def debug_model(model,
in_stream=sys.stdin,
out_stream=sys.stdout,
exit_func=sys.exit):
'''
Returns a cloned model that has debug nodes inserted everywhere. When the
graph is evaluated or trained, those nodes will allow to inspect the graph.
Args:
model (root node): root node until which the nodes are to be debugged
in_stream (object behaving like sys.stdin, default stdin): `readline()`
will be called on it to obtain user input
out_stream (object behaving like sys.stdout, default stdout): `write()`
and `flush()` will be called on it to output debug info to the user
exit_func (callable, default sys.exit): callable that takes an exit code and is called,
when the user exits the debugging process
Returns:
a clone of the model that has debugging enabled
'''
nodes = _nodes_to_debug(model)
dbg_state = _DebugState(nodes)
orig_node_count = len(nodes)
mod_counter = 1
# We cannot add the DebugNodes in one clone because the replacements will
# hide parent nodes.
while True:
modifications = {
n: user_function(
_DebugNode(n, dbg_state, in_stream, out_stream, exit_func))
for n in nodes
}
model = model.clone(CloneMethod.share, modifications)
from cntk.graph import plot
nodes = _nodes_to_debug(model)
if len(nodes) == 1:
# last node is the model node, which we want to debug as well
model = user_function(
_DebugNode(model, dbg_state, in_stream, out_stream))
break
if mod_counter > orig_node_count:
raise ValueError('cannot debug this graph')
mod_counter += 1
return model
def debug_model(model, in_stream=sys.stdin, out_stream=sys.stdout,
exit_func=sys.exit):
'''
Returns a cloned model that has debug nodes inserted everywhere. When the
graph is evaluated or trained, those nodes will allow to inspect the graph.
Args:
model (root node): root node until which the nodes are to be debugged
in_stream (object behaving like sys.stdin, default stdin): `readline()`
will be called on it to obtain user input
out_stream (object behaving like sys.stdout, default stdout): `write()`
and `flush()` will be called on it to output debug info to the user
exit_func (callable, default sys.exit): callable that takes an exit code and is called,
when the user exits the debugging process
Returns:
a clone of the model that has debugging enabled
'''
nodes = _nodes_to_debug(model)
dbg_state = _DebugState(nodes)
orig_node_count = len(nodes)
mod_counter = 1
# We cannot add the DebugNodes in one clone because the replacements will
# hide parent nodes.
while True:
modifications = {n: user_function(_DebugNode(n, dbg_state,
in_stream, out_stream,
exit_func))
for n in nodes}
model = model.clone(CloneMethod.share, modifications)
from cntk.graph import plot
nodes = _nodes_to_debug(model)
if len(nodes) == 1:
# last node is the model node, which we want to debug as well
model = user_function(_DebugNode(model, dbg_state,
in_stream, out_stream))
break
if mod_counter > orig_node_count:
raise ValueError('cannot debug this graph')
mod_counter += 1
return model
def create_proposal_target_layer(rpn_rois, scaled_gt_boxes, num_classes):
'''
Creates a proposal target layer that is used for training an object detection network as proposed in the "Faster R-CNN" paper:
Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun:
"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks"
Assigns object detection proposals to ground-truth targets.
Produces proposal classification labels and bounding-box regression targets.
It also adds gt_boxes to candidates and samples fg and bg rois for training.
Args:
rpn_rois: The proposed ROIs, e.g. from a region proposal network
scaled_gt_boxes: The ground truth boxes as (x1, y1, x2, y2, label). Coordinates are absolute pixels wrt. the input image.
num_classes: The number of classes in the data set
Returns:
rpn_target_rois - a set of rois containing the ground truth and a number of sampled fg and bg ROIs
label_targets - the target labels for the rois
bbox_targets - the regression coefficient targets for the rois
bbox_inside_weights - the weights for the regression loss
'''
ptl_param_string = "'num_classes': {}".format(num_classes)
ptl = user_function(ProposalTargetLayer(rpn_rois, scaled_gt_boxes, param_str=ptl_param_string))
# use an alias if you need to access the outputs, e.g., when cloning a trained network
rois = alias(ptl.outputs[0], name='rpn_target_rois')
label_targets = ptl.outputs[1]
bbox_targets = ptl.outputs[2]
bbox_inside_weights = ptl.outputs[3]
return rois, label_targets, bbox_targets, bbox_inside_weights
def test_ext_backpropstate(payload):
class TestBackPropState(UserFunction):
def __init__(self, arg, payload, name='f1'):
self.payload = payload
super(TestBackPropState, self).__init__([arg])
def infer_outputs(self):
return [C.output_variable(self.inputs[0].shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes)]
def forward(self, argument, device=None, outputs_to_retain=None):
return self.payload, argument
def backward(self, state, root_gradients):
assert state == self.payload
return root_gradients
dim = 4
p = C.parameter(shape=(dim,), init=10)
in1 = C.input_variable(dim, needs_gradient=True, name='i_var')
m = C.user_function(TestBackPropState(in1, payload))
z = m + p
lr_per_sample = C.learning_rate_schedule(0.007, C.UnitType.sample)
trainer = C.Trainer(None, (z), [C.sgd(z.parameters, lr_per_sample)])
for i in range(100):
input_data = np.random.rand(dim)
trainer.train_minibatch({in1: [input_data]})
def create_proposal_target_layer(rpn_rois, scaled_gt_boxes, num_classes):
'''
Creates a proposal target layer that is used for training an object detection network as proposed in the "Faster R-CNN" paper:
Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun:
"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks"
Assigns object detection proposals to ground-truth targets.
Produces proposal classification labels and bounding-box regression targets.
It also adds gt_boxes to candidates and samples fg and bg rois for training.
Args:
rpn_rois: The proposed ROIs, e.g. from a region proposal network
scaled_gt_boxes: The ground truth boxes as (x1, y1, x2, y2, label). Coordinates are absolute pixels wrt. the input image.
num_classes: The number of classes in the data set
Returns:
rpn_target_rois - a set of rois containing the ground truth and a number of sampled fg and bg ROIs
label_targets - the target labels for the rois
bbox_targets - the regression coefficient targets for the rois
bbox_inside_weights - the weights for the regression loss
'''
ptl_param_string = "'num_classes': {}".format(num_classes)
ptl = user_function(ProposalTargetLayer(rpn_rois, scaled_gt_boxes, param_str=ptl_param_string))
rois = alias(ptl.outputs[0], name='rpn_target_rois')
label_targets = alias(ptl.outputs[1], name='label_targets')
bbox_targets = alias(ptl.outputs[2], name='bbox_targets')
bbox_inside_weights = alias(ptl.outputs[3], name='bbox_inside_w')
return rois, label_targets, bbox_targets, bbox_inside_weights
def test_proposal_layer():
cls_prob_shape_cntk = (18,61,61)
cls_prob_shape_caffe = (18,61,61)
rpn_bbox_shape = (36, 61, 61)
im_info = [1000, 1000, 1]
# Create input tensors with values
cls_prob = np.random.random_sample(cls_prob_shape_cntk).astype(np.float32)
rpn_bbox_pred = np.random.random_sample(rpn_bbox_shape).astype(np.float32)
# Create CNTK layer and call forward
cls_prob_var = input_variable(cls_prob_shape_cntk)
rpn_bbox_var = input_variable(rpn_bbox_shape)
cntk_layer = user_function(CntkProposalLayer(cls_prob_var, rpn_bbox_var, cntk.constant(im_info, (3,))))
state, cntk_output = cntk_layer.forward({cls_prob_var: [cls_prob], rpn_bbox_var: [rpn_bbox_pred]})
cntk_proposals = cntk_output[next(iter(cntk_output))][0]
# Create Caffe layer and call forward
cls_prob_caffe = cls_prob.reshape(cls_prob_shape_caffe)
bottom = [np.array([cls_prob_caffe]),np.array([rpn_bbox_pred]),np.array([im_info])]
top = None # handled through return statement in caffe layer for unit testing
param_str = "'feat_stride': 16"
caffe_layer = CaffeProposalLayer()
caffe_layer.set_param_str(param_str)
caffe_layer.setup(bottom, top)
caffe_output = caffe_layer.forward(bottom, top)
