def forward(net, input_data, deploy=False):
"""Defines and creates the ReInspect network given the net, input data
and configurations."""
net.clear_forward()
if deploy:
image = np.array(input_data["image"])
else:
image = np.array(input_data["image"])
label = np.array(input_data["label"])
net.f(NumpyData("label", data=label))
net.f(NumpyData("image", data=image))
generate_decapitated_alexnet(net)
net.f(
InnerProduct(name="fc8_dish",
bottoms=["fc7"],
param_lr_mults=[1.0 * 10, 2.0 * 10],
param_decay_mults=[1.0, 0.0],
weight_filler=Filler("gaussian", 0.01),
bias_filler=Filler("constant", 0.0),
num_output=128))
net.f(Softmax("dish_probs", bottoms=["fc8_dish"]))
if not deploy:
net.f(SoftmaxWithLoss(name="loss", bottoms=["fc8_dish", "label"]))
# net.f(Accuracy(name="dish_accuracy",bottoms=["fc8_dish_23", "label"]))
if deploy:
probs = np.array(net.blobs["dish_probs"].data)
return probs
else:
return None
def generate_lstm_seeds(net, num_cells):
"""Generates the lstm seeds that are used as
input to the first lstm layer."""
net.f(NumpyData("lstm_hidden_seed",
np.zeros((net.blobs["lstm_input"].shape[0], num_cells))))
net.f(NumpyData("lstm_mem_seed",
np.zeros((net.blobs["lstm_input"].shape[0], num_cells))))
def forward(net, sentence_batches):
net.clear_forward()
batch = next(sentence_batches)
sentence_batch = pad_batch(batch)
length = min(sentence_batch.shape[1], 100)
assert length > 0
net.f(NumpyData('lstm_seed', np.zeros((batch_size, dimension))))
for step in range(length):
if step == 0:
prev_hidden = 'lstm_seed'
prev_mem = 'lstm_seed'
word = np.zeros(sentence_batch[:, 0].shape)
else:
prev_hidden = 'lstm%d_hidden' % (step - 1)
prev_mem = 'lstm%d_mem' % (step - 1)
word = sentence_batch[:, step - 1]
net.f(NumpyData('word%d' % step, word))
net.f(
Wordvec('wordvec%d' % step,
dimension,
vocab_size,
bottoms=['word%d' % step],
param_names=['wordvec_param']))
net.f(
Concat('lstm_concat%d' % step,
bottoms=[prev_hidden, 'wordvec%d' % step]))
net.f(
LstmUnit('lstm%d' % step,
bottoms=['lstm_concat%d' % step, prev_mem],
param_names=[
'lstm_input_value', 'lstm_input_gate',
'lstm_forget_gate', 'lstm_output_gate'
],
tops=['lstm%d_hidden' % step,
'lstm%d_mem' % step],
num_cells=dimension))
net.f(
Dropout('dropout%d' % step, 0.16,
bottoms=['lstm%d_hidden' % step]))
net.f(NumpyData('label%d' % step, sentence_batch[:, step]))
net.f(
InnerProduct('ip%d' % step,
vocab_size,
bottoms=['dropout%d' % step],
param_names=['softmax_ip_weights',
'softmax_ip_bias']))
net.f(
SoftmaxWithLoss('softmax_loss%d' % step,
ignore_label=zero_symbol,
bottoms=['ip%d' % step,
'label%d' % step]))
def generate_ground_truth_layers(net, box_flags, boxes):
"""Generates the NumpyData layers that output the box_flags and boxes
when not in deploy mode. box_flags = list of bitstring (e.g. [1,1,1,0,0])
encoding the number of bounding boxes in each cell, in unary,
boxes = a numpy array of the center_x, center_y, width and height
for each bounding box in each cell."""
