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def _add_loss_summaries(total_loss):
'Add summaries for losses in CIFAR-10 model.\n\n Generates moving average for all losses and associated summaries for\n visualizing the performance of the network.\n\n Args:\n total_loss: Total loss from loss().\n Returns:\n loss_averages_op: op for generating moving averages of losses.\n '
loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
losses = tf.get_collection('losses')
loss_averages_op = loss_averages.apply((losses + [total_loss]))
for l in (losses + [total_loss]):
tf.summary.scalar((l.op.name + ' (raw)'), l)
tf.summary.scalar(l.op.name, loss_averages.average(l))
return loss_averages_op
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def train(total_loss, global_step):
'Train CIFAR-10 model.\n\n Create an optimizer and apply to all trainable variables. Add moving\n average for all trainable variables.\n\n Args:\n total_loss: Total loss from loss().\n global_step: Integer Variable counting the number of training steps\n processed.\n Returns:\n train_op: op for training.\n '
num_batches_per_epoch = (NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size)
decay_steps = int((num_batches_per_epoch * NUM_EPOCHS_PER_DECAY))
lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE, global_step, decay_steps, LEARNING_RATE_DECAY_FACTOR, staircase=True)
tf.summary.scalar('learning_rate', lr)
loss_averages_op = _add_loss_summaries(total_loss)
with tf.control_dependencies([loss_averages_op]):
opt = tf.train.GradientDescentOptimizer(lr)
grads = opt.compute_gradients(total_loss)
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)
for (grad, var) in grads:
if (grad is not None):
tf.summary.histogram((var.op.name + '/gradients'), grad)
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
with tf.control_dependencies([apply_gradient_op, variables_averages_op]):
train_op = tf.no_op(name='train')
return train_op
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def maybe_download_and_extract():
"Download and extract the tarball from Alex's website."
dest_directory = FLAGS.data_dir
if (not os.path.exists(dest_directory)):
os.makedirs(dest_directory)
filename = DATA_URL.split('/')[(- 1)]
filepath = os.path.join(dest_directory, filename)
if (not os.path.exists(filepath)):
def _progress(count, block_size, total_size):
sys.stdout.write(('\r>> Downloading %s %.1f%%' % (filename, ((float((count * block_size)) / float(total_size)) * 100.0))))
sys.stdout.flush()
(filepath, _) = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
print()
statinfo = os.stat(filepath)
print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
extracted_dir_path = os.path.join(dest_directory, 'cifar-10-batches-bin')
if (not os.path.exists(extracted_dir_path)):
tarfile.open(filepath, 'r:gz').extractall(dest_directory)
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def read_cifar10(filename_queue):
'Reads and parses examples from CIFAR10 data files.\n\n Recommendation: if you want N-way read parallelism, call this function\n N times. This will give you N independent Readers reading different\n files & positions within those files, which will give better mixing of\n examples.\n\n Args:\n filename_queue: A queue of strings with the filenames to read from.\n\n Returns:\n An object representing a single example, with the following fields:\n height: number of rows in the result (32)\n width: number of columns in the result (32)\n depth: number of color channels in the result (3)\n key: a scalar string Tensor describing the filename & record number\n for this example.\n label: an int32 Tensor with the label in the range 0..9.\n uint8image: a [height, width, depth] uint8 Tensor with the image data\n '
