blob_out='conv5') #320x35x35
#
block35 = conv5
for i in range(10):
block35 = self.block35(model, block35, num_in_channels=320,
scale=0.17, name='inception_resnet_v2_a%d'%(i+1)) #320x35x35
# ra - reduction_a
ra = [None, None, None]
ra[0] = self.conv_factory(model, block35, 320, 384, kernel=3, stride=2, name='ra_1_1') #384x17x17
ra[1] = self.conv_factory(model, block35, 320, 256, kernel=1, name='ra_2_1') #256x35x35
ra[1] = self.conv_factory(model, ra[1], 256, 256, kernel=3, pad=1, name='ra_2_2') #256x35x35
ra[1] = self.conv_factory(model, ra[1], 256, 384, kernel=3, stride=2, name='ra_2_3') #384x17x17
ra[2] = brew.max_pool(model, block35, 'ra_3_1_pool', kernel=3, stride=2) #320x17x17
ra = brew.concat(model, ra, blob_out='ra') #1088x35x35
#
block17 = ra
for i in range(20):
block17 = self.block17(model, block17, num_in_channels=1088,
scale=0.1, name='inception_resnet_v2_b%d'%(i+1)) #1088x35x35
# rb -reduction_b
rb = [None, None, None, None]
rb[0] = self.conv_factory(model, block17, 1088, 256, kernel=1, name='rb_1_1') #256x17x17
rb[0] = self.conv_factory(model, rb[0], 256, 384, kernel=3, stride=2, name='rb_1_2') #384x8x8
rb[1] = self.conv_factory(model, block17, 1088, 256, kernel=1, name='rb_2_1') #256x17x17
rb[1] = self.conv_factory(model, rb[1], 256, 288, kernel=3, stride=2, name='rb_2_2') #288x8x8
rb[2] = self.conv_factory(model, block17, 1088, 256, kernel=1, name='rb_3_1') #256x17x17
rb[2] = self.conv_factory(model, rb[2], 256, 288, kernel=3, pad=1, name='rb_3_2') #288x17x17
rb[2] = self.conv_factory(model, rb[2], 288, 320, kernel=3, stride=2, name='rb_3_3') #320x8x8
rb[3] = brew.max_pool(model, block17, 'rb_4_1_pool', kernel=3, stride=2) #1088x8x8
rb = brew.concat(model, rb, blob_out='rb') #2080x8x8
#
block8 = rb
for i in range(9):
block8 = self.block8(model, block8, num_in_channels=2080,
scale=0.2, name='inception_resnet_v2_c%d'%(i+1)) #2080x8x8
block8 = self.block8(model, block8, num_in_channels=2080, relu=False,
name='inception_resnet_v2_c10') #2080x8x8
#
conv6 = self.conv_factory(model, block8, 2080, 1536, kernel=1, name='conv6') #1536x8x8
pool8 = brew.average_pool(model, conv6, 'pool8', kernel=8, global_pool=True) #1536x1x1
drop8 = brew.dropout(model, pool8, 'dtop8', ratio=0.2, #1536x1x1
is_test=(self.phase == 'inference'))
if not self.__run_with_resnet50_trainer:
return self.add_head_nodes(model, drop8, 1536, 'classifier', loss_scale=loss_scale)
else:
return brew.fc(model, drop8, 'classifier', dim_in=1536, dim_out=self.num_classes)
def forward_pass_builder(self, model, loss_scale=1.0):
"""
This function adds the operators, layers to the network. It should return a list
of loss-blobs that are used for computing the loss gradient. This function is
also passed an internally calculated loss_scale parameter that is used to scale
your loss to normalize for the number of GPUs. Signature: function(model, loss_scale)
"""
self.counts = defaultdict(lambda: 0)
is_inference = self.phase == 'inference'
v = 'data'
# Input conv modules
v = self.conv(model,
'conv',
v,
input_depth=3,
num_filters=32,
kernel=3,
stride=2,
pad=0,
is_inference=is_inference)
v = self.conv(model,
'conv',
v,
input_depth=32,
num_filters=32,
kernel=3,
stride=1,
pad=0,
is_inference=is_inference)
v = self.conv(model,
'conv',
v,
input_depth=32,
num_filters=64,
kernel=3,
stride=1,
pad=1,
is_inference=is_inference)
# Stem modules
v = self.inception_v4_sa(model,
inputs=v,
input_depth=64,
is_inference=is_inference)
v = self.inception_v4_sb(model,
inputs=v,
input_depth=160,
is_inference=is_inference)
v = self.inception_v4_sc(model,
inputs=v,
input_depth=192,
is_inference=is_inference)
# Four Type A modules
for _ in xrange(4):
v = self.inception_v4_a(model,
inputs=v,
input_depth=384,
is_inference=is_inference)
# One Type A Reduction module
v = self.inception_v4_ra(model,
inputs=v,
input_depth=384,
k=192,
l=224,
m=256,
n=384,
is_inference=is_inference)
# Seven Type B modules
for _ in xrange(7):
v = self.inception_v4_b(model,
inputs=v,
input_depth=1024,
is_inference=is_inference)
# One Type B Reduction module
v = self.inception_v4_rb(model,
inputs=v,
input_depth=1024,
is_inference=is_inference)
# Three Type C modules
for _ in xrange(3):
v = self.inception_v4_c(model,
inputs=v,
input_depth=1536,
is_inference=is_inference)
# Final global pooling
v = brew.average_pool(model,
v,
blob_out='pool',
kernel=8,
stride=1,
pad=0)
v = brew.dropout(model, v, 'drop', ratio=0.2, is_test=is_inference)
# And classifier
return self.add_head_nodes(model,
v,
1536,
'classifier',
loss_scale=loss_scale)
def Dropout(self, *args, **kwargs):
return brew.dropout(
self, *args, order=self.order, use_cudnn=self.use_cudnn, **kwargs
)
def create_r3d(
model,
data,
num_input_channels,
num_labels,
label=None,
is_test=False,
use_full_ft=True,
no_bias=0,
final_spatial_kernel=7,
final_temporal_kernel=1,
model_depth=18,
block_type='3d',
transformation_type='simple_block',
channel_multiplier=1.0,
bottleneck_multiplier=1.0,