caffe_proposals = caffe_output[:,1:]
# assert that results are exactly the same
assert cntk_proposals.shape == caffe_proposals.shape
assert np.allclose(cntk_proposals, caffe_proposals, rtol=0.0, atol=0.0)
print("Verified ProposalLayer")
def create_proposal_layer(rpn_cls_prob_reshape, rpn_bbox_pred, im_info, cfg, use_native_proposal_layer=False):
layer_config = {}
layer_config["feat_stride"] = cfg["MODEL"].FEATURE_STRIDE
layer_config["scales"] = cfg["DATA"].PROPOSAL_LAYER_SCALES
layer_config["train_pre_nms_topN"] = cfg["TRAIN"].RPN_PRE_NMS_TOP_N
layer_config["train_post_nms_topN"] = cfg["TRAIN"].RPN_POST_NMS_TOP_N
layer_config["train_nms_thresh"] = float(cfg["TRAIN"].RPN_NMS_THRESH)
layer_config["train_min_size"] = float(cfg["TRAIN"].RPN_MIN_SIZE)
layer_config["test_pre_nms_topN"] = cfg["TEST"].RPN_PRE_NMS_TOP_N
layer_config["test_post_nms_topN"] = cfg["TEST"].RPN_POST_NMS_TOP_N
layer_config["test_nms_thresh"] = float(cfg["TEST"].RPN_NMS_THRESH)
layer_config["test_min_size"] = float(cfg["TEST"].RPN_MIN_SIZE)
if use_native_proposal_layer:
cntk.ops.register_native_user_function('ProposalLayerOp',
'Cntk.ProposalLayerLib-' + cntk.__version__.rstrip('+'),
'CreateProposalLayer')
rpn_rois_raw = ops.native_user_function('ProposalLayerOp', [rpn_cls_prob_reshape, rpn_bbox_pred, im_info],
layer_config, 'native_proposal_layer')
else:
rpn_rois_raw = user_function(ProposalLayer(rpn_cls_prob_reshape, rpn_bbox_pred, im_info, layer_config))
return alias(rpn_rois_raw, name='rpn_rois')
def test_ext_backpropstate(payload):
class TestBackPropState(UserFunction):
def __init__(self, arg, payload, name='f1'):
self.payload = payload
super(TestBackPropState, self).__init__([arg])
def infer_outputs(self):
return [
C.output_variable(self.inputs[0].shape, self.inputs[0].dtype,
self.inputs[0].dynamic_axes)
]
def forward(self, argument, device=None, outputs_to_retain=None):
return self.payload, argument
def backward(self, state, root_gradients):
assert state == self.payload
return root_gradients
dim = 4
p = C.parameter(shape=(dim, ), init=10)
in1 = C.input_variable(dim, needs_gradient=True, name='i_var')
m = C.user_function(TestBackPropState(in1, payload))
z = m + p
lr_per_sample = C.learning_rate_schedule(0.007, C.UnitType.sample)
trainer = C.Trainer(None, (z), [C.sgd(z.parameters, lr_per_sample)])
for i in range(100):
input_data = np.random.rand(dim)
trainer.train_minibatch({in1: [input_data]})
def create_proposal_layer(rpn_cls_prob_reshape,
rpn_bbox_pred,
im_info,
cfg,
use_native_proposal_layer=False):
layer_config = {}
layer_config["feat_stride"] = cfg["MODEL"].FEATURE_STRIDE
layer_config["scales"] = cfg["DATA"].PROPOSAL_LAYER_SCALES
layer_config["train_pre_nms_topN"] = cfg["TRAIN"].RPN_PRE_NMS_TOP_N
layer_config["train_post_nms_topN"] = cfg["TRAIN"].RPN_POST_NMS_TOP_N
layer_config["train_nms_thresh"] = float(cfg["TRAIN"].RPN_NMS_THRESH)
layer_config["train_min_size"] = float(cfg["TRAIN"].RPN_MIN_SIZE)
layer_config["test_pre_nms_topN"] = cfg["TEST"].RPN_PRE_NMS_TOP_N
layer_config["test_post_nms_topN"] = cfg["TEST"].RPN_POST_NMS_TOP_N
layer_config["test_nms_thresh"] = float(cfg["TEST"].RPN_NMS_THRESH)
layer_config["test_min_size"] = float(cfg["TEST"].RPN_MIN_SIZE)
if use_native_proposal_layer:
cntk.ops.register_native_user_function(
'ProposalLayerOp',
'Cntk.ProposalLayerLib-' + cntk.__version__.rstrip('+'),
'CreateProposalLayer')
rpn_rois_raw = ops.native_user_function(
'ProposalLayerOp', [rpn_cls_prob_reshape, rpn_bbox_pred, im_info],
layer_config, 'native_proposal_layer')
else:
rpn_rois_raw = user_function(
ProposalLayer(rpn_cls_prob_reshape, rpn_bbox_pred, im_info,
layer_config))
return alias(rpn_rois_raw, name='rpn_rois')
def build_test_function():
dev = C.cpu()
w_value = np.asarray([[0.5, 2], [-0.5, 1.5]]).astype(np.float32)
c1_value = 2.718
c2_value = -3.141
if not C.cntk_py.is_native_user_function_registered('NativeUserTimesOp'):
C.ops.register_native_user_function('NativeUserTimesOp', 'Cntk.ExtensibilityExamples-' + C.__version__.rstrip('+'), 'CreateUserTimesFunction')
x = C.input_variable((2))
w = C.parameter((2, 2), init=w_value, device=dev)
op = C.user_function(MyPlus(x, C.constant(c1_value)))
op = C.ops.native_user_function('NativeUserTimesOp', [w, op], user_function_instance_name='my_times')
return dev, w_value, c1_value, c2_value, C.user_function(MyPlus(op, C.constant(c2_value)))
def build_test_function():
dev = C.cpu()
w_value = np.asarray([[0.5, 2], [-0.5, 1.5]]).astype(np.float32)
c1_value = 2.718
c2_value = -3.141
if not C.cntk_py.is_native_user_function_registered('NativeUserTimesOp'):
C.ops.register_native_user_function('NativeUserTimesOp', 'Cntk.ExtensibilityExamples-' + C.__version__.rstrip('+'), 'CreateUserTimesFunction')
x = C.input_variable((2))
w = C.parameter((2, 2), init=w_value, device=dev)
op = C.user_function(MyPlus(x, C.constant(c1_value)))
op = C.ops.native_user_function('NativeUserTimesOp', [w, op], user_function_instance_name='my_times')
return dev, w_value, c1_value, c2_value, C.user_function(MyPlus(op, C.constant(c2_value)))
def test_anchor_target_layer():
from utils.rpn.anchor_target_layer import AnchorTargetLayer as CntkAnchorTargetLayer
from utils.caffe_layers.anchor_target_layer import AnchorTargetLayer as CaffeAnchorTargetLayer
rpn_cls_score_shape_cntk = (1, 18, 61, 61)
num_gt_boxes = 50
gt_boxes_shape_cntk = (num_gt_boxes,5)
dims_info_shape = (6,)
im_info = [1000, 1000, 1]
# Create input tensors with values
rpn_cls_score_dummy = np.random.random_sample(rpn_cls_score_shape_cntk).astype(np.float32)
dims_input = np.array([1000, 1000, 1000, 1000, 1000, 1000]).astype(np.float32)
x1y1 = np.random.random_sample((num_gt_boxes, 2)) * 500
wh = np.random.random_sample((num_gt_boxes, 2)) * 400
x2y2 = x1y1 + wh + 50
label = np.random.random_sample((num_gt_boxes, 1))
label = (label * 17.0)
gt_boxes = np.hstack((x1y1, x2y2, label)).astype(np.float32)
# Create CNTK layer and call forward
rpn_cls_score_var = input_variable(rpn_cls_score_shape_cntk)
gt_boxes_var = input_variable(gt_boxes_shape_cntk)
dims_info_var = input_variable(dims_info_shape)
cntk_layer = user_function(CntkAnchorTargetLayer(rpn_cls_score_var, gt_boxes_var, dims_info_var, deterministic=True))
state, cntk_output = cntk_layer.forward({rpn_cls_score_var: [rpn_cls_score_dummy], gt_boxes_var: [gt_boxes], dims_info_var: dims_input})
obj_key = [k for k in cntk_output if 'objectness_target' in str(k)][0]
bbt_key = [k for k in cntk_output if 'rpn_bbox_target' in str(k)][0]
bbw_key = [k for k in cntk_output if 'rpn_bbox_inside_w' in str(k)][0]
cntk_objectness_target = cntk_output[obj_key][0]
cntk_bbox_targets = cntk_output[bbt_key][0]
cntk_bbox_inside_w = cntk_output[bbw_key][0]
# Create Caffe layer and call forward
bottom = [np.array(rpn_cls_score_dummy),np.array(gt_boxes), np.array(im_info)]
top = None # handled through return statement in caffe layer for unit testing
param_str = "'feat_stride': 16"
caffe_layer = CaffeAnchorTargetLayer()
caffe_layer.set_param_str(param_str)
caffe_layer.setup(bottom, top)
caffe_layer.set_deterministic_mode()
caffe_objectness_target, caffe_bbox_targets, caffe_bbox_inside_w = caffe_layer.forward(bottom, top)
# assert that results are exactly the same
assert cntk_objectness_target.shape == caffe_objectness_target.shape
assert cntk_bbox_targets.shape == caffe_bbox_targets.shape
assert cntk_bbox_inside_w.shape == caffe_bbox_inside_w.shape
assert np.allclose(cntk_objectness_target, caffe_objectness_target, rtol=0.0, atol=0.0)
assert np.allclose(cntk_bbox_targets, caffe_bbox_targets, rtol=0.0, atol=0.0)
assert np.allclose(cntk_bbox_inside_w, caffe_bbox_inside_w, rtol=0.0, atol=0.0)
print("Verified AnchorTargetLayer")
def test_ext_eval_3_no_input():
dim = 4
p = C.parameter(shape=(dim, ), init=10, name='p')
m = C.user_function(MyPlus(p, C.constant(3)))
z = m + 0
result = z.eval()
# No batch dimension since we have no input
assert np.allclose(result, np.zeros_like(p) + 10 + 3)
def test_ext_eval_4_b_inside_graph():
dim = 4
p_init = 10
p = C.parameter(shape=(dim, ), init=p_init, name='p')
z = C.user_function(p * MyPlus(p, C.constant(3)))