old_shape = list(box_flags.shape)
new_shape = [old_shape[0] * old_shape[1]] + old_shape[2:]
net.f(NumpyData("box_flags", data=np.reshape(box_flags, new_shape)))
old_shape = list(boxes.shape)
new_shape = [old_shape[0] * old_shape[1]] + old_shape[2:]
net.f(NumpyData("boxes", data=np.reshape(boxes, new_shape)))
def eval_forward(net):
net.clear_forward()
output_words = []
net.f(NumpyData('lstm_hidden_prev', np.zeros((1, dimension))))
net.f(NumpyData('lstm_mem_prev', np.zeros((1, dimension))))
length = 150
for step in range(length):
net.clear_forward()
net.f(NumpyData('word', [0]))
prev_hidden = 'lstm_hidden_prev'
prev_mem = 'lstm_mem_prev'
if step == 0:
output = ord('.')
else:
output = softmax_choice(net.blobs['softmax'].data)
output_words.append(output)
net.blobs['word'].data[0] = output
net.f(
Wordvec('wordvec',
dimension,
vocab_size,
bottoms=['word'],
param_names=['wordvec_param']))
net.f(Concat('lstm_concat', bottoms=[prev_hidden, 'wordvec']))
net.f(
LstmUnit('lstm',
dimension,
bottoms=['lstm_concat', prev_mem],
param_names=[
'lstm_input_value', 'lstm_input_gate',
'lstm_forget_gate', 'lstm_output_gate'
],
tops=['lstm_hidden_next', 'lstm_mem_next']))
net.f(Dropout('dropout', 0.16, bottoms=['lstm_hidden_next']))
net.f(
InnerProduct('ip',
vocab_size,
bottoms=['dropout'],
param_names=['softmax_ip_weights',
'softmax_ip_bias']))
net.blobs['ip'].data[:] *= i_temperature
net.f(Softmax('softmax', bottoms=['ip']))
net.blobs['lstm_hidden_prev'].data_tensor.copy_from(
net.blobs['lstm_hidden_next'].data_tensor)
net.blobs['lstm_mem_prev'].data_tensor.copy_from(
net.blobs['lstm_mem_next'].data_tensor)
print ''.join([chr(x) for x in output_words])
def forward(net, input_data, net_config, deploy=False):
"""Defines and creates the ReInspect network given the net, input data
and configurations."""
net.clear_forward()
if deploy:
image = np.array(input_data["image"])
else:
image = np.array(input_data["image"])
box_flags = np.array(input_data["box_flags"])
boxes = np.array(input_data["boxes"])
numbers = np.array(input_data["numbers"])
net.f(NumpyData("image", data=image))
generate_decapitated_googlenet(net, net_config)
generate_intermediate_layers(net)
if not deploy:
generate_ground_truth_layers(net, box_flags, boxes)
generate_number_ground_truth_layers(net, numbers)
generate_lstm_seeds(net, net_config["lstm_num_cells"])
filler = Filler("uniform", net_config["init_range"])
concat_bottoms = {"score": [], "bbox": []}
lstm_params = (net_config["lstm_num_cells"], filler)
for step in range(net_config["max_len"]):
lstm_out = get_lstm_params(step)
generate_lstm(net, step, lstm_params, lstm_out,
net_config["dropout_ratio"])
generate_inner_products(net, step, filler)
concat_bottoms["score"].append("ip_conf%d" % step)
concat_bottoms["bbox"].append("ip_bbox%d" % step)
net.f(Concat("score_concat", bottoms=concat_bottoms["score"],
concat_dim=2))
net.f(Concat("bbox_concat", bottoms=concat_bottoms["bbox"], concat_dim=2))
generate_number_layers(net, step, filler, net_config["max_len"])
if not deploy:
generate_losses(net, net_config)
generate_number_losses(net, net_config)
if deploy:
bbox = [
np.array(net.blobs["ip_bbox%d" % j].data)
for j in range(net_config["max_len"])
]
conf = [
np.array(net.blobs["ip_soft_conf%d" % j].data)
for j in range(net_config["max_len"])
]
num = np.array(net.blobs["ip_number"].data)
return (bbox, conf, num)
else:
return None
def evaluate_forward(net, net_config):
net.clear_forward()
length = 20
net.f(NumpyData("prev_hidden", np.zeros((1, net_config["mem_cells"]))))
net.f(NumpyData("prev_mem", np.zeros((1, net_config["mem_cells"]))))
filler = Filler("uniform", net_config["init_range"])
predictions = []
value = 0.5
for _ in range(length):