use_dropout=False,
conv1_temporal_stride=1,
conv1_temporal_kernel=3,
spatial_bn_mom=0.9,
clip_length=8,
use_shuffle=False,
use_convolutional_pred=False,
use_pool1=False,
):
assert conv1_temporal_kernel == 3 or conv1_temporal_kernel == 5
if not use_full_ft:
is_test = True
# conv1 + maxpool
if block_type != '2.5d' and block_type != '2.5d-sep':
model.ConvNd(
data,
'conv1',
num_input_channels,
64,
[conv1_temporal_kernel, 7, 7],
weight_init=("MSRAFill", {}),
strides=[conv1_temporal_stride, 2, 2],
pads=[1 if conv1_temporal_kernel == 3 else 2, 3, 3] * 2,
no_bias=no_bias
)
else:
model.ConvNd(
data,
'conv1_middle',
num_input_channels,
45,
[1, 7, 7],
weight_init=("MSRAFill", {}),
strides=[1, 2, 2],
pads=[0, 3, 3] * 2,
no_bias=no_bias
)
model.SpatialBN(
'conv1_middle',
'conv1_middle_spatbn_relu',
45,
epsilon=1e-3,
momentum=spatial_bn_mom,
is_test=is_test
)
model.Relu('conv1_middle_spatbn_relu', 'conv1_middle_spatbn_relu')
model.ConvNd(
'conv1_middle_spatbn_relu',
'conv1',
45,
64,
[conv1_temporal_kernel, 1, 1],
weight_init=("MSRAFill", {}),
strides=[conv1_temporal_stride, 1, 1],
pads=[1 if conv1_temporal_kernel == 3 else 2, 0, 0] * 2,
no_bias=no_bias
)
model.SpatialBN(
'conv1',
'conv1_spatbn_relu',
64,
epsilon=1e-3,
momentum=spatial_bn_mom,
is_test=is_test
)
last_conv1 = model.Relu('conv1_spatbn_relu', 'conv1_spatbn_relu')
if use_pool1:
last_conv1 = model.MaxPool(
'conv1_spatbn_relu',
'pool1',
kernels=[1, 3, 3],
strides=[1, 2, 2],
pads=[0, 1, 1] * 2,
)
(n1, n2, n3, n4) = BLOCK_CONFIG[model_depth]
# Residual blocks...
builder = VideoModelBuilder(model, last_conv1, no_bias=no_bias,
is_test=is_test, spatial_bn_mom=spatial_bn_mom)
if transformation_type == 'simple_block':
transformation = builder.add_simple_block
elif transformation_type == 'bottleneck':
transformation = builder.add_bottleneck
else:
print('Unknown transformation type...')
if model_depth <= 34:
filter_config = SHALLOW_FILTER_CONFIG
else:
filter_config = DEEP_FILTER_CONFIG
filter_config = np.multiply(
filter_config, channel_multiplier).astype(np.int)
# conv_2x
transformation(
64, filter_config[0][0],
int(filter_config[0][1] * bottleneck_multiplier),
block_type=block_type,
use_shuffle=use_shuffle)
for _ in range(n1 - 1):
transformation(
filter_config[0][0], filter_config[0][0],
int(filter_config[0][1] * bottleneck_multiplier),
block_type=block_type,
use_shuffle=use_shuffle)
# conv_3x
transformation(
filter_config[0][0], filter_config[1][0],
int(filter_config[1][1] * bottleneck_multiplier),
down_sampling=True,
block_type=block_type,
use_shuffle=use_shuffle)
for _ in range(n2 - 1):
transformation(
filter_config[1][0], filter_config[1][0],
int(filter_config[1][1] * bottleneck_multiplier),
block_type=block_type,
use_shuffle=use_shuffle)
# conv_4x
if clip_length < 4:
transformation(
filter_config[1][0], filter_config[2][0],
int(filter_config[2][1] * bottleneck_multiplier),
down_sampling=True,
down_sampling_temporal=False,
block_type=block_type,
use_shuffle=use_shuffle)
else:
transformation(
filter_config[1][0], filter_config[2][0],
int(filter_config[2][1] * bottleneck_multiplier),
down_sampling=True,
block_type=block_type,
use_shuffle=use_shuffle)
for _ in range(n3 - 1):
transformation(
filter_config[2][0], filter_config[2][0],
int(filter_config[2][1] * bottleneck_multiplier),
block_type=block_type,
use_shuffle=use_shuffle)
# conv_5x
if clip_length < 8:
transformation(
filter_config[2][0], filter_config[3][0],
int(filter_config[3][1] * bottleneck_multiplier),
down_sampling=True,
down_sampling_temporal=False,
block_type=block_type,
use_shuffle=use_shuffle)
else:
transformation(
filter_config[2][0], filter_config[3][0],
int(filter_config[3][1] * bottleneck_multiplier),
down_sampling=True, block_type=block_type,
use_shuffle=use_shuffle)
for _ in range(n4 - 1):
transformation(
filter_config[3][0], filter_config[3][0],
int(filter_config[3][1] * bottleneck_multiplier),
block_type=block_type,
use_shuffle=use_shuffle)
# Final layers
model.AveragePool(
builder.prev_blob,
'final_avg',
kernels=[
final_temporal_kernel,
final_spatial_kernel,
final_spatial_kernel
],
strides=[1, 1, 1],
)
if use_dropout:
dropout = brew.dropout(model, 'final_avg', 'dropout', is_test=is_test)
else:
dropout = 'final_avg'
if not use_full_ft:
dropout = model.StopGradient(dropout, dropout)
if use_convolutional_pred:
assert is_test
last_out = model.ConvNd(
dropout,
'last_out_L{}'.format(num_labels),
filter_config[3][0],
num_labels,
[1, 1, 1],
weight_init=("MSRAFill", {}),
strides=[1, 1, 1],
pads=[0, 0, 0] * 2,
no_bias=False
)
else:
last_out = model.FC(
dropout, 'last_out_L{}'.format(num_labels),
filter_config[3][0],
num_labels
)
return last_out
def Add_Action_Tufts_Model(model,
num_classes,
image_height,
image_width,
image_channels,
is_test=0):
################################## Block 1 ############################