result = z.eval()
# No batch dimension since we have no input
assert np.allclose(result, ((p_init * np.ones_like(result)) + 3) * p_init)
def test_ext_eval_4_b_inside_graph():
dim = 4
p_init = 10
p = C.parameter(shape=(dim,), init=p_init, name='p')
z = C.user_function(p * MyPlus(p, C.constant(3)))
result = z.eval()
# No batch dimension since we have no input
assert np.allclose(result, ((p_init * np.ones_like(result)) + 3) * p_init)
def test_ext_eval_3_no_input():
dim = 4
p = C.parameter(shape=(dim,), init=10, name='p')
m = C.user_function(MyPlus(p, C.constant(3)))
z = m + 0
result = z.eval()
# No batch dimension since we have no input
assert np.allclose(result, np.zeros_like(p) + 10 + 3)
def test_ext_eval_5_times():
dim = 2
p_init = 10
p = C.parameter(shape=(dim, ), init=p_init, name='p')
m = C.user_function(MyPlus(p, C.constant(3)))
z = C.times(m, C.parameter(shape=(2, 50), init=2))
result = z.eval()
# No batch dimension since we have no input
assert np.allclose(result, ((p_init * np.ones_like(result)) + 3) * 2 * 2)
def test_ext_eval_1():
dim = 4
p = C.parameter(shape=(dim,), init=10, name='p')
i = C.sequence.input_variable(dim, needs_gradient=True, name='i_var')
m = C.user_function(MyPlus(i, C.constant(3)))
z = m + p
input_data = np.random.rand(dim)
result = z.eval([input_data])
assert np.allclose(result[0][0], input_data + 3 + 10)
def test_ext_eval_1():
dim = 4
p = C.parameter(shape=(dim, ), init=10, name='p')
i = C.sequence.input_variable(dim, needs_gradient=True, name='i_var')
m = C.user_function(MyPlus(i, C.constant(3)))
z = m + p
input_data = np.random.rand(dim)
result = z.eval([input_data])
assert np.allclose(result[0][0], input_data + 3 + 10)
def test_ext_eval_5_times():
dim = 2
p_init = 10
p = C.parameter(shape=(dim,), init=p_init, name='p')
m = C.user_function(MyPlus(p, C.constant(3)))
z = C.times(m, C.parameter(shape=(2, 50), init=2))
result = z.eval()
# No batch dimension since we have no input
assert np.allclose(result, ((p_init * np.ones_like(result)) + 3) * 2 * 2)
def test_anchor_target_layer():
rpn_cls_score_shape_cntk = (1, 18, 61, 61)
num_gt_boxes = 50
gt_boxes_shape_cntk = (num_gt_boxes,5)
dims_info_shape = (6,)
im_info = [1000, 1000, 1]
# Create input tensors with values
rpn_cls_score_dummy = np.random.random_sample(rpn_cls_score_shape_cntk).astype(np.float32)
dims_input = np.array([1000, 1000, 1000, 1000, 1000, 1000]).astype(np.float32)
x1y1 = np.random.random_sample((num_gt_boxes, 2)) * 500
wh = np.random.random_sample((num_gt_boxes, 2)) * 400
x2y2 = x1y1 + wh + 50
label = np.random.random_sample((num_gt_boxes, 1))
label = (label * 17.0)
gt_boxes = np.hstack((x1y1, x2y2, label)).astype(np.float32)
# Create CNTK layer and call forward
rpn_cls_score_var = input_variable(rpn_cls_score_shape_cntk)
gt_boxes_var = input_variable(gt_boxes_shape_cntk)
dims_info_var = input_variable(dims_info_shape)
cntk_layer = user_function(CntkAnchorTargetLayer(rpn_cls_score_var, gt_boxes_var, dims_info_var, deterministic=True))
state, cntk_output = cntk_layer.forward({rpn_cls_score_var: [rpn_cls_score_dummy], gt_boxes_var: [gt_boxes], dims_info_var: dims_input})
obj_key = [k for k in cntk_output if 'objectness_target' in str(k)][0]
bbt_key = [k for k in cntk_output if 'rpn_bbox_target' in str(k)][0]
bbw_key = [k for k in cntk_output if 'rpn_bbox_inside_w' in str(k)][0]
cntk_objectness_target = cntk_output[obj_key][0]
cntk_bbox_targets = cntk_output[bbt_key][0]
cntk_bbox_inside_w = cntk_output[bbw_key][0]
# Create Caffe layer and call forward
bottom = [np.array(rpn_cls_score_dummy),np.array(gt_boxes), np.array(im_info)]
top = None # handled through return statement in caffe layer for unit testing
param_str = "'feat_stride': 16"
caffe_layer = CaffeAnchorTargetLayer()
caffe_layer.set_param_str(param_str)
caffe_layer.setup(bottom, top)
caffe_layer.set_deterministic_mode()
caffe_objectness_target, caffe_bbox_targets, caffe_bbox_inside_w = caffe_layer.forward(bottom, top)
# assert that results are exactly the same
assert cntk_objectness_target.shape == caffe_objectness_target.shape
assert cntk_bbox_targets.shape == caffe_bbox_targets.shape
assert cntk_bbox_inside_w.shape == caffe_bbox_inside_w.shape
assert np.allclose(cntk_objectness_target, caffe_objectness_target, rtol=0.0, atol=0.0)
assert np.allclose(cntk_bbox_targets, caffe_bbox_targets, rtol=0.0, atol=0.0)
assert np.allclose(cntk_bbox_inside_w, caffe_bbox_inside_w, rtol=0.0, atol=0.0)
print("Verified AnchorTargetLayer")
def test_proposal_layer():
from utils.rpn.proposal_layer import ProposalLayer as CntkProposalLayer
from utils.caffe_layers.proposal_layer import ProposalLayer as CaffeProposalLayer
from FasterRCNN.FasterRCNN_config import cfg
cls_prob_shape_cntk = (18,61,61)
cls_prob_shape_caffe = (18,61,61)
rpn_bbox_shape = (36, 61, 61)
dims_info_shape = (6,)
im_info = [1000, 1000, 1]
# Create input tensors with values
cls_prob = np.random.random_sample(cls_prob_shape_cntk).astype(np.float32)
rpn_bbox_pred = np.random.random_sample(rpn_bbox_shape).astype(np.float32)
dims_input = np.array([1000, 1000, 1000, 1000, 1000, 1000]).astype(np.float32)
# Create CNTK layer and call forward
cls_prob_var = input_variable(cls_prob_shape_cntk)
rpn_bbox_var = input_variable(rpn_bbox_shape)
dims_info_var = input_variable(dims_info_shape)
layer_config = {}
layer_config["feat_stride"] = 16
layer_config["scales"] = [8, 16, 32]
layer_config["train_pre_nms_topN"] = cfg["TRAIN"].RPN_PRE_NMS_TOP_N
layer_config["train_post_nms_topN"] = cfg["TRAIN"].RPN_POST_NMS_TOP_N
layer_config["train_nms_thresh"] = float(cfg["TRAIN"].RPN_NMS_THRESH)
layer_config["train_min_size"] = float(cfg["TRAIN"].RPN_MIN_SIZE)
layer_config["test_pre_nms_topN"] = cfg["TEST"].RPN_PRE_NMS_TOP_N
layer_config["test_post_nms_topN"] = cfg["TEST"].RPN_POST_NMS_TOP_N
layer_config["test_nms_thresh"] = float(cfg["TEST"].RPN_NMS_THRESH)
layer_config["test_min_size"] = float(cfg["TEST"].RPN_MIN_SIZE)
cntk_layer = user_function(CntkProposalLayer(cls_prob_var, rpn_bbox_var, dims_info_var, layer_config))
state, cntk_output = cntk_layer.forward({cls_prob_var: [cls_prob], rpn_bbox_var: [rpn_bbox_pred], dims_info_var: dims_input})
cntk_proposals = cntk_output[next(iter(cntk_output))][0]
# Create Caffe layer and call forward
cls_prob_caffe = cls_prob.reshape(cls_prob_shape_caffe)
bottom = [np.array([cls_prob_caffe]),np.array([rpn_bbox_pred]),np.array([im_info])]
top = None # handled through return statement in caffe layer for unit testing
param_str = "'feat_stride': 16"
caffe_layer = CaffeProposalLayer()
caffe_layer.set_param_str(param_str)
caffe_layer.setup(bottom, top)
caffe_output = caffe_layer.forward(bottom, top)
caffe_proposals = caffe_output[:,1:]
# assert that results are exactly the same
assert cntk_proposals.shape == caffe_proposals.shape
assert np.allclose(cntk_proposals, caffe_proposals, rtol=0.0, atol=0.0)
print("Verified ProposalLayer")
def test_udf_clone():
dim = 4
i = C.sequence.input_variable(dim, needs_gradient=True, name='i_var')
m_udf = C.user_function(MyPlus(i, C.constant(3)))
p = C.parameter(shape=(dim, ), init=10, name='p')
z = m_udf + p
z_clone = z.clone('share')
input_data = np.random.rand(dim)
result = z_clone.eval([input_data])
assert np.allclose(result[0][0], input_data + 3 + 10)
def test_udf_input_values_no_sharing():
i = C.input_variable(1, needs_gradient=True, name='i_var')
m = C.user_function(MyArgumentPreservingPlus(i + 1, i + 2))
w = C.parameter(shape=(1,), init=1)
m = m + w
m2 = C.splice(m, m, axis=0)
m3 = C.splice(m2, m2, axis=0)
m4 = C.splice(m3, m3, axis=0)
grad_value, result = m4.grad({i : np.asarray([2], dtype=np.float32)}, outputs=[m4], wrt=[w, i])
assert np.array_equal(result, [[8, 8, 8, 8, 8, 8, 8, 8]])
def test_udf_input_values_no_sharing():
i = C.input_variable(1, needs_gradient=True, name='i_var')
m = C.user_function(MyArgumentPreservingPlus(i + 1, i + 2))
w = C.parameter(shape=(1,), init=1)
m = m + w
m2 = C.splice(m, m, axis=0)
m3 = C.splice(m2, m2, axis=0)
m4 = C.splice(m3, m3, axis=0)
grad_value, result = m4.grad({i : np.asarray([2], dtype=np.float32)}, outputs=[m4], wrt=[w, i])