# We'll be updating values in place for efficient memory usage. This
# will break backprop and cause warnings. Use clear_forward to suppress.
net.clear_forward()
# Add 0.5 to the sum at each step
net.f(NumpyData("value", data=np.array(value).reshape((1, 1))))
prev_hidden = "prev_hidden"
prev_mem = "prev_mem"
net.f(Concat("lstm_concat", bottoms=[prev_hidden, "value"]))
net.f(
LstmUnit("lstm",
net_config["mem_cells"],
bottoms=["lstm_concat", prev_mem],
param_names=[
"input_value", "input_gate", "forget_gate",
"output_gate"
],
weight_filler=filler,
tops=["next_hidden", "next_mem"]))
net.f(InnerProduct("ip", 1, bottoms=["next_hidden"]))
predictions.append(float(net.blobs["ip"].data.flatten()[0]))
# set up for next prediction by copying LSTM outputs back to inputs
net.blobs["prev_hidden"].data_tensor.copy_from(
net.blobs["next_hidden"].data_tensor)
net.blobs["prev_mem"].data_tensor.copy_from(
net.blobs["next_mem"].data_tensor)
targets = np.cumsum([value for _ in predictions])
residuals = [x - y for x, y in zip(predictions, targets)]
return targets, predictions, residuals
def evaluate_forward(net, net_config, feat,scene_feat):
net.clear_forward()
feat_dim=feat.shape[1]
net.f(NumpyData("prev_hidden", np.zeros((1, net_config["mem_cells"]))))
net.f(NumpyData("prev_mem", np.zeros((1, net_config["mem_cells"]))))
filler = Filler("uniform", net_config["init_range"])
length = feat.shape[0]+1
for step in range(length):
net.clear_forward()
if step==0:
value=scene_feat.reshape(1,feat_dim)
else:
value = feat[step-1,:].reshape(1,feat_dim)
net.f(NumpyData("value", data=value ))
prev_hidden = "prev_hidden"
prev_mem = "prev_mem"
net.f(Concat("lstm_concat", bottoms=[prev_hidden, "value"]))
net.f(LstmUnit("lstm", net_config["mem_cells"],
bottoms=["lstm_concat", prev_mem],
param_names=[
"input_value", "input_gate", "forget_gate", "output_gate"],
weight_filler=filler,
tops=["next_hidden", "next_mem"]))
net.f(InnerProduct("ip", 1, bottoms=["next_hidden"]))
net.blobs["prev_hidden"].data_tensor.copy_from(
net.blobs["next_hidden"].data_tensor)
net.blobs["prev_mem"].data_tensor.copy_from(
net.blobs["next_mem"].data_tensor)
for i in xrange(6):
net.f(InnerProduct("ip%d"%i, 2, bottoms=["next_hidden"]))
net.f(Softmax("prob%d"%i, bottoms=["ip%d"%i]))
predictions=[]
for i in xrange(6):
predictions.append(float(net.blobs["prob%d"%i].data.flatten()[1]))
return predictions
def add_MMD_loss_layer(target_net, src_net, MMD_config):
# # # loss of ip split
for layers, loss_weight in zip(MMD_config['layers'],
MMD_config['loss_weights']):
if loss_weight == 0:
continue
for bottom0 in layers:
bottom1 = 'src_' + bottom0
target_net.f(NumpyData(bottom1, data=src_net.blobs[bottom0].data))
top = bottom0 + '_loss'
target_net.f(
MMDLoss(name=top,
bottoms=[bottom0, bottom1],
loss_weight=loss_weight))
def forward(net, net_config):
net.clear_forward()
length = random.randrange(net_config["min_len"], net_config["max_len"])
# initialize all weights in [-0.1, 0.1]
filler = Filler("uniform", net_config["init_range"])
# initialize the LSTM memory with all 0's
net.f(
NumpyData(
"lstm_seed",
np.zeros((net_config["batch_size"], net_config["mem_cells"]))))
accum = np.zeros((net_config["batch_size"], ))
# Begin recurrence through 5 - 15 inputs
for step in range(length):
# Generate random inputs
value = np.array(
[random.random() for _ in range(net_config["batch_size"])])