# Convolutional layer 1
# conv1_1 = brew.conv(model, 'data', 'conv1_1', dim_in=image_channels, dim_out=64, kernel=3, stride=2, pad=0)
# h,w = update_dims(height=image_height, width=image_width, kernel=3, stride=2, pad=0)
# ReLU layer 1
# relu1_1 = brew.relu(model, conv1_1, 'relu1_1')
# Batch normalization layer 1
# bn1_1 = brew.spatial_bn(model, relu1_1, 'bn1_1', dim_in=64, epsilon=1e-3, momentum=0.1, is_test=is_test)
# Drop out with p=0.25
# dropout1_1 = brew.dropout(model, bn1_1, 'dropout1_1', ratio=0.25, is_test=is_test)
# Convolutional layer 2
# conv1_2 = brew.conv(model, dropout1_1, 'conv1_2', dim_in=64, dim_out=64, kernel=3, stride=1, pad=0)
# h,w = update_dims(height=h, width=w, kernel=3, stride=1, pad=0)
# ReLU layer 1
# relu1_2 = brew.relu(model, conv1_2, 'relu1_2')
# Batch normalization layer 1
# bn1_2 = brew.spatial_bn(model, relu1_2, 'bn1_2', dim_in=64, epsilon=1e-3, momentum=0.1, is_test=is_test)
# Drop out with p=0.25
# dropout1_2 = brew.dropout(model, bn1_2, 'dropout1_2', ratio=0.25, is_test=is_test)
##################################### Block 2 ##########################
# Convolutional layer 3
conv2_1 = brew.conv(model,
'data',
'conv2_1',
dim_in=image_channels,
dim_out=128,
kernel=3,
stride=2,
pad=0)
h, w = update_dims(height=image_height,
width=image_width,
kernel=3,
stride=2,
pad=0)
# ReLU layer 1
relu2_1 = brew.relu(model, conv2_1, 'relu2_1')
# Batch normalization layer 1
bn2_1 = brew.spatial_bn(model,
relu2_1,
'bn2_1',
dim_in=128,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
# Drop out with p=0.25
dropout2_1 = brew.dropout(model,
bn2_1,
'dropout2_1',
ratio=0.25,
is_test=is_test)
# Convolutional layer 4
conv2_2 = brew.conv(model,
dropout2_1,
'conv2_2',
dim_in=128,
dim_out=128,
kernel=3,
stride=1,
pad=0)
h, w = update_dims(height=h, width=w, kernel=3, stride=1, pad=0)
# ReLU layer 1
relu2_2 = brew.relu(model, conv2_2, 'relu2_2')
# Batch normalization layer 1
bn2_2 = brew.spatial_bn(model,
relu2_2,
'bn2_2',
dim_in=128,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
# Drop out with p=0.25
dropout2_2 = brew.dropout(model,
bn2_2,
'dropout2_2',
ratio=0.25,
is_test=is_test)
##################################### Block 3 ############################
# Convolutional layer 5
conv3_1 = brew.conv(model,
dropout2_2,
'conv3_1',
dim_in=128,
dim_out=256,
kernel=3,
stride=2,
pad=0)
h, w = update_dims(height=h, width=w, kernel=3, stride=2, pad=0)
# ReLU layer 1
relu3_1 = brew.relu(model, conv3_1, 'relu3_1')
# Batch normalization layer 1
bn3_1 = brew.spatial_bn(model,
relu3_1,
'bn3_1',
dim_in=256,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
# Drop out with p=0.25
dropout3_1 = brew.dropout(model,
bn3_1,
'dropout3_1',
ratio=0.25,
is_test=is_test)
# Convolutional layer 4
conv3_2 = brew.conv(model,
dropout3_1,
'conv3_2',
dim_in=256,
dim_out=256,
kernel=3,
stride=1,
pad=0)
h, w = update_dims(height=h, width=w, kernel=3, stride=1, pad=0)
# ReLU layer 1
relu3_2 = brew.relu(model, conv3_2, 'relu3_2')
# Batch normalization layer 1
bn3_2 = brew.spatial_bn(model,
relu3_2,
'bn3_2',
dim_in=256,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
# Drop out with p=0.25
dropout3_2 = brew.dropout(model,
bn3_2,
'dropout3_2',
ratio=0.25,
is_test=is_test)
# Global average pooling
pool1 = brew.average_pool(model, dropout3_2, 'pool1', global_pooling=True)
# Fully connected layers
pred = brew.fc(model, pool1, 'fc1', dim_in=256, dim_out=num_classes)
# Softmax layer
softmax, loss = model.SoftmaxWithLoss([pred, 'label'], ['softmax', 'loss'])
brew.accuracy(model, [softmax, 'label'], 'accuracy')
model.net.MultiClassAccuracy([softmax, 'label'],
['accuracy_per_class', 'amount_per_class'])
return [loss]
def create_alexnet(
model,
data,
num_input_channels,
num_labels,
is_test=False,
):
conv1 = brew.conv(
model,
data,
"conv1",
num_input_channels, # dim_in
64, # dim_out
11, # kernel
('XavierFill', {}),
('ConstantFill', {}),
stride=4,
pad=2)
relu1 = brew.relu(model, conv1, "conv1")
norm1 = brew.lrn(model, relu1, "norm1", size=5, alpha=0.0001, beta=0.75)
pool1 = brew.max_pool(model, norm1, "pool1", kernel=3, stride=2)
conv2 = brew.conv(model,
pool1,
"conv2",
64,
192,
5, ('XavierFill', {}), ('ConstantFill', {}),
pad=2)
relu2 = brew.relu(model, conv2, "conv2")
norm2 = brew.lrn(model, relu2, "norm2", size=5, alpha=0.0001, beta=0.75)
pool2 = brew.max_pool(model, norm2, "pool2", kernel=3, stride=2)
conv3 = brew.conv(model,
pool2,
"conv3",
192,
384,
3, ('XavierFill', {}), ('ConstantFill', {}),
pad=1)
relu3 = brew.relu(model, conv3, "conv3")
conv4 = brew.conv(model,
relu3,
"conv4",
384,
256,
3, ('XavierFill', {}), ('ConstantFill', {}),
pad=1)
relu4 = brew.relu(model, conv4, "conv4")
conv5 = brew.conv(model,
relu4,
"conv5",
256,
256,
3, ('XavierFill', {}), ('ConstantFill', {}),
pad=1)
relu5 = brew.relu(model, conv5, "conv5")
pool5 = brew.max_pool(model, relu5, "pool5", kernel=3, stride=2)
fc6 = brew.fc(model, pool5, "fc6", 256 * 6 * 6, 4096, ('XavierFill', {}),
('ConstantFill', {}))
relu6 = brew.relu(model, fc6, "fc6")