assert np.array_equal(result, [[8, 8, 8, 8, 8, 8, 8, 8]])
def test_ext_eval_2_only_param():
dim = 4
p = C.parameter(shape=(dim,), init=10, name='p')
i = C.sequence.input_variable(dim, needs_gradient=True, name='i_var')
m = C.user_function(MyPlus(p, C.constant(3)))
# combine does not work
# z = combine([m.output])
z = m + i
input_data = np.random.rand(dim)
result = z.eval([input_data])
assert np.allclose(result[0][0], input_data + 3 + 10)
def test_udf_clone():
dim = 4
i = C.sequence.input_variable(dim, needs_gradient=True, name='i_var')
m_udf = C.user_function(MyPlus(i, C.constant(3)))
p = C.parameter(shape=(dim,), init=10, name='p')
z = m_udf + p
z_clone = z.clone('share')
input_data = np.random.rand(dim)
result = z_clone.eval([input_data])
assert np.allclose(result[0][0], input_data + 3 + 10)
def test_ext_eval_2_only_param():
dim = 4
p = C.parameter(shape=(dim, ), init=10, name='p')
i = C.sequence.input_variable(dim, needs_gradient=True, name='i_var')
m = C.user_function(MyPlus(p, C.constant(3)))
# combine does not work
# z = combine([m.output])
z = m + i
input_data = np.random.rand(dim)
result = z.eval([input_data])
assert np.allclose(result[0][0], input_data + 3 + 10)
def test_ext_eval_freedimension_input():
i = C.sequence.input_variable((C.FreeDimension), needs_gradient=True, name='i_var')
m = C.user_function(MyPlus(i, C.constant(3)))
input_data = np.random.rand(3)
gradient_value, result = m.grad({i: input_data}, wrt=[i], outputs=[m.output])
assert np.allclose(result[0][0], input_data + 3)
assert np.allclose(gradient_value[0][0], np.ones_like(input_data))
input_data = np.random.rand(6)
gradient_value, result = m.grad({i: input_data}, wrt=[i], outputs=[m.output])
assert np.allclose(result[0][0], input_data + 3)
assert np.allclose(gradient_value[0][0], np.ones_like(input_data))
def test_ext_eval_freedimension_input():
i = C.sequence.input_variable((C.FreeDimension), needs_gradient=True, name='i_var')
m = C.user_function(MyPlus(i, C.constant(3)))
input_data = np.random.rand(3)
gradient_value, result = m.grad({i: input_data}, wrt=[i], outputs=[m.output])
assert np.allclose(result[0][0], input_data + 3)
assert np.allclose(gradient_value[0][0], np.ones_like(input_data))
input_data = np.random.rand(6)
gradient_value, result = m.grad({i: input_data}, wrt=[i], outputs=[m.output])
assert np.allclose(result[0][0], input_data + 3)
assert np.allclose(gradient_value[0][0], np.ones_like(input_data))
def CustomMultibitKernel(input, bit_map, mean_bits=None):
if (mean_bits):
bit_map = np.asarray(np.maximum(np.round(np.random.normal(mean_bits, 1, input.shape)), 1), dtype=np.int32)
print("Mean Bits: ",np.mean(bit_map))
else:
if (type(bit_map) == int):
length = C.reshape(input, (-1))
bit_map = [bit_map]*length.shape[0]
bit_map = np.asarray(bit_map)
bit_map = bit_map.reshape(input.shape)
else:
bit_map = np.asarray(bit_map)
assert (bit_map.shape == input.shape)
return C.user_function(MultibitKernel(input, bit_map))
def test_udf_no_gradient_for_some_inputs():
dim = 2
x = C.sequence.input_variable(dim, needs_gradient=True, name='x')
y = C.sequence.input_variable(dim, needs_gradient=True, name='y')
op = C.user_function(MyPlusWithNoGradientToRightOperand(x, y))
x_data = [AA([[1., 2.], [3., 4.]], dtype=np.float32)]
y_data = [AA([[5., 6.], [7., 8.]], dtype=np.float32)]
gradients, result = op.grad({x: x_data, y: y_data}, op.arguments, [op.output])
assert np.allclose(gradients[op.arguments[0]], [[[1., 1.], [1., 1.]]])
assert np.allclose(gradients[op.arguments[1]], [[[0., 0.], [0., 0.]]])
assert np.allclose(result, [[[6., 8.], [10., 12.]]])
def test_udf_plus_and_last():
x = C.sequence.input_variable(shape=(2, ))
y = C.input_variable(shape=(2, ))
func = C.user_function(PlusAndLast(x, y))
dt_precision = np.float32
operand1 = [AA([[1., 2.], [3., 4.]], dtype=dt_precision)]
operand2 = [AA([2., 2.], dtype=dt_precision)]
_, result = func.forward({x: operand1, y: operand2}, [func.output])
expected_forward = AA([[[5., 6.]]], dtype=dt_precision)
assert np.allclose(result[func.output], expected_forward)
def test_udf_plus_and_last():
x = C.sequence.input_variable(shape=(2,))
y = C.input_variable(shape=(2,))
func = C.user_function(PlusAndLast(x, y))
dt_precision = np.float32
operand1 = [AA([[1., 2.], [3., 4.]], dtype=dt_precision)]
operand2 = [AA([2., 2.], dtype=dt_precision)]
_, result = func.forward({x: operand1, y: operand2}, [func.output])
expected_forward = AA([[[5., 6.]]], dtype=dt_precision)
assert np.allclose(result[func.output], expected_forward)
def test_udf_no_gradient_for_some_inputs():
dim = 2
x = C.sequence.input_variable(dim, needs_gradient=True, name='x')
y = C.sequence.input_variable(dim, needs_gradient=True, name='y')
op = C.user_function(MyPlusWithNoGradientToRightOperand(x, y))
x_data = [AA([[1., 2.], [3., 4.]], dtype=np.float32)]
y_data = [AA([[5., 6.], [7., 8.]], dtype=np.float32)]
gradients, result = op.grad({x: x_data, y: y_data}, op.arguments, [op.output])
assert np.allclose(gradients[op.arguments[0]], [[[1., 1.], [1., 1.]]])
assert np.allclose(gradients[op.arguments[1]], [[[0., 0.], [0., 0.]]])
assert np.allclose(result, [[[6., 8.], [10., 12.]]])
def test_override_serialize(tmpdir):
dev = C.cpu()
a, b = 1.2322341, -0.29084
op = MyPlusPlus([C.constant(a), C.constant(b)], '++')
op = MyPlusPlus([op, op], '+++')
op = MyPlusPlus([op, op], '++++')
op = C.user_function(op)
result1 = op.eval({}, device=dev)
filepath = str(tmpdir / 'test_udf_with_renamed_deserialize.dat')
op.save(filepath)
op_reloaded = Function.load(filepath, device=dev)
assert result1 == op_reloaded.eval({}, device=dev)
def test_override_serialize(tmpdir):
dev = C.cpu()
a, b = 1.2322341, -0.29084
op = MyPlusPlus([C.constant(a), C.constant(b)], '++')
op = MyPlusPlus([op, op], '+++')
op = MyPlusPlus([op, op], '++++')
op = C.user_function(op)
result1 = op.eval({}, device=dev)
filepath = str(tmpdir / 'test_udf_with_renamed_deserialize.dat')
op.save(filepath)
op_reloaded = Function.load(filepath, device=dev)
assert result1 == op_reloaded.eval({}, device=dev)
def test_multi_freedim_output_udf():
dim = 2
x = C.sequence.input_variable(dim, needs_gradient=True, name='x')
y = C.sequence.input_variable(dim, needs_gradient=True, name='y')
op = C.user_function(MultiFreeDimensionOutputUserFunction(x, y))
x_data = [AA([[1., 2.], [3., 4.]], dtype=np.float32)]
y_data = [AA([[5., 6.], [7., 8.]], dtype=np.float32)]
result = op.eval({x: x_data, y: y_data})
assert np.allclose(result[op.outputs[0]], x_data[0] + 2 * y_data[0])
assert np.allclose(result[op.outputs[1]], 2 * x_data[0] + y_data[0])
op = op.outputs[0] + op.outputs[1]
gradients = op.grad({x: x_data, y: y_data}, op.arguments)
assert np.allclose(gradients[op.arguments[0]], [[[3., 3.], [3., 3.]]])
assert np.allclose(gradients[op.arguments[1]], [[[3., 3.], [3., 3.]]])
def CustomMultibitKernel(input, bit_map, mean_bits=None):
if (mean_bits):
bit_map = np.asarray(np.maximum(
np.round(np.random.normal(mean_bits, 1, input.shape)), 1),
dtype=np.int32)
print("Mean Bits: ", np.mean(bit_map))
else:
if (type(bit_map) == int):
length = C.reshape(input, (-1))
bit_map = [bit_map] * length.shape[0]
bit_map = np.asarray(bit_map)
bit_map = bit_map.reshape(input.shape)
else:
bit_map = np.asarray(bit_map)
assert (bit_map.shape == input.shape)
return C.user_function(MultibitKernel(input, bit_map))
def test_multioutput_udf():
dim = 2
x = C.sequence.input_variable(dim, needs_gradient=True, name='x')
y = C.sequence.input_variable(dim, needs_gradient=True, name='y')
op = C.user_function(MultiOutputUserFunction(x, y))
x_data = [AA([[1., 2.], [3., 4.]], dtype=np.float32)]
y_data = [AA([[5., 6.], [7., 8.]], dtype=np.float32)]
result = op.eval({x: x_data, y: y_data})
assert np.allclose(result[op.outputs[0]], x_data[0] + 2 * y_data[0])
assert np.allclose(result[op.outputs[1]], 2 * x_data[0] + y_data[0])
op = op.outputs[0] + op.outputs[1]
gradients = op.grad({x: x_data, y: y_data}, op.arguments)
assert np.allclose(gradients[op.arguments[0]], [[[3., 3.], [3., 3.]]])
assert np.allclose(gradients[op.arguments[1]], [[[3., 3.], [3., 3.]]])