class CIFAR10Record(object):
pass
result = CIFAR10Record()
label_bytes = 1
result.height = 32
result.width = 32
result.depth = 3
image_bytes = ((result.height * result.width) * result.depth)
record_bytes = (label_bytes + image_bytes)
reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
(result.key, value) = reader.read(filename_queue)
record_bytes = tf.decode_raw(value, tf.uint8)
result.label = tf.cast(tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)
depth_major = tf.reshape(tf.strided_slice(record_bytes, [label_bytes], [(label_bytes + image_bytes)]), [result.depth, result.height, result.width])
result.uint8image = tf.transpose(depth_major, [1, 2, 0])
return result
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def _generate_image_and_label_batch(image, label, min_queue_examples, batch_size, shuffle):
'Construct a queued batch of images and labels.\n\n Args:\n image: 3-D Tensor of [height, width, 3] of type.float32.\n label: 1-D Tensor of type.int32\n min_queue_examples: int32, minimum number of samples to retain\n in the queue that provides of batches of examples.\n batch_size: Number of images per batch.\n shuffle: boolean indicating whether to use a shuffling queue.\n\n Returns:\n images: Images. 4D tensor of [batch_size, height, width, 3] size.\n labels: Labels. 1D tensor of [batch_size] size.\n '
num_preprocess_threads = 16
if shuffle:
(images, label_batch) = tf.train.shuffle_batch([image, label], batch_size=batch_size, num_threads=num_preprocess_threads, capacity=(min_queue_examples + (3 * batch_size)), min_after_dequeue=min_queue_examples)
else:
(images, label_batch) = tf.train.batch([image, label], batch_size=batch_size, num_threads=num_preprocess_threads, capacity=(min_queue_examples + (3 * batch_size)))
tf.summary.image('images', images)
return (images, tf.reshape(label_batch, [batch_size]))
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def distorted_inputs(data_dir, batch_size):
'Construct distorted input for CIFAR training using the Reader ops.\n\n Args:\n data_dir: Path to the CIFAR-10 data directory.\n batch_size: Number of images per batch.\n\n Returns:\n images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.\n labels: Labels. 1D tensor of [batch_size] size.\n '
filenames = [os.path.join(data_dir, ('data_batch_%d.bin' % i)) for i in xrange(1, 6)]
for f in filenames:
if (not tf.gfile.Exists(f)):
raise ValueError(('Failed to find file: ' + f))
filename_queue = tf.train.string_input_producer(filenames)
read_input = read_cifar10(filename_queue)
reshaped_image = tf.cast(read_input.uint8image, tf.float32)
height = IMAGE_SIZE
width = IMAGE_SIZE
distorted_image = tf.random_crop(reshaped_image, [height, width, 3])
distorted_image = tf.image.random_flip_left_right(distorted_image)
distorted_image = tf.image.random_brightness(distorted_image, max_delta=63)
distorted_image = tf.image.random_contrast(distorted_image, lower=0.2, upper=1.8)
float_image = tf.image.per_image_standardization(distorted_image)
float_image.set_shape([height, width, 3])
read_input.label.set_shape([1])
min_fraction_of_examples_in_queue = 0.4