# Set data of value blob to contain a batch of random numbers
net.f(NumpyData("value%d" % step, value.reshape((-1, 1))))
accum += value
for l in lstm_layers(net, step, filler, net_config):
net.f(l)
# Add a fully connected layer with a bottom blob set to be the last used
# LSTM cell. Note that the network structure is now a function of the data
net.f(
InnerProduct("ip",
1,
bottoms=["lstm_hidden%d" % (length - 1)],
weight_filler=filler))
# Add a label for the sum of the inputs
net.f(NumpyData("label", np.reshape(accum, (-1, 1))))
# Compute the Euclidean loss between the preiction and label,
# used for backprop
net.f(EuclideanLoss("euclidean", bottoms=["ip", "label"]))
def forward(net, input_data, net_config, deploy=False):
net.clear_forward()
if deploy:
image = np.array(input_data["image"])
else:
image = np.array(input_data["image"])
box_flags = np.array(input_data["box_flags"])
boxes = np.array(input_data["boxes"])
net.f(NumpyData("image", data=image))
generate_decapitated_googlenet(net)
generate_googlenet_to_lstm_layers(net)
if not deploy:
generate_ground_truth_layers(net, box_flags, boxes)
generate_lstm_seeds(net, net_config["lstm_num_cells"])
filler = Filler("uniform", net_config["init_range"])
score_concat_bottoms = []
bbox_concat_bottoms = []
for step in range(net_config["max_len"]):
hidden_bottom, mem_bottom = get_lstm_params(step)
generate_lstm(net, step, net_config["lstm_num_cells"], hidden_bottom,
mem_bottom, filler, net_config["dropout_ratio"])
generate_inner_products(net, step, filler)
score_concat_bottoms.append("ip_conf%d" % step)
bbox_concat_bottoms.append("ip_bbox%d" % step)
net.f(Concat("score_concat", bottoms=score_concat_bottoms, concat_dim=2))
net.f(Concat("bbox_concat", bottoms=bbox_concat_bottoms, concat_dim=2))
if not deploy:
generate_losses(net)
if deploy:
bbox = [
np.array(net.blobs["ip_bbox%d" % j].data)
for j in range(net_config["max_len"])
]
conf = [
np.array(net.blobs["ip_soft_conf%d" % j].data)
for j in range(net_config["max_len"])
]
return (bbox, conf)
else:
return None
def forward(net, input_data, net_config, deploy=False):
"""Defines and creates the ReInspect network given the net, input data
and configurations."""
net.clear_forward()
net.f(
NumpyData("wordvec_layer",
data=np.array(input_data["wordvec_layer"]))) # 128*38*100*1
net.f(NumpyData("target_words",
data=np.array(input_data["target_words"]))) # 128*100*1*1
tops = []
slice_point = []
for i in range(net_config['max_len']):
tops.append('label%d' % i)
if i != 0:
slice_point.append(i)
net.f(
Slice("label_slice_layer",
slice_dim=1,
bottoms=["target_words"],
tops=tops,
slice_point=slice_point))
tops = []
slice_point = []
for i in range(net_config['max_len']):
tops.append('target_wordvec%d_4d' % i)
if i != 0:
slice_point.append(i)
net.f(
Slice("wordvec_slice_layer",
slice_dim=2,
bottoms=['wordvec_layer'],
tops=tops,
slice_point=slice_point))
for i in range(net_config["max_len"]): # 128*38*1*1 -> 128*38
net.f("""
name: "target_wordvec%d"
type: "Reshape"
bottom: "target_wordvec%d_4d"
top: "target_wordvec%d"
reshape_param {
shape {
dim: 0 # copy the dimension from below
dim: -1
}
}
""" % (i, i, i))
#net.f(Reshape('target_wordvec%d'%i, bottoms = ['target_wordvec%d_4d'%i], shape = [0,-1]))
filler = Filler("uniform", net_config["init_range"])
for i in range(net_config['max_len']):
if i == 0:
net.f(
NumpyData(
"dummy_layer",
np.zeros((net_config["batch_size"],