dropout1 = brew.dropout(model,
relu6,
'dropout1',
ratio=0.5,
is_test=is_test)
fc7 = brew.fc(model, dropout1, "fc7", 4096, 4096, ('XavierFill', {}),
('ConstantFill', {}))
relu7 = brew.relu(model, fc7, "fc7")
dropout2 = brew.dropout(model,
relu7,
'dropout2',
ratio=0.5,
is_test=is_test)
fc8 = brew.fc(model, dropout2, "fc8", 4096, num_labels, ('XavierFill', {}),
('ConstantFill', {}))
# pred = brew.softmax(model, fc8, "pred")
# xent = model.net.LabelCrossEntropy([pred, "label"], "xent")
# model.net.AveragedLoss(xent, "loss")
return fc8
def alexnet():
model = ModelHelper(name="r", arg_scope={"order": "NCHW", "is_test": True})
conv1 = brew.conv(model,
"data",
"conv1",
3,
64,
11, ('XavierFill', {}), ('ConstantFill', {}),
stride=4,
pad=2)
relu1 = brew.relu(model, conv1, "conv1")
pool1 = brew.max_pool(model,
relu1,
"pool1",
kernel=3,
stride=2,
pad=0,
legacy_pad=3)
lrn1 = brew.lrn(model,
pool1,
"pool1_lrn",
size=5,
alpha=1.0e-4,
beta=0.75,
bias=1.0)
conv2 = brew.conv(model,
lrn1,
"conv2",
64,
192,
5, ('XavierFill', {}), ('ConstantFill', {}),
pad=2)
relu2 = brew.relu(model, conv2, "conv2")
pool2 = brew.max_pool(model, relu2, "pool2", kernel=3, stride=2)
lrn2 = brew.lrn(model,
pool2,
"pool2_lrn",
size=5,
alpha=1.0e-4,
beta=0.75,
bias=1.0)
conv3 = brew.conv(model,
lrn2,
"conv3",
192,
384,
3, ('XavierFill', {}), ('ConstantFill', {}),
pad=1)
relu3 = brew.relu(model, conv3, "conv3")
conv4 = brew.conv(model,
relu3,
"conv4",
384,
256,
3, ('XavierFill', {}), ('ConstantFill', {}),
pad=1)
relu4 = brew.relu(model, conv4, "conv4")
conv5 = brew.conv(model,
relu4,
"conv5",
256,
256,
3, ('XavierFill', {}), ('ConstantFill', {}),
pad=1)
relu5 = brew.relu(model, conv5, "conv5")
pool5 = brew.max_pool(model, relu5, "pool5", kernel=3, stride=2)
fc6 = brew.fc(model, pool5, "fc6", 256 * 6 * 6, 4096, ('XavierFill', {}),
('ConstantFill', {}))
relu6 = brew.relu(model, fc6, "fc6")
fc7 = brew.fc(model, relu6, "fc7", 4096, 4096, ('XavierFill', {}),
('ConstantFill', {}))
relu7 = brew.relu(model, fc7, "fc7")
drop7 = brew.dropout(model, relu7, "fc7_dropout", is_test=1, ratio=0.5)
fc8 = brew.fc(model, drop7, "fc8", 4096, 1000, ('XavierFill', {}),
('ConstantFill', {}))
relu8 = brew.relu(model, fc8, "fc8")
_ = brew.dropout(model, relu8, "fc8_dropout", is_test=1, ratio=0.5)
return model, [(1, 3, 224, 224)]
def forward_pass_builder(self, model, loss_scale=1.0):
"""
This function adds the operators, layers to the network. It should return
a list of loss-blobs that are used for computing the loss gradient. This
function is also passed an internally calculated loss_scale parameter that
is used to scale your loss to normalize for the number of GPUs.
Signature: function(model, loss_scale)
"""
v = 'data'
v = conv_factory(model,
v,
self.input_shape[0],
64,
kernel=7,
stride=2,
pad=3,
name="conv1/7x7_s2")
v = brew.max_pool(model, v, 'pool1/3x3_s2', kernel=3, stride=2)
v = brew.lrn(model, v, 'pool1/norm1', size=5, alpha=0.0001, beta=0.75)
v = conv_factory(model,
v,
64,
64,
kernel=1,
stride=1,
name="conv2/3x3_reduce")
v = conv_factory(model,
v,
64,
192,
kernel=3,
stride=1,
pad=1,
name="conv2/3x3")
v = brew.lrn(model, v, 'conv2/norm2', size=5, alpha=0.0001, beta=0.75)
v = brew.max_pool(model, v, 'pool2/3x3_s2', kernel=3, stride=2)
v = inception_factory(model,
v,
192,
64,
96,
128,
16,
32,
32,
name="inception_3a")
v = inception_factory(model,
v,
256,
128,
128,
192,
32,
96,
64,
name="inception_3b")
v = brew.max_pool(model, v, 'pool3/3x3_s2', kernel=3, stride=2)
v = inception_factory(model,
v,
480,
192,
96,
208,
16,
48,
64,
name="inception_4a")
v = inception_factory(model,
v,
512,
160,
112,
224,
24,
64,
64,
name="inception_4b")
v = inception_factory(model,
v,
512,
128,
128,
256,
24,
64,
64,
name="inception_4c")
v = inception_factory(model,
v,
512,
112,
144,
288,
32,
64,
64,
name="inception_4d")
v = inception_factory(model,
v,
528,
256,
160,
320,
32,
128,
128,
name="inception_4e")
v = brew.max_pool(model, v, 'pool4/3x3_s2', kernel=3, stride=2, pad=1)
v = inception_factory(model,
v,
832,
256,
160,
320,
32,
128,
128,
name="inception_5a")
v = inception_factory(model,
v,
832,
384,
192,
384,
48,
128,
128,
name="inception_5b")
v = brew.average_pool(model, v, 'pool5/7x7_s1', kernel=7, stride=1)
v = brew.dropout(model,
v,
'pool5/drop_7x7_s1',
ratio=0.5,
is_test=(self.phase == 'inference'))
return self.add_head_nodes(model,
v,
1024,
'classifier',
loss_scale=loss_scale)
def create_av_resnet(
model,
data,
num_input_channels,
num_labels,
acoustic_data="logmels",
label=None,
is_test=False,
use_full_ft=True,
no_bias=0,
final_spatial_kernel=7,
final_temporal_kernel=1,
model_depth=18,
block_type='3d',
transformation_type='simple_block',
bottleneck_multiplier=1.0,
channel_multiplier=1.0,
use_dropout=False,
conv1_temporal_stride=1,
conv1_temporal_kernel=3,
spatial_bn_mom=0.9,
clip_length=8,
use_convolutional_pred=False,
use_pool1=False,
audio_input_3d=False,
g_blend=False,
):