min_queue_examples = int((NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN * min_fraction_of_examples_in_queue))
print(('Filling queue with %d CIFAR images before starting to train. This will take a few minutes.' % min_queue_examples))
return _generate_image_and_label_batch(float_image, read_input.label, min_queue_examples, batch_size, shuffle=True)
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def inputs(eval_data, data_dir, batch_size):
'Construct input for CIFAR evaluation using the Reader ops.\n\n Args:\n eval_data: bool, indicating if one should use the train or eval data set.\n data_dir: Path to the CIFAR-10 data directory.\n batch_size: Number of images per batch.\n\n Returns:\n images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.\n labels: Labels. 1D tensor of [batch_size] size.\n '
if (not eval_data):
filenames = [os.path.join(data_dir, ('data_batch_%d.bin' % i)) for i in xrange(1, 6)]
num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
else:
filenames = [os.path.join(data_dir, 'test_batch.bin')]
num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL
for f in filenames:
if (not tf.gfile.Exists(f)):
raise ValueError(('Failed to find file: ' + f))
filename_queue = tf.train.string_input_producer(filenames)
read_input = read_cifar10(filename_queue)
reshaped_image = tf.cast(read_input.uint8image, tf.float32)
height = IMAGE_SIZE
width = IMAGE_SIZE
resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image, height, width)
float_image = tf.image.per_image_standardization(resized_image)
float_image.set_shape([height, width, 3])
read_input.label.set_shape([1])
min_fraction_of_examples_in_queue = 0.4
min_queue_examples = int((num_examples_per_epoch * min_fraction_of_examples_in_queue))
return _generate_image_and_label_batch(float_image, read_input.label, min_queue_examples, batch_size, shuffle=False)
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def conv_variable(weight_shape):
w = weight_shape[0]
h = weight_shape[1]
input_channels = weight_shape[2]
output_channels = weight_shape[3]
d = (1.0 / np.sqrt(((input_channels * w) * h)))
bias_shape = [output_channels]
weight = tf.Variable(tf.random_uniform(weight_shape, minval=(- d), maxval=d))
bias = tf.Variable(tf.random_uniform(bias_shape, minval=(- d), maxval=d))
return (weight, bias)
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def conv2d(x, W, stride):
return tf.nn.conv2d(x, W, strides=[1, stride, stride, 1], padding='SAME')
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def maxpool2d(x, k=2):
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')
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def IPE_r1(F1F2, F2F3, F3F4, F4F5, F5F6, x_cor, y_cor, FLAGS):
img_pair = tf.concat([F1F2, F2F3, F3F4, F4F5, F5F6], 0)
(w_1_1, b_1_1) = conv_variable([10, 10, (FLAGS.col_dim * 2), 4])
h_1_1 = tf.nn.relu((conv2d(img_pair, w_1_1, 1) + b_1_1))
(w_1_2, b_1_2) = conv_variable([10, 10, 4, 4])
h_1_2 = tf.nn.relu((conv2d(h_1_1, w_1_2, 1) + b_1_2))
(w_2_1, b_2_1) = conv_variable([3, 3, (FLAGS.col_dim * 2), 16])
h_2_1 = tf.nn.relu((conv2d(img_pair, w_2_1, 1) + b_2_1))
(w_2_2, b_2_2) = conv_variable([3, 3, 16, 16])
h_2_2 = tf.nn.relu((conv2d(h_2_1, w_2_2, 1) + b_2_2))
en_pair = tf.concat([h_1_2, h_2_2], 3)
(w_3_1, b_3_1) = conv_variable([3, 3, 20, 16])
h_3_1 = tf.nn.relu((conv2d(en_pair, w_3_1, 1) + b_3_1))
(w_3_2, b_3_2) = conv_variable([3, 3, 16, 16])
h_3_2 = tf.nn.relu((conv2d(h_3_1, w_3_2, 1) + b_3_2))
h_3_2_x_y = tf.concat([h_3_2, x_cor, y_cor], 3)