def test_proposal_layer():
cls_prob_shape_cntk = (18, 61, 61)
cls_prob_shape_caffe = (18, 61, 61)
rpn_bbox_shape = (36, 61, 61)
dims_info_shape = (6, )
im_info = [1000, 1000, 1]
# Create input tensors with values
cls_prob = np.random.random_sample(cls_prob_shape_cntk).astype(np.float32)
rpn_bbox_pred = np.random.random_sample(rpn_bbox_shape).astype(np.float32)
dims_input = np.array([1000, 1000, 1000, 1000, 1000,
1000]).astype(np.float32)
# Create CNTK layer and call forward
cls_prob_var = input_variable(cls_prob_shape_cntk)
rpn_bbox_var = input_variable(rpn_bbox_shape)
dims_info_var = input_variable(dims_info_shape)
cntk_layer = user_function(
CntkProposalLayer(cls_prob_var, rpn_bbox_var, dims_info_var))
state, cntk_output = cntk_layer.forward({
cls_prob_var: [cls_prob],
rpn_bbox_var: [rpn_bbox_pred],
dims_info_var: dims_input
})
cntk_proposals = cntk_output[next(iter(cntk_output))][0]
# Create Caffe layer and call forward
cls_prob_caffe = cls_prob.reshape(cls_prob_shape_caffe)
bottom = [
np.array([cls_prob_caffe]),
np.array([rpn_bbox_pred]),
np.array([im_info])
]
top = None # handled through return statement in caffe layer for unit testing
param_str = "'feat_stride': 16"
caffe_layer = CaffeProposalLayer()
caffe_layer.set_param_str(param_str)
caffe_layer.setup(bottom, top)
caffe_output = caffe_layer.forward(bottom, top)
caffe_proposals = caffe_output[:, 1:]
# assert that results are exactly the same
assert cntk_proposals.shape == caffe_proposals.shape
assert np.allclose(cntk_proposals, caffe_proposals, rtol=0.0, atol=0.0)
print("Verified ProposalLayer")
def test_ext_train(tmpdir):
dim = 4
p = C.parameter(shape=(dim, ), init=10)
i = C.sequence.input_variable(dim, needs_gradient=True, name='i_var')
m = MyPlus(i, C.constant(3), 'my_plus')
# keeping m unwrapped since we need to access its member variables
z = C.user_function(m) + p
momentum_time_constant = C.momentum_as_time_constant_schedule(1100)
lr_per_sample = C.learning_parameter_schedule(0.007, minibatch_size=1)
trainer = C.Trainer(z, (z + 0, z + 0), [
C.momentum_sgd(z.parameters,
lr_per_sample,
momentum_time_constant,
True,
minibatch_size=0)
])
i = 0
while i < 100:
i += 1
input_data = np.random.rand(dim)
trainer.train_minibatch([input_data])
assert m.forward_calls == m.backward_calls == 100
filepath = str(tmpdir / 'test_ext_train.dat')
z.save(filepath)
buf = open(filepath, 'rb').read()
# this is only need for Python 2.7
# (which does not distinguish between bytes and strings)
if isinstance(buf, str):
buf = bytearray(buf)
z1 = Function.load(buf)
m1 = z1.find_by_name('my_plus')
# m1 is an instance of UserFunction, cannot directly downcast it to MyPlus,
# using serialize as workaround:
state = m1.serialize()['state']
assert state['forward_calls'] == state['backward_calls'] == 100
def test_lstm_over_lstm_thought_vectors_2(device_id):
dev = cntk_device(device_id)
input_vocab_size=3
emb_dim = 2
hidden_dim = 2
num_labels = 2
utterances_input = C.sequence.input_variable((input_vocab_size), is_sparse=True, name='utterances')
conversation_lengths_input = C.input_variable((), name='conversation_sequence_lengths')
label_input = C.sequence.input_variable(num_labels, is_sparse=True, sequence_axis=C.Axis('label_sequence'), name='labels')
with C.default_options(initial_state=0.1):
model = C.layers.Embedding(emb_dim, name='embed')(utterances_input)
model = C.layers.Recurrence(C.layers.LSTM(hidden_dim), go_backwards=False)(model)
model = C.sequence.last(model)
model = C.user_function(UtteranceBatchReshape(model, conversation_lengths_input))
model = C.to_sequence_like(model, label_input)
model = C.layers.Recurrence(C.layers.LSTM(hidden_dim), go_backwards=False)(model)
model = C.layers.Dense(num_labels, name='classify')(model)
z = model
ce = C.cross_entropy_with_softmax(z, label_input)
sentinel_utt_data = C.NDArrayView.from_csr(_to_csr([[0, 0, 1]]), device=C.cpu())
c1_utt1_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 1], [0, 1, 0], [1, 0, 0]]), device=C.cpu())
c1_utt2_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 0], [0, 1, 1]]), device=C.cpu())
c1_utt3_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 1], [0, 1, 0]]), device=C.cpu())
c2_utt1_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 1]]), device=C.cpu())
c3_utt1_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 0], [0, 1, 1], [1, 0, 0]]), device=C.cpu())
c3_utt2_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 0]]), device=C.cpu())
all_utt_data = C.Value.create(C.sequence.input_variable((input_vocab_size), is_sparse=True), [c1_utt1_data, c1_utt2_data, c1_utt3_data, c2_utt1_data, sentinel_utt_data, sentinel_utt_data, c3_utt1_data, c3_utt2_data, sentinel_utt_data], device=C.cpu()).data
conversation_lengths_data = np.asarray([3, 1, 2], dtype=np.float32)
seq1_label_data = [[0, 1], [0, 1], [1, 0]]
seq2_label_data = [[1, 0]]
seq3_label_data = [[1, 0], [0, 1]]
label_data = [_to_csr(seq1_label_data), _to_csr(seq2_label_data), _to_csr(seq3_label_data)]
param_grads, loss_result = ce.grad({utterances_input : all_utt_data, label_input : label_data, conversation_lengths_input : conversation_lengths_data},
wrt=ce.parameters, outputs=[ce], as_numpy=False)
loss_result = loss_result.as_sequences()
absolute_tolerance = 0.01
assert np.allclose(loss_result[0], [[0.678914], [0.668076], [0.728129]], atol=absolute_tolerance)
assert np.allclose(loss_result[1], [[0.679029]], atol=absolute_tolerance)
assert np.allclose(loss_result[2], [[0.705393], [0.674243]], atol=absolute_tolerance)
def test_ext_lambdafunc(tmpdir):
dim = 4
class CallbackCounter(object):
def __init__(self):
self.count = 0
def inc(self, arg):
self.count += 1
cb = CallbackCounter()
p = C.parameter(shape=(dim,), init=1)
i = C.input_variable(dim, needs_gradient=True, name='i_var')
k = i * p
m = LambdaFunc(k,
when=lambda arg: np.sum(arg) > 1,
execute=cb.inc)
m = C.user_function(m)
z0 = m + 0
filepath = str(tmpdir / 'test_ext_lambdafunc.dat')
z0.save(filepath)
Function.register_udf_deserialize_callback('conditional_exec_lambda',
lambda x, *unused: LambdaFunc(x, when=lambda arg: np.sum(arg) > 1, execute=cb.inc))
z = Function.load(filepath)
momentum_time_constant = C.momentum_as_time_constant_schedule(1100)
lr_per_sample = C.learning_parameter_schedule(0.007, minibatch_size = 1)
trainer = C.Trainer(z, (z + 0, z + 0), [C.momentum_sgd(z.parameters,
lr_per_sample,
momentum_time_constant,
True)])
i = 0
input_data = 0.1 * np.ones(dim)
trainer.train_minibatch([input_data])
assert cb.count == 0
input_data = 0.3 * np.ones(dim)
trainer.train_minibatch([input_data])
assert cb.count == 1
def test_lstm_over_lstm_thought_vectors_2(device_id):
dev = cntk_device(device_id)
input_vocab_size=3
emb_dim = 2
hidden_dim = 2
num_labels = 2
utterances_input = C.sequence.input_variable((input_vocab_size), is_sparse=True, name='utterances')
conversation_lengths_input = C.input_variable((), name='conversation_sequence_lengths')
label_input = C.sequence.input_variable(num_labels, is_sparse=True, sequence_axis=C.Axis('label_sequence'), name='labels')
with C.default_options(initial_state=0.1):
model = C.layers.Embedding(emb_dim, name='embed')(utterances_input)
model = C.layers.Recurrence(C.layers.LSTM(hidden_dim), go_backwards=False)(model)
model = C.sequence.last(model)
model = C.user_function(UtteranceBatchReshape(model, conversation_lengths_input))
model = C.to_sequence_like(model, label_input)
model = C.layers.Recurrence(C.layers.LSTM(hidden_dim), go_backwards=False)(model)
model = C.layers.Dense(num_labels, name='classify')(model)
z = model
ce = C.cross_entropy_with_softmax(z, label_input)
sentinel_utt_data = C.NDArrayView.from_csr(_to_csr([[0, 0, 1]]), device=C.cpu())
c1_utt1_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 1], [0, 1, 0], [1, 0, 0]]), device=C.cpu())
c1_utt2_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 0], [0, 1, 1]]), device=C.cpu())
c1_utt3_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 1], [0, 1, 0]]), device=C.cpu())
c2_utt1_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 1]]), device=C.cpu())
c3_utt1_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 0], [0, 1, 1], [1, 0, 0]]), device=C.cpu())
c3_utt2_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 0]]), device=C.cpu())
all_utt_data = C.Value.create(C.sequence.input_variable((input_vocab_size), is_sparse=True), [c1_utt1_data, c1_utt2_data, c1_utt3_data, c2_utt1_data, sentinel_utt_data, sentinel_utt_data, c3_utt1_data, c3_utt2_data, sentinel_utt_data], device=C.cpu()).data
conversation_lengths_data = np.asarray([3, 1, 2], dtype=np.float32)
seq1_label_data = [[0, 1], [0, 1], [1, 0]]
seq2_label_data = [[1, 0]]
seq3_label_data = [[1, 0], [0, 1]]
label_data = [_to_csr(seq1_label_data), _to_csr(seq2_label_data), _to_csr(seq3_label_data)]
param_grads, loss_result = ce.grad({utterances_input : all_utt_data, label_input : label_data, conversation_lengths_input : conversation_lengths_data},
wrt=ce.parameters, outputs=[ce], as_numpy=False)
loss_result = loss_result.as_sequences()
absolute_tolerance = 0.01
assert np.allclose(loss_result[0], [[0.678914], [0.668076], [0.728129]], atol=absolute_tolerance)
assert np.allclose(loss_result[1], [[0.679029]], atol=absolute_tolerance)
assert np.allclose(loss_result[2], [[0.705393], [0.674243]], atol=absolute_tolerance)
def test_ext_lambdafunc(tmpdir):
dim = 4
class CallbackCounter(object):
def __init__(self):
self.count = 0
def inc(self, arg):
self.count += 1
cb = CallbackCounter()
p = C.parameter(shape=(dim,), init=1)
i = C.input_variable(dim, needs_gradient=True, name='i_var')
k = i * p
m = LambdaFunc(k,
when=lambda arg: np.sum(arg) > 1,
execute=cb.inc)
m = C.user_function(m)
z0 = m + 0
filepath = str(tmpdir / 'test_ext_lambdafunc.dat')
z0.save(filepath)
Function.register_udf_deserialize_callback('conditional_exec_lambda',
lambda x, *unused: LambdaFunc(x, when=lambda arg: np.sum(arg) > 1, execute=cb.inc))
z = Function.load(filepath)
momentum_time_constant = C.momentum_as_time_constant_schedule(1100)
lr_per_sample = C.learning_rate_schedule(0.007, C.UnitType.sample)
trainer = C.Trainer(z, (z + 0, z + 0), [C.momentum_sgd(z.parameters,
lr_per_sample,
momentum_time_constant,
True)])
i = 0
input_data = 0.1 * np.ones(dim)
trainer.train_minibatch([input_data])
assert cb.count == 0
input_data = 0.3 * np.ones(dim)
trainer.train_minibatch([input_data])
assert cb.count == 1
def create_proposal_target_layer(rpn_rois, scaled_gt_boxes, cfg):
'''
Creates a proposal target layer that is used for training an object detection network as proposed in the "Faster R-CNN" paper:
Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun:
"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks"
Assigns object detection proposals to ground-truth targets.