# sanity checking of model params
assert conv1_temporal_kernel == 3 or conv1_temporal_kernel == 5
# conv1 + maxpool for visual model
if block_type != '2.5d' and block_type != '2.5d-sep':
model.ConvNd(
data,
'v_conv1',
num_input_channels,
64,
[conv1_temporal_kernel, 7, 7],
weight_init=("MSRAFill", {}),
strides=[conv1_temporal_stride, 2, 2],
pads=[1 if conv1_temporal_kernel == 3 else 2, 3, 3] * 2,
no_bias=no_bias
)
else:
model.ConvNd(
data,
'v_conv1_middle',
num_input_channels,
45,
[1, 7, 7],
weight_init=("MSRAFill", {}),
strides=[1, 2, 2],
pads=[0, 3, 3] * 2,
no_bias=no_bias
)
model.SpatialBN(
'v_conv1_middle',
'v_conv1_middle_spatbn_relu',
45,
epsilon=1e-3,
momentum=spatial_bn_mom,
is_test=is_test
)
model.Relu('v_conv1_middle_spatbn_relu', 'v_conv1_middle_spatbn_relu')
model.ConvNd(
'v_conv1_middle_spatbn_relu',
'v_conv1',
45,
64,
[conv1_temporal_kernel, 1, 1],
weight_init=("MSRAFill", {}),
strides=[conv1_temporal_stride, 1, 1],
pads=[1 if conv1_temporal_kernel == 3 else 2, 0, 0] * 2,
no_bias=no_bias
)
model.SpatialBN(
'v_conv1',
'v_conv1_spatbn_relu',
64,
epsilon=1e-3,
momentum=spatial_bn_mom,
is_test=is_test
)
v_conv1 = model.Relu('v_conv1_spatbn_relu', 'v_conv1_spatbn_relu')
if use_pool1:
v_conv1 = model.MaxPool(
'v_conv1_spatbn_relu',
'pool1',
kernels=[1, 3, 3],
strides=[1, 2, 2],
pads=[0, 1, 1] * 2,
)
# conv1 equivalent of audio model
# it is approximated by a bunch of 3x3 kernels
if audio_input_3d:
acoustic_data_swap = model.NCHW2NHWC(
acoustic_data, acoustic_data + "_NHWC")
acoustic_data_greyscale = model.ReduceBackMean(
acoustic_data_swap,
acoustic_data_swap + '_c_pool',
num_reduce_dim=1)
acoustic_data = acoustic_data_greyscale
a_conv1 = model.Conv(acoustic_data, 'a_conv1', 1, 16, kernel=3, pad=1)
a_builder = AudioModelBuilder(
model, a_conv1, no_bias=no_bias,
is_test=is_test, spatial_bn_mom=spatial_bn_mom, prefix='a_')
a_builder.add_simple_block(16, 32, down_sampling=True)
a_builder.add_simple_block(32, 32)
a_builder.add_simple_block(32, 32)
(n1, n2, n3, n4) = BLOCK_CONFIG[model_depth]
# Residual blocks...
v_builder = VideoModelBuilder(
model, v_conv1, no_bias=no_bias,
is_test=is_test, spatial_bn_mom=spatial_bn_mom, prefix='v_')
if transformation_type == 'simple_block':
v_transformation = v_builder.add_simple_block
a_transformation = a_builder.add_simple_block
elif transformation_type == 'bottleneck':
v_transformation = v_builder.add_bottleneck
a_transformation = a_builder.add_bottleneck
else:
print('Unknown transformation type...')
if model_depth <= 34:
filter_config = SHALLOW_FILTER_CONFIG
else:
filter_config = DEEP_FILTER_CONFIG
filter_config = np.multiply(
filter_config, channel_multiplier).astype(np.int)
# conv_2x
v_transformation(
64, filter_config[0][0], int(bottleneck_multiplier * filter_config[0][1]),
block_type=block_type)
a_transformation(32, filter_config[0][0], filter_config[0][1],
down_sampling=True)
for _ in range(n1 - 1):
v_transformation(
filter_config[0][0], filter_config[0][0],
int(bottleneck_multiplier * filter_config[0][1]),
block_type=block_type)
a_transformation(
filter_config[0][0], filter_config[0][0], filter_config[0][1])
# conv_3x
v_transformation(
filter_config[0][0], filter_config[1][0],
int(bottleneck_multiplier * filter_config[1][1]),
down_sampling=True, block_type=block_type)
a_transformation(
filter_config[0][0], filter_config[1][0], filter_config[1][1],
down_sampling=True)
for _ in range(n2 - 1):
v_transformation(
filter_config[1][0], filter_config[1][0],
int(bottleneck_multiplier * filter_config[1][1]),
block_type=block_type)
a_transformation(
filter_config[1][0], filter_config[1][0], filter_config[1][1])
# conv_4x
v_transformation(
filter_config[1][0], filter_config[2][0],
int(bottleneck_multiplier * filter_config[2][1]),
down_sampling=True, block_type=block_type)
a_transformation(
filter_config[1][0], filter_config[2][0], filter_config[2][1],
down_sampling=True)
for _ in range(n3 - 1):
v_transformation(
filter_config[2][0], filter_config[2][0],
int(bottleneck_multiplier * filter_config[2][1]),
block_type=block_type)
a_transformation(
filter_config[2][0], filter_config[2][0], filter_config[2][1])
# conv_5x
if clip_length < 8:
v_transformation(
filter_config[2][0], filter_config[3][0],
int(bottleneck_multiplier * filter_config[3][1]),
down_sampling=True, down_sampling_temporal=False,
block_type=block_type)
else:
v_transformation(
filter_config[2][0], filter_config[3][0],
int(bottleneck_multiplier * filter_config[3][1]),
down_sampling=True, block_type=block_type)
a_transformation(
filter_config[2][0], filter_config[3][0], filter_config[3][1],
down_sampling=True)
for _ in range(n4 - 1):
v_transformation(
filter_config[3][0], filter_config[3][0],
int(bottleneck_multiplier * filter_config[3][1]),
block_type=block_type)
a_transformation(
filter_config[3][0], filter_config[3][0], filter_config[3][1])
# Final layers
# final pool for visual model
v_builder.prev_blob = model.AveragePool(
v_builder.prev_blob,
'v_final_avg',