fil_num = 16
(w_4_1, b_4_1) = conv_variable([3, 3, 18, fil_num])
h_4_1 = tf.nn.relu((conv2d(h_3_2_x_y, w_4_1, 1) + b_4_1))
h_4_1 = maxpool2d(h_4_1)
(w_4_2, b_4_2) = conv_variable([3, 3, fil_num, fil_num])
h_4_2 = tf.nn.relu((conv2d(h_4_1, w_4_2, 1) + b_4_2))
h_4_2 = maxpool2d(h_4_2)
(w_4_3, b_4_3) = conv_variable([3, 3, fil_num, fil_num])
h_4_3 = tf.nn.relu((conv2d(h_4_2, w_4_3, 1) + b_4_3))
h_4_3 = maxpool2d(h_4_3)
(w_4_4, b_4_4) = conv_variable([3, 3, fil_num, (fil_num * 2)])
h_4_4 = tf.nn.relu((conv2d(h_4_3, w_4_4, 1) + b_4_4))
fil_num = 32
h_4_4 = maxpool2d(h_4_4)
(w_4_5, b_4_5) = conv_variable([3, 3, fil_num, fil_num])
h_4_5 = tf.nn.relu((conv2d(h_4_4, w_4_5, 1) + b_4_5))
h_4_5 = maxpool2d(h_4_5)
res_pair = tf.reshape(h_4_5, [(- 1), fil_num])
pair1 = tf.slice(res_pair, [0, 0], [FLAGS.batch_num, (- 1)])
pair2 = tf.slice(res_pair, [FLAGS.batch_num, 0], [FLAGS.batch_num, (- 1)])
pair3 = tf.slice(res_pair, [(FLAGS.batch_num * 2), 0], [FLAGS.batch_num, (- 1)])
pair4 = tf.slice(res_pair, [(FLAGS.batch_num * 3), 0], [FLAGS.batch_num, (- 1)])
pair5 = tf.slice(res_pair, [(FLAGS.batch_num * 4), 0], [FLAGS.batch_num, (- 1)])
return (pair1, pair2, pair3, pair4, pair5)
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def IPE_r2(F1F2, F2F3, F3F4, F4F5, F5F6, x_cor, y_cor, FLAGS):
fil_num = FLAGS.fil_num
img_pair = tf.concat([F1F2, F2F3, F3F4, F4F5, F5F6], 0)
h_3_2_x_y = tf.concat([img_pair, x_cor, y_cor], 3)
(w_4_1, b_4_1) = conv_variable([3, 3, 10, fil_num])
h_4_1 = tf.nn.relu((conv2d(h_3_2_x_y, w_4_1, 1) + b_4_1))
h_4_1 = maxpool2d(h_4_1)
(w_4_2, b_4_2) = conv_variable([3, 3, fil_num, fil_num])
h_4_2 = tf.nn.relu(((conv2d(h_4_1, w_4_2, 1) + b_4_2) + h_4_1))
h_4_2 = maxpool2d(h_4_2)
(w_4_3, b_4_3) = conv_variable([3, 3, fil_num, fil_num])
h_4_3 = tf.nn.relu(((conv2d(h_4_2, w_4_3, 1) + b_4_3) + h_4_2))
h_4_3 = maxpool2d(h_4_3)
(w_4_4, b_4_4) = conv_variable([3, 3, fil_num, fil_num])
h_4_4 = tf.nn.relu(((conv2d(h_4_3, w_4_4, 1) + b_4_4) + h_4_3))
h_4_4 = maxpool2d(h_4_4)
(w_4_5, b_4_5) = conv_variable([3, 3, fil_num, fil_num])
h_4_5 = tf.nn.relu(((conv2d(h_4_4, w_4_5, 1) + b_4_5) + h_4_4))
h_4_5 = maxpool2d(h_4_5)
res_pair = tf.reshape(h_4_5, [(- 1), fil_num])
pair1 = tf.slice(res_pair, [0, 0], [FLAGS.batch_num, (- 1)])
pair2 = tf.slice(res_pair, [FLAGS.batch_num, 0], [FLAGS.batch_num, (- 1)])
pair3 = tf.slice(res_pair, [(FLAGS.batch_num * 2), 0], [FLAGS.batch_num, (- 1)])
pair4 = tf.slice(res_pair, [(FLAGS.batch_num * 3), 0], [FLAGS.batch_num, (- 1)])
pair5 = tf.slice(res_pair, [(FLAGS.batch_num * 4), 0], [FLAGS.batch_num, (- 1)])
return (pair1, pair2, pair3, pair4, pair5)
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def VE(F1, F2, F3, F4, F5, F6, x_cor, y_cor, FLAGS):
F1F2 = tf.concat([F1, F2], 3)
F2F3 = tf.concat([F2, F3], 3)
F3F4 = tf.concat([F3, F4], 3)
F4F5 = tf.concat([F4, F5], 3)
F5F6 = tf.concat([F5, F6], 3)
(pair1, pair2, pair3, pair4, pair5) = IPE_r2(F1F2, F2F3, F3F4, F4F5, F5F6, x_cor, y_cor, FLAGS)
concated_pair = tf.concat([pair1, pair2, pair3, pair4, pair5], 0)
fil_num = FLAGS.fil_num
w0 = tf.Variable(tf.truncated_normal([fil_num, (FLAGS.No * FLAGS.Ds)], stddev=0.1), dtype=tf.float32)
b0 = tf.Variable(tf.zeros([(FLAGS.No * FLAGS.Ds)]), dtype=tf.float32)
h0 = (tf.matmul(concated_pair, w0) + b0)
enpair1 = tf.reshape(tf.slice(h0, [0, 0], [FLAGS.batch_num, (- 1)]), [(- 1), FLAGS.No, FLAGS.Ds])
enpair2 = tf.reshape(tf.slice(h0, [FLAGS.batch_num, 0], [FLAGS.batch_num, (- 1)]), [(- 1), FLAGS.No, FLAGS.Ds])