Produces proposal classification labels and bounding-box regression targets.
It also adds gt_boxes to candidates and samples fg and bg rois for training.
Args:
rpn_rois: The proposed ROIs, e.g. from a region proposal network
scaled_gt_boxes: The ground truth boxes as (x1, y1, x2, y2, label). Coordinates are absolute pixels wrt. the input image.
num_classes: The number of classes in the data set
Returns:
rpn_target_rois - a set of rois containing the ground truth and a number of sampled fg and bg ROIs
label_targets - the target labels for the rois
bbox_targets - the regression coefficient targets for the rois
bbox_inside_weights - the weights for the regression loss
'''
ptl_param_string = "'num_classes': {}".format(cfg["DATA"].NUM_CLASSES)
ptl = user_function(
ProposalTargetLayer(rpn_rois,
scaled_gt_boxes,
batch_size=cfg.NUM_ROI_PROPOSALS,
fg_fraction=cfg["TRAIN"].FG_FRACTION,
normalize_targets=cfg.BBOX_NORMALIZE_TARGETS,
normalize_means=cfg.BBOX_NORMALIZE_MEANS,
normalize_stds=cfg.BBOX_NORMALIZE_STDS,
fg_thresh=cfg["TRAIN"].FG_THRESH,
bg_thresh_hi=cfg["TRAIN"].BG_THRESH_HI,
bg_thresh_lo=cfg["TRAIN"].BG_THRESH_LO,
param_str=ptl_param_string))
# use an alias if you need to access the outputs, e.g., when cloning a trained network
rois = alias(ptl.outputs[0], name='rpn_target_rois')
label_targets = ptl.outputs[1]
bbox_targets = ptl.outputs[2]
bbox_inside_weights = ptl.outputs[3]
return rois, label_targets, bbox_targets, bbox_inside_weights
def test_ext_train(tmpdir):
dim = 4
p = C.parameter(shape=(dim,), init=10)
i = C.sequence.input_variable(dim, needs_gradient=True, name='i_var')
m = MyPlus(i, C.constant(3), 'my_plus')
# keeping m unwrapped since we need to access its member variables
z = C.user_function(m) + p
momentum_time_constant = C.momentum_as_time_constant_schedule(1100)
lr_per_sample = C.learning_rate_schedule(0.007, C.UnitType.sample)
trainer = C.Trainer(z, (z + 0, z + 0),
[C.momentum_sgd(z.parameters, lr_per_sample, momentum_time_constant,
True)])
i = 0
while i < 100:
i += 1
input_data = np.random.rand(dim)
trainer.train_minibatch([input_data])
assert m.forward_calls == m.backward_calls == 100
filepath = str(tmpdir / 'test_ext_train.dat')
z.save(filepath)
buf = open(filepath, 'rb').read()
# this is only need for Python 2.7
# (which does not distinguish between bytes and strings)
if isinstance(buf, str):
buf = bytearray(buf)
z1 = Function.load(buf)
m1 = z1.find_by_name('my_plus')
# m1 is an instance of UserFunction, cannot directly downcast it to MyPlus,
# using serialize as workaround:
state = m1.serialize()['state']
assert state['forward_calls'] == state['backward_calls'] == 100
def create_proposal_target_layer(rpn_rois, scaled_gt_boxes, cfg):
'''
Creates a proposal target layer that is used for training an object detection network as proposed in the "Faster R-CNN" paper:
Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun:
"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks"
Assigns object detection proposals to ground-truth targets.
Produces proposal classification labels and bounding-box regression targets.
It also adds gt_boxes to candidates and samples fg and bg rois for training.
Args:
rpn_rois: The proposed ROIs, e.g. from a region proposal network
scaled_gt_boxes: The ground truth boxes as (x1, y1, x2, y2, label). Coordinates are absolute pixels wrt. the input image.
num_classes: The number of classes in the data set
Returns:
rpn_target_rois - a set of rois containing the ground truth and a number of sampled fg and bg ROIs
label_targets - the target labels for the rois
bbox_targets - the regression coefficient targets for the rois
bbox_inside_weights - the weights for the regression loss
'''
ptl_param_string = "'num_classes': {}".format(cfg["DATA"].NUM_CLASSES)
ptl = user_function(ProposalTargetLayer(rpn_rois, scaled_gt_boxes,
batch_size=cfg.NUM_ROI_PROPOSALS,
fg_fraction=cfg["TRAIN"].FG_FRACTION,
normalize_targets=cfg.BBOX_NORMALIZE_TARGETS,
normalize_means=cfg.BBOX_NORMALIZE_MEANS,
normalize_stds=cfg.BBOX_NORMALIZE_STDS,
fg_thresh=cfg["TRAIN"].FG_THRESH,
bg_thresh_hi=cfg["TRAIN"].BG_THRESH_HI,
bg_thresh_lo=cfg["TRAIN"].BG_THRESH_LO,
param_str=ptl_param_string))
# use an alias if you need to access the outputs, e.g., when cloning a trained network
rois = alias(ptl.outputs[0], name='rpn_target_rois')
label_targets = ptl.outputs[1]
bbox_targets = ptl.outputs[2]
bbox_inside_weights = ptl.outputs[3]
return rois, label_targets, bbox_targets, bbox_inside_weights
def test_proposal_target_layer():
num_rois = 400
all_rois_shape_cntk = (num_rois,4)
num_gt_boxes = 50
gt_boxes_shape_cntk = (num_gt_boxes,5)
im_info = [1000, 1000, 1]
# Create input tensors with values
x1y1 = np.random.random_sample((num_rois, 2)) * 500
wh = np.random.random_sample((num_rois, 2)) * 400
x2y2 = x1y1 + wh + 50
all_rois = np.hstack((x1y1, x2y2)).astype(np.float32)
#all_rois = np.random.random_sample(all_rois_shape_cntk).astype(np.float32)
x1y1 = np.random.random_sample((num_gt_boxes, 2)) * 500
wh = np.random.random_sample((num_gt_boxes, 2)) * 400
x2y2 = x1y1 + wh + 50
label = np.random.random_sample((num_gt_boxes, 1))
label = (label * 17.0)
gt_boxes = np.hstack((x1y1, x2y2, label)).astype(np.float32)
# Create CNTK layer and call forward
all_rois_var = input_variable(all_rois_shape_cntk)
gt_boxes_var = input_variable(gt_boxes_shape_cntk)
cntk_layer = user_function(CntkProposalTargetLayer(all_rois_var, gt_boxes_var, param_str="'num_classes': 17", deterministic=True))
state, cntk_output = cntk_layer.forward({all_rois_var: [all_rois], gt_boxes_var: [gt_boxes]})
roi_key = [k for k in cntk_output if 'rpn_target_rois_raw' in str(k)][0]
labels_key = [k for k in cntk_output if 'label_targets_raw' in str(k)][0]
bbox_key = [k for k in cntk_output if 'bbox_targets_raw' in str(k)][0]
bbox_w_key = [k for k in cntk_output if 'bbox_inside_w_raw' in str(k)][0]
cntk_rois = cntk_output[roi_key][0]
cntk_labels_one_hot = cntk_output[labels_key][0]
cntk_bbox_targets = cntk_output[bbox_key][0]
cntk_bbox_inside_weights = cntk_output[bbox_w_key][0]
cntk_labels = np.argmax(cntk_labels_one_hot, axis=1)
# Create Caffe layer and call forward
zeros = np.zeros((all_rois.shape[0], 1), dtype=gt_boxes.dtype)
all_rois_caffe = np.hstack((zeros, all_rois))
bottom = [np.array(all_rois_caffe),np.array(gt_boxes)]
top = None # handled through return statement in caffe layer for unit testing
param_str = "'num_classes': 17"
caffe_layer = CaffeProposalTargetLayer()
caffe_layer.set_param_str(param_str)
caffe_layer.setup(bottom, top)
caffe_layer.set_deterministic_mode()
caffe_rois, caffe_labels, caffe_bbox_targets, caffe_bbox_inside_weights = caffe_layer.forward(bottom, top)
caffe_rois = caffe_rois[:,1:]
num_caffe_rois = caffe_rois.shape[0]
cntk_rois = cntk_rois[:num_caffe_rois,:]
cntk_labels = cntk_labels[:num_caffe_rois]
cntk_bbox_targets = cntk_bbox_targets[:num_caffe_rois,:]
cntk_bbox_inside_weights = cntk_bbox_inside_weights[:num_caffe_rois,:]
# assert that results are exactly the same
assert cntk_rois.shape == caffe_rois.shape
assert cntk_labels.shape == caffe_labels.shape
assert cntk_bbox_targets.shape == caffe_bbox_targets.shape
assert cntk_bbox_inside_weights.shape == caffe_bbox_inside_weights.shape
caffe_labels = [int(x) for x in caffe_labels]
assert np.allclose(cntk_rois, caffe_rois, rtol=0.0, atol=0.0)
assert np.allclose(cntk_labels, caffe_labels, rtol=0.0, atol=0.0)
assert np.allclose(cntk_bbox_targets, caffe_bbox_targets, rtol=0.0, atol=0.0)
assert np.allclose(cntk_bbox_inside_weights, caffe_bbox_inside_weights, rtol=0.0, atol=0.0)
print("Verified ProposalTargetLayer")
def print_node(v):
return C.user_function(LambdaFunc(v))
def __cntk_det__(m):
return C.user_function(__cntk_class_det__(m))
def create_rpn(conv_out, scaled_gt_boxes, im_info, add_loss_functions=True,
proposal_layer_param_string=None):
'''
Creates a region proposal network for object detection as proposed in the "Faster R-CNN" paper:
Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun:
"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks"
Outputs object detection proposals by applying estimated bounding-box
transformations to a set of regular boxes (called "anchors").