kernels=[
final_temporal_kernel,
final_spatial_kernel,
final_spatial_kernel
],
strides=[1, 1, 1],
)
# final pool for audio model
a_builder.prev_blob = model.MaxPool(
a_builder.prev_blob,
'a_final_avg',
kernels=[4, 2],
stride=1
)
last_a_layer = a_builder.prev_blob
last_v_layer = v_builder.prev_blob
if use_convolutional_pred:
assert is_test
a_last_3D = model.ExpandDims(
last_a_layer, last_a_layer + '_3D', dims=[4]
)
a_last_tile_1 = model.Tile(
a_last_3D,
a_last_3D + '_tiled_1',
tiles=2,
axis=3
)
a_last_tile_2 = model.Tile(
a_last_tile_1,
a_last_3D + '_tiled_2',
tiles=2,
axis=4
)
av_concat = model.Concat(
[last_v_layer, a_last_tile_2],
'av_concat',
axis=1
)
if use_dropout:
dropout = brew.dropout(
model, av_concat, 'dropout', is_test=is_test
)
else:
dropout = av_concat
if not use_full_ft:
dropout = model.StopGradient(dropout, dropout)
dim = 2 * filter_config[3][0]
fc_dim = int(dim / 2)
fc1 = model.ConvNd(
dropout,
'av_fc1',
dim,
fc_dim,
[1, 1, 1],
weight_init=("MSRAFill", {}),
strides=[1, 1, 1],
pads=[0, 0, 0] * 2,
no_bias=False
)
relu1 = brew.relu(model, fc1, fc1)
fc2 = model.ConvNd(
relu1,
'av_fc2',
fc_dim,
fc_dim,
[1, 1, 1],
weight_init=("MSRAFill", {}),
strides=[1, 1, 1],
pads=[0, 0, 0] * 2,
no_bias=False
)
relu2 = brew.relu(model, fc2, fc2)
last_out = model.ConvNd(
relu2,
'last_out_L{}'.format(num_labels),
fc_dim,
num_labels,
[1, 1, 1],
weight_init=("MSRAFill", {}),
strides=[1, 1, 1],
pads=[0, 0, 0] * 2,
no_bias=False
)
return last_out
else:
# reduce to 4D tensor
v_builder.prev_blob = model.Squeeze(
v_builder.prev_blob, 'v_final_avg_squeezed', dims=[4])
last_v_layer = v_builder.prev_blob
av_concat = model.Concat(
[last_v_layer, last_a_layer],
'av_concat',
axis=1
)
if use_dropout:
dropout = brew.dropout(
model, av_concat, 'dropout', is_test=is_test
)
else:
dropout = av_concat
dim = 2 * filter_config[3][0]
fc_dim = int(dim / 2)
fc1 = brew.fc(model, dropout, 'av_fc1', dim, fc_dim)
relu1 = brew.relu(model, fc1, fc1)
fc2 = brew.fc(model, relu1, 'av_fc2', fc_dim, fc_dim)
relu2 = brew.relu(model, fc2, fc2)
last_out = brew.fc(
model, relu2, 'last_out_L{}'.format(num_labels), fc_dim, num_labels)
if g_blend:
a_last_out = brew.fc(
model,
last_a_layer,
'a_last_out_L{}'.format(num_labels),
filter_config[3][0],
num_labels,
)
v_last_out = brew.fc(
model,
last_v_layer,
'v_last_out_L{}'.format(num_labels),
filter_config[3][0],
num_labels,
)
return [a_last_out, v_last_out, last_out]
return last_out
def alexnet():
model = ModelHelper(name="r", arg_scope={"order": "NCHW", "is_test": True})
conv1 = brew.conv(
model,
"data",
"conv1",
3,
64,
11, ('XavierFill', {}), ('ConstantFill', {}),
stride=4,
pad=2
)
relu1 = brew.relu(model, conv1, "conv1")
pool1 = brew.max_pool(model, relu1, "pool1", kernel=3, stride=2, pad=0,
legacy_pad=3)
lrn1 = brew.lrn(
model, pool1, "pool1_lrn", size=5, alpha=1.0e-4, beta=0.75, bias=1.0)
conv2 = brew.conv(
model,
lrn1,
"conv2",
64,
192,
5,
('XavierFill', {}),
('ConstantFill', {}),
pad=2
)
relu2 = brew.relu(model, conv2, "conv2")
pool2 = brew.max_pool(model, relu2, "pool2", kernel=3, stride=2)
lrn2 = brew.lrn(
model, pool2, "pool2_lrn", size=5, alpha=1.0e-4, beta=0.75, bias=1.0)
conv3 = brew.conv(
model,
lrn2,
"conv3",
192,
384,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu3 = brew.relu(model, conv3, "conv3")
conv4 = brew.conv(
model,
relu3,
"conv4",
384,
256,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu4 = brew.relu(model, conv4, "conv4")
conv5 = brew.conv(
model,
relu4,
"conv5",
256,
256,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu5 = brew.relu(model, conv5, "conv5")
pool5 = brew.max_pool(model, relu5, "pool5", kernel=3, stride=2)
fc6 = brew.fc(
model,
pool5, "fc6", 256 * 6 * 6, 4096, ('XavierFill', {}),
('ConstantFill', {})
)
relu6 = brew.relu(model, fc6, "fc6")
fc7 = brew.fc(
model, relu6, "fc7", 4096, 4096, ('XavierFill', {}), ('ConstantFill', {})
)
relu7 = brew.relu(model, fc7, "fc7")
drop7 = brew.dropout(model, relu7, "fc7_dropout", is_test=1, ratio=0.5)
fc8 = brew.fc(
model, drop7, "fc8", 4096, 1000, ('XavierFill', {}), ('ConstantFill', {})
)
relu8 = brew.relu(model, fc8, "fc8")
_ = brew.dropout(model, relu8, "fc8_dropout", is_test=1, ratio=0.5)
return model, [(1, 3, 224, 224)]
def create_unet_model(m, device_opts, is_test):
base_n_filters = 16
kernel_size = 3
pad = (kernel_size - 1) / 2
do_dropout = True
num_classes = 3
weight_init = ("MSRAFill", {})
with core.DeviceScope(device_opts):
contr_1_1 = brew.spatial_bn(m,
brew.relu(
m,
brew.conv(m,
'data',
'conv_1_1',
dim_in=num_classes,
dim_out=base_n_filters,
kernel=kernel_size,
pad=pad,
weight_init=weight_init),
'nonl_1_1'),
'contr_1_1',
dim_in=base_n_filters,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
contr_1_2 = brew.spatial_bn(m,
brew.relu(
m,
brew.conv(m,
contr_1_1,
'conv_1_2',
dim_in=base_n_filters,
dim_out=base_n_filters,
kernel=kernel_size,
pad=pad,
weight_init=weight_init),
'nonl_1_2'),
'contr_1_2',