net_config["lstm_num_cells"]))))
net.f(
NumpyData(
"dummy_mem_cell",
np.zeros((net_config["batch_size"],
net_config["lstm_num_cells"]))))
for j in range(net_config['lstm_num_stacks']):
bottoms = []
if j == 0:
bottoms.append('target_wordvec%d' % i)
if j >= 1:
bottoms.append('dropout%d_%d' % (j - 1, i))
if i == 0:
bottoms.append("dummy_layer")
else:
bottoms.append('lstm%d_hidden%d' % (j, i - 1))
net.f(Concat('concat%d_layer%d' % (j, i), bottoms=bottoms))
param_names = []
for k in range(4):
param_names.append('lstm%d_param_%d' % (j, k))
bottoms = ['concat%d_layer%d' % (j, i)]
if i == 0:
bottoms.append('dummy_mem_cell')
else:
bottoms.append('lstm%d_mem_cell%d' % (j, i - 1))
net.f(
LstmUnit('lstm%d_layer%d' % (j, i),
net_config["lstm_num_cells"],
weight_filler=filler,
param_names=param_names,
bottoms=bottoms,
tops=[
'lstm%d_hidden%d' % (j, i),
'lstm%d_mem_cell%d' % (j, i)
]))
net.f(
Dropout('dropout%d_%d' % (j, i),
net_config["dropout_ratio"],
bottoms=['lstm%d_hidden%d' % (j, i)]))
bottoms = []
for i in range(net_config['max_len']):
bottoms.append('dropout%d_%d' % (net_config['lstm_num_stacks'] - 1, i))
net.f(Concat('hidden_concat', bottoms=bottoms, concat_dim=0))
net.f(
InnerProduct("inner_product",
net_config['vocab_size'],
bottoms=["hidden_concat"],
weight_filler=filler))
bottoms = []
for i in range(net_config['max_len']):
bottoms.append('label%d' % i)
net.f(Concat('label_concat', bottoms=bottoms, concat_dim=0))
if deploy:
net.f(Softmax("word_probs", bottoms=["inner_product"]))
else:
net.f(
SoftmaxWithLoss("word_loss",
bottoms=["inner_product", "label_concat"],
ignore_label=net_config['zero_symbol']))
def generate_numbers_ground_truth_layers(net, numbers):
"""Generates the NumpyData layers that output the numbers."""
net.f(NumpyData("numbers", data=numbers))
import apollocaffe
from apollocaffe.layers import NumpyData, Wordvec, TheanoGPU, EuclideanLoss
import numpy as np
apollocaffe.set_device(0)
net = apollocaffe.ApolloNet()
for i in range(1000):
val1 = [[-2, 4, 1]]
net.clear_forward()
net.f(NumpyData('val1', val1))
net.f(NumpyData('wordval', [0]))
net.f(Wordvec('vec', 3, 1, bottoms=['wordval']))
net.f(NumpyData('cosine_target', [1]))
net.f(NumpyData('norm_target', [2]))
expr = 'T.dot(x[0], x[1].T) / (T.dot(x[0], x[0].T) * T.dot(x[1], x[1].T))**0.5'
net.f(TheanoGPU('cosine', [expr, (1, 1)], bottoms=['val1', 'vec']))
expr2 = 'T.dot(x[0], x[0].T)'
net.f(TheanoGPU('norm', [expr2, (1, 1)], bottoms=['vec']))
net.f(EuclideanLoss('loss1', bottoms=['cosine', 'cosine_target']))
net.f(EuclideanLoss('loss2', bottoms=['norm', 'norm_target']))
net.backward()
net.update(lr=0.01)
if i % 100 == 0:
print net.loss
print net.blobs['vec'].data
print net.blobs['norm'].data
import apollocaffe
from apollocaffe.layers import NumpyData, Convolution, EuclideanLoss
import numpy as np
net = apollocaffe.ApolloNet()
for i in range(1000):
example = np.array(np.random.random()).reshape((1, 1, 1, 1))
net.clear_forward()
net.f(NumpyData('data', example))
net.f(NumpyData('label', example * 3))
net.f(Convolution('conv', (1, 1), 1, bottoms=['data']))
net.f(EuclideanLoss('loss', bottoms=['conv', 'label']))
net.backward()
net.update(lr=0.1)
if i % 100 == 0:
print net.loss
def forward(net, input_data, net_config, phase='train', deploy=False):
"""Defines and creates the ReInspect network given the net, input data
and configurations."""