Args:
conv_out: The convolutional feature map, i.e. the output of the conv layers from the pretrained classification network
scaled_gt_boxes: The ground truth boxes as (x1, y1, x2, y2, label). Coordinates are absolute pixels wrt. the input image.
im_info: (image_widht, image_height, image_scale) as CNTK variable or constant
add_loss_functions: If set to True rpn_losses will be returned, otherwise None is returned for the losses
proposal_layer_param_string: A yaml parameter string that is passed to the proposal layer.
Returns:
rpn_rois - the proposed ROIs
rpn_losses - the losses (SmoothL1 loss for bbox regression plus cross entropy for objectness)
'''
# RPN network
# init = 'normal', initValueScale = 0.01, initBias = 0.1
rpn_conv_3x3 = Convolution((3, 3), 256, activation=relu, pad=True, strides=1,
init = normal(scale=0.01), init_bias=0.1)(conv_out)
rpn_cls_score = Convolution((1, 1), 18, activation=None, name="rpn_cls_score",
init = normal(scale=0.01), init_bias=0.1)(rpn_conv_3x3) # 2(bg/fg) * 9(anchors)
rpn_bbox_pred = Convolution((1, 1), 36, activation=None, name="rpn_bbox_pred",
init = normal(scale=0.01), init_bias=0.1)(rpn_conv_3x3) # 4(coords) * 9(anchors)
# apply softmax to get (bg, fg) probabilities and reshape predictions back to grid of (18, H, W)
num_predictions = int(np.prod(rpn_cls_score.shape) / 2)
rpn_cls_score_rshp = reshape(rpn_cls_score, (2, num_predictions))
rpn_cls_prob = softmax(rpn_cls_score_rshp, axis=0, name="objness_softmax")
rpn_cls_prob_reshape = reshape(rpn_cls_prob, rpn_cls_score.shape)
# proposal layer
rpn_rois_raw = user_function(ProposalLayer(rpn_cls_prob_reshape, rpn_bbox_pred, im_info, param_str=proposal_layer_param_string))
rpn_rois = alias(rpn_rois_raw, name='rpn_rois')
rpn_losses = None
if(add_loss_functions):
# RPN targets
# Comment: rpn_cls_score is only passed vvv to get width and height of the conv feature map ...
atl = user_function(AnchorTargetLayer(rpn_cls_score, scaled_gt_boxes, im_info, param_str=proposal_layer_param_string))
rpn_labels = atl.outputs[0]
rpn_bbox_targets = atl.outputs[1]
rpn_bbox_inside_weights = atl.outputs[2]
# For loss functions: ignore label predictions for the 'ignore label',
# i.e. set target and prediction to 0 --> needs to be softmaxed before
rpn_labels_rshp = reshape(rpn_labels, (1, num_predictions))
ignore = user_function(IgnoreLabel(rpn_cls_prob, rpn_labels_rshp, ignore_label=-1))
rpn_cls_prob_ignore = ignore.outputs[0]
fg_targets = ignore.outputs[1]
bg_targets = 1 - fg_targets
rpn_labels_ignore = splice(bg_targets, fg_targets, axis=0)
# RPN losses
rpn_loss_cls = cross_entropy_with_softmax(rpn_cls_prob_ignore, rpn_labels_ignore, axis=0)
rpn_loss_bbox = user_function(SmoothL1Loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights))
rpn_losses = plus(reduce_sum(rpn_loss_cls), reduce_sum(rpn_loss_bbox), name="rpn_losses")
return rpn_rois, rpn_losses
def test_udf_op_name():
dim = 4
i = C.input_variable(dim, needs_gradient=True, name='i_var')
m = C.user_function(MyPlus(i, C.constant(3)))
assert str(m.root_function) != ''
def dense_layer(inp, output_dim, nonlinearity):
r = linear_layer(inp, output_dim)
r = nonlinearity(r)
if isinstance(r, UserFunction):
r = C.user_function(r)
return r
def _(x):
return C.user_function(__cntk_class_mvn_log_prob__(x, mu, sig))
def create_rpn(conv_out, scaled_gt_boxes, im_info, add_loss_functions=True,
proposal_layer_param_string=None, conv_bias_init=0.0):
'''
Creates a region proposal network for object detection as proposed in the "Faster R-CNN" paper:
Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun:
"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks"
Outputs object detection proposals by applying estimated bounding-box
transformations to a set of regular boxes (called "anchors").
Args:
conv_out: The convolutional feature map, i.e. the output of the conv layers from the pretrained classification network
scaled_gt_boxes: The ground truth boxes as (x1, y1, x2, y2, label). Coordinates are absolute pixels wrt. the input image.
im_info: A CNTK variable or constant containing
(pad_width, pad_height, scaled_image_width, scaled_image_height, orig_img_width, orig_img_height)
e.g. (1000, 1000, 1000, 600, 500, 300) for an original image of 600x300 that is scaled and padded to 1000x1000
add_loss_functions: If set to True rpn_losses will be returned, otherwise None is returned for the losses
proposal_layer_param_string: A yaml parameter string that is passed to the proposal layer.
Returns:
rpn_rois - the proposed ROIs
rpn_losses - the losses (SmoothL1 loss for bbox regression plus cross entropy for objectness)
'''
# RPN network
# init = 'normal', initValueScale = 0.01, initBias = 0.1
num_channels = cfg["CNTK"].RPN_NUM_CHANNELS
rpn_conv_3x3 = Convolution((3, 3), num_channels, activation=relu, pad=True, strides=1,
init = normal(scale=0.01), init_bias=conv_bias_init)(conv_out)
rpn_cls_score = Convolution((1, 1), 18, activation=None, name="rpn_cls_score",
init = normal(scale=0.01), init_bias=conv_bias_init)(rpn_conv_3x3) # 2(bg/fg) * 9(anchors)
rpn_bbox_pred = Convolution((1, 1), 36, activation=None, name="rpn_bbox_pred",
init = normal(scale=0.01), init_bias=conv_bias_init)(rpn_conv_3x3) # 4(coords) * 9(anchors)
# apply softmax to get (bg, fg) probabilities and reshape predictions back to grid of (18, H, W)
num_predictions = int(rpn_cls_score.shape[0] / 2)
rpn_cls_score_rshp = reshape(rpn_cls_score, (2, num_predictions, rpn_cls_score.shape[1], rpn_cls_score.shape[2]), name="rpn_cls_score_rshp")
p_rpn_cls_score_rshp = cntk.placeholder()
rpn_cls_sm = softmax(p_rpn_cls_score_rshp, axis=0)
rpn_cls_prob = cntk.as_block(rpn_cls_sm, [(p_rpn_cls_score_rshp, rpn_cls_score_rshp)], 'Softmax', 'rpn_cls_prob')
rpn_cls_prob_reshape = reshape(rpn_cls_prob, rpn_cls_score.shape, name="rpn_cls_prob_reshape")
# proposal layer
rpn_rois_raw = user_function(ProposalLayer(rpn_cls_prob_reshape, rpn_bbox_pred, im_info, param_str=proposal_layer_param_string))
rpn_rois = alias(rpn_rois_raw, name='rpn_rois')
rpn_losses = None
if(add_loss_functions):