dim_in=base_n_filters,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
pool1 = brew.max_pool(m, contr_1_2, 'pool1', kernel=2, stride=2)
contr_2_1 = brew.spatial_bn(m,
brew.relu(
m,
brew.conv(m,
pool1,
'conv_2_1',
dim_in=base_n_filters,
dim_out=base_n_filters * 2,
kernel=kernel_size,
pad=pad,
weight_init=weight_init),
'nonl_2_1'),
'contr_2_1',
dim_in=base_n_filters * 2,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
contr_2_2 = brew.spatial_bn(m,
brew.relu(
m,
brew.conv(m,
contr_2_1,
'conv_2_2',
dim_in=base_n_filters * 2,
dim_out=base_n_filters * 2,
kernel=kernel_size,
pad=pad,
weight_init=weight_init),
'nonl_2_2'),
'contr_2_2',
dim_in=base_n_filters * 2,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
pool2 = brew.max_pool(m, contr_2_2, 'pool2', kernel=2, stride=2)
contr_3_1 = brew.spatial_bn(m,
brew.relu(
m,
brew.conv(m,
pool2,
'conv_3_1',
dim_in=base_n_filters * 2,
dim_out=base_n_filters * 4,
kernel=kernel_size,
pad=pad,
weight_init=weight_init),
'nonl_3_1'),
'contr_3_1',
dim_in=base_n_filters * 4,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
contr_3_2 = brew.spatial_bn(m,
brew.relu(
m,
brew.conv(m,
contr_3_1,
'conv_3_2',
dim_in=base_n_filters * 4,
dim_out=base_n_filters * 4,
kernel=kernel_size,
pad=pad,
weight_init=weight_init),
'nonl_3_2'),
'contr_3_2',
dim_in=base_n_filters * 4,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
pool3 = brew.max_pool(m, contr_3_2, 'pool3', kernel=2, stride=2)
contr_4_1 = brew.spatial_bn(m,
brew.relu(
m,
brew.conv(m,
pool3,
'conv_4_1',
dim_in=base_n_filters * 4,
dim_out=base_n_filters * 8,
kernel=kernel_size,
pad=pad,
weight_init=weight_init),
'nonl_4_1'),
'contr_4_1',
dim_in=base_n_filters * 8,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
contr_4_2 = brew.spatial_bn(m,
brew.relu(
m,
brew.conv(m,
contr_4_1,
'conv_4_2',
dim_in=base_n_filters * 8,
dim_out=base_n_filters * 8,
kernel=kernel_size,
pad=pad,
weight_init=weight_init),
'nonl_4_2'),
'contr_4_2',
dim_in=base_n_filters * 8,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
pool4 = brew.max_pool(m, contr_4_2, 'pool4', kernel=2, stride=2)
if do_dropout:
pool4 = brew.dropout(m, pool4, 'drop', ratio=0.4)
encode_5_1 = brew.spatial_bn(m,
brew.relu(
m,
brew.conv(m,
pool4,
'conv_5_1',
dim_in=base_n_filters * 8,
dim_out=base_n_filters * 16,
kernel=kernel_size,
pad=pad,
weight_init=weight_init),
'nonl_5_1'),
'encode_5_1',
dim_in=base_n_filters * 16,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
encode_5_2 = brew.spatial_bn(m,
brew.relu(
m,
brew.conv(m,
encode_5_1,
'conv_5_2',
dim_in=base_n_filters * 16,
dim_out=base_n_filters * 16,
kernel=kernel_size,
pad=pad,
weight_init=weight_init),
'nonl_5_2'),
'encode_5_2',
dim_in=base_n_filters * 16,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
upscale5 = brew.conv_transpose(m,
encode_5_2,
'upscale5',
dim_in=base_n_filters * 16,
dim_out=base_n_filters * 16,
kernel=2,
stride=2,
weight_init=weight_init)
concat6 = brew.concat(m, [upscale5, contr_4_2], 'concat6') #, axis=1)
expand_6_1 = brew.spatial_bn(
m,
brew.relu(
m,
brew.conv(m,
concat6,
'conv_6_1',
dim_in=base_n_filters * 8 * 3,
dim_out=base_n_filters * 8,
kernel=kernel_size,
pad=pad,
weight_init=weight_init), 'nonl_6_1'),
'expand_6_1',
dim_in=base_n_filters * 8,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
expand_6_2 = brew.spatial_bn(m,
brew.relu(
m,
brew.conv(m,
expand_6_1,
'conv_6_2',
dim_in=base_n_filters * 8,
dim_out=base_n_filters * 8,
kernel=kernel_size,
pad=pad,
weight_init=weight_init),
'nonl_6_2'),
'expand_6_2',
dim_in=base_n_filters * 8,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
upscale6 = brew.conv_transpose(m,
expand_6_2,
'upscale6',
dim_in=base_n_filters * 8,
dim_out=base_n_filters * 8,
kernel=2,
stride=2,
weight_init=weight_init)
concat7 = brew.concat(m, [upscale6, contr_3_2], 'concat7')
expand_7_1 = brew.spatial_bn(
m,
brew.relu(
m,
brew.conv(m,
concat7,
'conv_7_1',
dim_in=base_n_filters * 4 * 3,
dim_out=base_n_filters * 4,
kernel=kernel_size,
pad=pad,
weight_init=weight_init), 'nonl_7_1'),
'expand_7_1',
dim_in=base_n_filters * 4,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
expand_7_2 = brew.spatial_bn(m,
brew.relu(
m,
brew.conv(m,
expand_7_1,
'conv_7_2',
dim_in=base_n_filters * 4,
dim_out=base_n_filters * 4,
kernel=kernel_size,
pad=pad,
weight_init=weight_init),
'nonl_7_2'),
'expand_7_2',
dim_in=base_n_filters * 4,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
upscale7 = brew.conv_transpose(m,
expand_7_2,
'upscale7',
dim_in=base_n_filters * 4,
dim_out=base_n_filters * 4,
kernel=2,
stride=2,
weight_init=weight_init)
concat8 = brew.concat(m, [upscale7, contr_2_2], 'concat8')
expand_8_1 = brew.spatial_bn(
m,
brew.relu(
m,
brew.conv(m,
concat8,
'conv_8_1',
dim_in=base_n_filters * 2 * 3,
dim_out=base_n_filters * 2,
kernel=kernel_size,
pad=pad,
weight_init=weight_init), 'nonl_8_1'),
'expand_8_1',
dim_in=base_n_filters * 2,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