net.clear_forward()
batch_ws_i = input_data["ws_i"]
batch_stop_i = [net_config['max_len']] * net_config['batch_size']
wordvec_layer = input_data["wordvec_layer"] # 128*38*100*1
net.f(NumpyData("target_words",
data=np.array(input_data["target_words"]))) # 128*100*1*1
tops = []
slice_point = []
for i in range(net_config['max_len']):
tops.append('label%d' % i)
if i != 0:
slice_point.append(i)
net.f(
Slice("label_slice_layer",
slice_dim=1,
bottoms=["target_words"],
tops=tops,
slice_point=slice_point))
net.f(NumpyData("target_wordvec%d" % 0,
data=wordvec_layer[:, :, 0, 0])) # start symbol, 128*38
filler = Filler("uniform", net_config["init_range"])
for i in range(net_config['max_len']):
if i == 0:
net.f(
NumpyData(
"dummy_layer",
np.zeros((net_config["batch_size"],
net_config["lstm_num_cells"]))))
net.f(
NumpyData(
"dummy_mem_cell",
np.zeros((net_config["batch_size"],
net_config["lstm_num_cells"]))))
for j in range(net_config['lstm_num_stacks']):
bottoms = []
if j == 0:
bottoms.append('target_wordvec%d' % i)
if j >= 1:
bottoms.append('dropout%d_%d' % (j - 1, i))
if i == 0:
bottoms.append("dummy_layer")
else:
bottoms.append('lstm%d_hidden%d' % (j, i - 1))
net.f(Concat('concat%d_layer%d' % (j, i), bottoms=bottoms))
param_names = []
for k in range(4):
param_names.append('lstm%d_param_%d' % (j, k))
bottoms = ['concat%d_layer%d' % (j, i)]
if i == 0:
bottoms.append('dummy_mem_cell')
else:
bottoms.append('lstm%d_mem_cell%d' % (j, i - 1))
net.f(
LstmUnit('lstm%d_layer%d' % (j, i),
net_config["lstm_num_cells"],
weight_filler=filler,
param_names=param_names,
bottoms=bottoms,
tops=[
'lstm%d_hidden%d' % (j, i),
'lstm%d_mem_cell%d' % (j, i)
]))
net.f(
Dropout('dropout%d_%d' % (j, i),
net_config["dropout_ratio"],
bottoms=['lstm%d_hidden%d' % (j, i)]))
net.f(
InnerProduct("ip%d" % i,
net_config['vocab_size'],
bottoms=[
'dropout%d_%d' %
(net_config['lstm_num_stacks'] - 1, i)
],
weight_filler=filler))
if i < net_config['max_len'] - 1:
tar_wordvec = np.array(wordvec_layer[:, :, i + 1, 0]) # 128*38
if phase == 'test':
net.f(Softmax("word_probs%d" % i, bottoms=["ip%d" % i]))
probs = net.blobs["word_probs%d" % i].data
for bi in range(net_config['batch_size']):
if i >= batch_ws_i[bi] and i < batch_stop_i[bi]:
vec = [0] * net_config["vocab_size"]
peakIndex = np.argmax(probs[bi, :])
if peakIndex == net_config['whitespace_symbol']:
batch_stop_i[bi] = i + 1
vec[peakIndex] = 1
tar_wordvec[bi, :] = vec
net.f(NumpyData("target_wordvec%d" % (i + 1), data=tar_wordvec))
bottoms = []
for i in range(net_config['max_len']):
bottoms.append("ip%d" % i)
net.f(Concat('ip_concat', bottoms=bottoms, concat_dim=0))
bottoms = []
for i in range(net_config['max_len']):
bottoms.append('label%d' % i)
net.f(Concat('label_concat', bottoms=bottoms, concat_dim=0))
if deploy:
net.f(Softmax("word_probs", bottoms=["ip_concat"]))
net.f(
SoftmaxWithLoss("word_loss",
bottoms=["ip_concat", "label_concat"],
ignore_label=net_config['zero_symbol']))