# RPN targets
# Comment: rpn_cls_score is only passed vvv to get width and height of the conv feature map ...
atl = user_function(AnchorTargetLayer(rpn_cls_score, scaled_gt_boxes, im_info, param_str=proposal_layer_param_string))
rpn_labels = atl.outputs[0]
rpn_bbox_targets = atl.outputs[1]
rpn_bbox_inside_weights = atl.outputs[2]
# classification loss
p_rpn_labels = cntk.placeholder()
p_rpn_cls_score_rshp = cntk.placeholder()
keeps = cntk.greater_equal(p_rpn_labels, 0.0)
fg_labels = element_times(p_rpn_labels, keeps, name="fg_targets")
bg_labels = minus(1, fg_labels, name="bg_targets")
rpn_labels_ignore = splice(bg_labels, fg_labels, axis=0)
rpn_ce = cross_entropy_with_softmax(p_rpn_cls_score_rshp, rpn_labels_ignore, axis=0)
rpn_loss_cls = element_times(rpn_ce, keeps)
# The terms that are accounted for in the cls loss are those that have a label >= 0
cls_num_terms = reduce_sum(keeps)
cls_normalization_factor = 1.0 / cls_num_terms
normalized_rpn_cls_loss = reduce_sum(rpn_loss_cls) * cls_normalization_factor
reduced_rpn_loss_cls = cntk.as_block(normalized_rpn_cls_loss,
[(p_rpn_labels, rpn_labels), (p_rpn_cls_score_rshp, rpn_cls_score_rshp)],
'CE_with_ignore', 'norm_rpn_cls_loss')
# regression loss
p_rpn_bbox_pred = cntk.placeholder()
p_rpn_bbox_targets = cntk.placeholder()
p_rpn_bbox_inside_weights = cntk.placeholder()
rpn_loss_bbox = SmoothL1Loss(cfg["CNTK"].SIGMA_RPN_L1, p_rpn_bbox_pred, p_rpn_bbox_targets, p_rpn_bbox_inside_weights, 1.0)
# The bbox loss is normalized by the rpn batch size
bbox_normalization_factor = 1.0 / cfg["TRAIN"].RPN_BATCHSIZE
normalized_rpn_bbox_loss = reduce_sum(rpn_loss_bbox) * bbox_normalization_factor
reduced_rpn_loss_bbox = cntk.as_block(normalized_rpn_bbox_loss,
[(p_rpn_bbox_pred, rpn_bbox_pred), (p_rpn_bbox_targets, rpn_bbox_targets),
(p_rpn_bbox_inside_weights, rpn_bbox_inside_weights)],
'SmoothL1Loss', 'norm_rpn_bbox_loss')
rpn_losses = plus(reduced_rpn_loss_cls, reduced_rpn_loss_bbox, name="rpn_losses")
return rpn_rois, rpn_losses
def create_rpn(conv_out,
scaled_gt_boxes,
im_info,
cfg,
add_loss_functions=True):
'''
Creates a region proposal network for object detection as proposed in the "Faster R-CNN" paper:
Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun:
"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks"
Outputs object detection proposals by applying estimated bounding-box
transformations to a set of regular boxes (called "anchors").
Args:
conv_out: The convolutional feature map, i.e. the output of the conv layers from the pretrained classification network
scaled_gt_boxes: The ground truth boxes as (x1, y1, x2, y2, label). Coordinates are absolute pixels wrt. the input image.
im_info: A CNTK variable or constant containing
(pad_width, pad_height, scaled_image_width, scaled_image_height, orig_img_width, orig_img_height)
e.g. (1000, 1000, 1000, 600, 500, 300) for an original image of 600x300 that is scaled and padded to 1000x1000
cfg: The configuration dictionary
add_loss_functions: If set to True rpn_losses will be returned, otherwise None is returned for the losses
Returns:
rpn_rois - the proposed ROIs
rpn_losses - the losses (SmoothL1 loss for bbox regression plus cross entropy for objectness)
'''
# RPN network
# init = 'normal', initValueScale = 0.01, initBias = 0.1
num_channels = cfg["MODEL"].RPN_NUM_CHANNELS
rpn_conv_3x3 = Convolution((3, 3),
num_channels,
activation=relu,
pad=True,
strides=1,
init=normal(scale=0.01),
init_bias=0.0)(conv_out)
rpn_cls_score = Convolution(
(1, 1),
18,
activation=None,
name="rpn_cls_score",
init=normal(scale=0.01),
init_bias=0.0)(rpn_conv_3x3) # 2(bg/fg) * 9(anchors)
rpn_bbox_pred = Convolution(
(1, 1),
36,
activation=None,
name="rpn_bbox_pred",
init=normal(scale=0.01),
init_bias=0.0)(rpn_conv_3x3) # 4(coords) * 9(anchors)
# apply softmax to get (bg, fg) probabilities and reshape predictions back to grid of (18, H, W)
num_predictions = int(rpn_cls_score.shape[0] / 2)
rpn_cls_score_rshp = reshape(
rpn_cls_score,
(2, num_predictions, rpn_cls_score.shape[1], rpn_cls_score.shape[2]),
name="rpn_cls_score_rshp")
p_rpn_cls_score_rshp = cntk.placeholder()
rpn_cls_sm = softmax(p_rpn_cls_score_rshp, axis=0)
rpn_cls_prob = cntk.as_block(rpn_cls_sm,
[(p_rpn_cls_score_rshp, rpn_cls_score_rshp)],
'Softmax', 'rpn_cls_prob')
rpn_cls_prob_reshape = reshape(rpn_cls_prob,
rpn_cls_score.shape,
name="rpn_cls_prob_reshape")
# proposal layer
rpn_rois = create_proposal_layer(rpn_cls_prob_reshape, rpn_bbox_pred,
im_info, cfg)
rpn_losses = None
if (add_loss_functions):
# RPN targets
# Comment: rpn_cls_score is only passed vvv to get width and height of the conv feature map ...
proposal_layer_params = "'feat_stride': {}\n'scales':\n - {}". \
format(cfg["MODEL"].FEATURE_STRIDE, "\n - ".join([str(v) for v in cfg["DATA"].PROPOSAL_LAYER_SCALES]))
atl = user_function(
AnchorTargetLayer(
rpn_cls_score,
scaled_gt_boxes,
im_info,
rpn_batch_size=cfg["TRAIN"].RPN_BATCHSIZE,
rpn_fg_fraction=cfg["TRAIN"].RPN_FG_FRACTION,
clobber_positives=cfg["TRAIN"].RPN_CLOBBER_POSITIVES,
positive_overlap=cfg["TRAIN"].RPN_POSITIVE_OVERLAP,
negative_overlap=cfg["TRAIN"].RPN_NEGATIVE_OVERLAP,
param_str=proposal_layer_params))
rpn_labels = atl.outputs[0]
rpn_bbox_targets = atl.outputs[1]
rpn_bbox_inside_weights = atl.outputs[2]
# classification loss
p_rpn_labels = cntk.placeholder()
p_rpn_cls_score_rshp = cntk.placeholder()
keeps = cntk.greater_equal(p_rpn_labels, 0.0)
fg_labels = element_times(p_rpn_labels, keeps, name="fg_targets")
bg_labels = minus(1, fg_labels, name="bg_targets")
rpn_labels_ignore = splice(bg_labels, fg_labels, axis=0)
rpn_ce = cross_entropy_with_softmax(p_rpn_cls_score_rshp,
rpn_labels_ignore,
axis=0)
rpn_loss_cls = element_times(rpn_ce, keeps)
# The terms that are accounted for in the cls loss are those that have a label >= 0
cls_num_terms = reduce_sum(keeps)
cls_normalization_factor = 1.0 / cls_num_terms
normalized_rpn_cls_loss = reduce_sum(
rpn_loss_cls) * cls_normalization_factor
reduced_rpn_loss_cls = cntk.as_block(
normalized_rpn_cls_loss,
[(p_rpn_labels, rpn_labels),
(p_rpn_cls_score_rshp, rpn_cls_score_rshp)], 'CE_with_ignore',
'norm_rpn_cls_loss')
# regression loss
p_rpn_bbox_pred = cntk.placeholder()
p_rpn_bbox_targets = cntk.placeholder()
p_rpn_bbox_inside_weights = cntk.placeholder()
rpn_loss_bbox = SmoothL1Loss(cfg.SIGMA_RPN_L1, p_rpn_bbox_pred,
p_rpn_bbox_targets,
p_rpn_bbox_inside_weights, 1.0)
# The bbox loss is normalized by the rpn batch size
bbox_normalization_factor = 1.0 / cfg["TRAIN"].RPN_BATCHSIZE
normalized_rpn_bbox_loss = reduce_sum(
rpn_loss_bbox) * bbox_normalization_factor
reduced_rpn_loss_bbox = cntk.as_block(
normalized_rpn_bbox_loss,
[(p_rpn_bbox_pred, rpn_bbox_pred),
(p_rpn_bbox_targets, rpn_bbox_targets),
(p_rpn_bbox_inside_weights, rpn_bbox_inside_weights)],
'SmoothL1Loss', 'norm_rpn_bbox_loss')
rpn_losses = plus(reduced_rpn_loss_cls,
reduced_rpn_loss_bbox,
name="rpn_losses")
return rpn_rois, rpn_losses
def dense_layer(inp, output_dim, nonlinearity):
r = linear_layer(inp, output_dim)
r = nonlinearity(r)
if isinstance(r, UserFunction):
r = C.user_function(r)
return r
def test_no_deadlock_init_outputs():
x = C.input_variable((3, C.FreeDimension, 2), name='x')
from cntk import user_function
with pytest.raises(RuntimeError):
s = user_function(FaultyUserFunc(x))