expand_8_2 = brew.spatial_bn(m,
brew.relu(
m,
brew.conv(m,
expand_8_1,
'conv_8_2',
dim_in=base_n_filters * 2,
dim_out=base_n_filters * 2,
kernel=kernel_size,
pad=pad,
weight_init=weight_init),
'nonl_8_2'),
'expand_8_2',
dim_in=base_n_filters * 2,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
upscale8 = brew.conv_transpose(m,
expand_8_2,
'upscale8',
dim_in=base_n_filters * 2,
dim_out=base_n_filters * 2,
kernel=2,
stride=2,
weight_init=weight_init)
concat9 = brew.concat(m, [upscale8, contr_1_2], 'concat9')
expand_9_1 = brew.spatial_bn(m,
brew.relu(
m,
brew.conv(m,
concat9,
'conv_9_1',
dim_in=base_n_filters * 3,
dim_out=base_n_filters,
kernel=kernel_size,
pad=pad,
weight_init=weight_init),
'nonl_9_1'),
'expand_9_1',
dim_in=base_n_filters,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
expand_9_2 = brew.spatial_bn(m,
brew.relu(
m,
brew.conv(m,
expand_9_1,
'conv_9_2',
dim_in=base_n_filters,
dim_out=base_n_filters,
kernel=kernel_size,
pad=pad,
weight_init=weight_init),
'nonl_9_2'),
'expand_9_2',
dim_in=base_n_filters,
epsilon=1e-3,
momentum=0.1,
is_test=is_test)
output_segmentation = brew.conv(m,
expand_9_2,
'output_segmentation',
dim_in=base_n_filters,
dim_out=num_classes,
kernel=1,
pad=0,
stride=1,
weight_init=weight_init)
m.net.AddExternalOutput(output_segmentation)
output_sigmoid = m.Sigmoid(output_segmentation, 'output_sigmoid')
m.net.AddExternalOutput(output_sigmoid)
return output_segmentation
def create_alexnetv0(
model,
data,
num_input_channels,
num_labels,
is_test=False,
):
conv1 = brew.conv(
model,
data,
"conv1",
num_input_channels, # dim_in
96, # dim_out
11, # kernel
('XavierFill', {}),
('ConstantFill', {}),
stride=4,
pad=2)
relu1 = brew.relu(model, conv1, "conv1")
norm1 = brew.lrn(model, relu1, "norm1", size=5, alpha=0.0001, beta=0.75)
pool1 = brew.max_pool(model, norm1, "pool1", kernel=3, stride=2)
conv2 = brew.conv(model,
pool1,
"conv2",
96,
256,
5, ('XavierFill', {}), ('ConstantFill', {}),
pad=2)
relu2 = brew.relu(model, conv2, "conv2")
norm2 = brew.lrn(model, relu2, "norm2", size=5, alpha=0.0001, beta=0.75)
pool2 = brew.max_pool(model, norm2, "pool2", kernel=3, stride=2)
conv3 = brew.conv(model,
pool2,
"conv3",
256,
384,
3, ('XavierFill', {}), ('ConstantFill', {}),
pad=1)
relu3 = brew.relu(model, conv3, "conv3")
conv4 = brew.conv(model,
relu3,
"conv4",
384,
384,
3, ('XavierFill', {}), ('ConstantFill', {}),
pad=1)
relu4 = brew.relu(model, conv4, "conv4")
conv5 = brew.conv(model,
relu4,
"conv5",
384,
256,
3, ('XavierFill', {}), ('ConstantFill', {}),
pad=1)
relu5 = brew.relu(model, conv5, "conv5")
pool5 = brew.max_pool(model, relu5, "pool5", kernel=3, stride=2)
fc6 = brew.fc(model, pool5, "fc6", 256 * 6 * 6, 4096, ('XavierFill', {}),
('ConstantFill', {}))
relu6 = brew.relu(model, fc6, "fc6")
dropout1 = brew.dropout(model,
relu6,
'dropout1',
ratio=0.5,
is_test=is_test)
fc7 = brew.fc(model, dropout1, "fc7", 4096, 4096, ('XavierFill', {}),
('ConstantFill', {}))
relu7 = brew.relu(model, fc7, "fc7")
dropout2 = brew.dropout(model,
relu7,
'dropout2',
ratio=0.5,
is_test=is_test)
fc8 = brew.fc(model, dropout2, "fc8", 4096, num_labels, ('XavierFill', {}),
('ConstantFill', {}))
return fc8
def Dropout(self, *args, **kwargs):
return brew.dropout(self, *args, **kwargs)
def make_forward_pass_ops(
self,
model: ModelHelper,
input_blob: str,
output_blob: str,
is_test: bool = False,
) -> None:
"""
Performs a forward pass of a multi-layer perceptron.
:param model: The ModelHelper object whose net will execute this pass
:param input_blob: The blob containing the input data
:param output_blob: The blob where the output data will be placed
:param is_test: Indicates whether or not this forward pass should skip
node dropout.
"""
model.net.NanCheck([input_blob], [input_blob])
model_states = []
num_layer_connections = len(self.layers) - 1
for x in range(num_layer_connections + 1):
if x == 0:
model_states.append(input_blob)
elif x == num_layer_connections:
model_states.append(output_blob)
else:
model_states.append(
model.net.NextBlob("ModelState_" + str(x) + "_" +
self.model_id))
for x in range(num_layer_connections):
inputs = model_states[x]
outputs = model_states[x + 1]
activation = self.activations[x]
dim_in = self.layers[x]
dim_out = self.layers[x + 1]
weight_name = self.weights[x]
bias_name = self.biases[x]
brew.fc_explicit_param_names( # type: ignore
model,
inputs,
outputs,
dim_in=dim_in,
dim_out=dim_out,
bias_name=bias_name,
weight_name=weight_name,
weight_init=(
"GivenTensorFill",
{
"values": workspace.FetchBlob(weight_name)
},
),
bias_init=(
"GivenTensorFill",
{
"values": workspace.FetchBlob(bias_name)
},
),
)
if activation == "relu":
brew.relu(model, outputs, outputs)
elif activation == "linear":
pass
else:
raise Exception("Unknown activation function")
if self.dropout_ratio > 0.01:
brew.dropout(model,
outputs,
outputs,
ratio=self.dropout_ratio,
is_test=is_test)
model.net.NanCheck([output_blob], [output_blob])