enpair3 = tf.reshape(tf.slice(h0, [(FLAGS.batch_num * 2), 0], [FLAGS.batch_num, (- 1)]), [(- 1), FLAGS.No, FLAGS.Ds])
enpair4 = tf.reshape(tf.slice(h0, [(FLAGS.batch_num * 3), 0], [FLAGS.batch_num, (- 1)]), [(- 1), FLAGS.No, FLAGS.Ds])
enpair5 = tf.reshape(tf.slice(h0, [(FLAGS.batch_num * 4), 0], [FLAGS.batch_num, (- 1)]), [(- 1), FLAGS.No, FLAGS.Ds])
three1 = tf.concat([enpair1, enpair2], 2)
three2 = tf.concat([enpair2, enpair3], 2)
three3 = tf.concat([enpair3, enpair4], 2)
three4 = tf.concat([enpair4, enpair5], 2)
three = tf.concat([three1, three2, three3, three4], 0)
three = tf.reshape(three, [(- 1), (FLAGS.Ds * 2)])
w1 = tf.Variable(tf.truncated_normal([(FLAGS.Ds * 2), 64], stddev=0.1), dtype=tf.float32)
b1 = tf.Variable(tf.zeros([64]), dtype=tf.float32)
h1 = tf.nn.relu((tf.matmul(three, w1) + b1))
w2 = tf.Variable(tf.truncated_normal([64, 64], stddev=0.1), dtype=tf.float32)
b2 = tf.Variable(tf.zeros([64]), dtype=tf.float32)
h2 = tf.nn.relu(((tf.matmul(h1, w2) + b2) + h1))
w3 = tf.Variable(tf.truncated_normal([64, FLAGS.Ds], stddev=0.1), dtype=tf.float32)
b3 = tf.Variable(tf.zeros([FLAGS.Ds]), dtype=tf.float32)
h3 = ((tf.matmul(h2, w3) + b3) + h2)
h3 = tf.reshape(h3, [(- 1), FLAGS.No, FLAGS.Ds])
S1 = tf.slice(h3, [0, 0, 0], [FLAGS.batch_num, (- 1), (- 1)])
S2 = tf.slice(h3, [FLAGS.batch_num, 0, 0], [FLAGS.batch_num, (- 1), (- 1)])
S3 = tf.slice(h3, [(FLAGS.batch_num * 2), 0, 0], [FLAGS.batch_num, (- 1), (- 1)])
S4 = tf.slice(h3, [(FLAGS.batch_num * 3), 0, 0], [FLAGS.batch_num, (- 1), (- 1)])
return (S1, S2, S3, S4)
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def core_r1(S, FLAGS, idx):
fil_num = 64
M = tf.unstack(S, FLAGS.No, 1)
SD_in = tf.reshape(S, [(- 1), FLAGS.Ds])
with tf.variable_scope(('self-dynamics' + str(idx))):
w1 = tf.get_variable('w1', shape=[FLAGS.Ds, fil_num])
b1 = tf.get_variable('b1', shape=[fil_num])
h1 = tf.nn.relu((tf.matmul(SD_in, w1) + b1))
w2 = tf.get_variable('w2', shape=[fil_num, fil_num])
b2 = tf.get_variable('b2', shape=[fil_num])
h2 = ((tf.matmul(h1, w2) + b2) + h1)
M_self = tf.reshape(h2, [(- 1), FLAGS.No, fil_num])
rel_num = int((FLAGS.No * (FLAGS.No - 1)))
rel_in = np.zeros(rel_num, dtype=object)
for i in range(rel_num):
row_idx = int((i / (FLAGS.No - 1)))
col_idx = int((i % (FLAGS.No - 1)))
rel_in[i] = tf.concat([M[row_idx], M[col_idx]], 1)
rel_in = tf.concat(list(rel_in), 0)
with tf.variable_scope(('Relation' + str(idx))):
w1 = tf.get_variable('w1', shape=[(FLAGS.Ds * 2), fil_num])
b1 = tf.get_variable('b1', shape=[fil_num])
h1 = tf.nn.relu((tf.matmul(rel_in, w1) + b1))
w2 = tf.get_variable('w2', shape=[fil_num, fil_num])
b2 = tf.get_variable('b2', shape=[fil_num])
h2 = tf.nn.relu(((tf.matmul(h1, w2) + b2) + h1))
w3 = tf.get_variable('w3', shape=[fil_num, fil_num])
b3 = tf.get_variable('b3', shape=[fil_num])
h3 = ((tf.matmul(h2, w3) + b3) + h2)
M_rel = np.zeros(rel_num, dtype=object)
for i in range(rel_num):
M_rel[i] = tf.slice(h3, [(FLAGS.batch_num * i), 0], [FLAGS.batch_num, (- 1)])
M_rel2 = np.zeros(FLAGS.No, dtype=object)
for i in range(FLAGS.No):
for j in range((FLAGS.No - 1)):
M_rel2[i] += M_rel[((i * (FLAGS.No - 1)) + j)]
M_rel2 = tf.stack(list(M_rel2), 1)
M_update = (M_self + M_rel2)
aff_in = tf.reshape(M_update, [(- 1), fil_num])
with tf.variable_scope(('Affector' + str(idx))):
w1 = tf.get_variable('w1', shape=[fil_num, fil_num])
b1 = tf.get_variable('b1', shape=[fil_num])
h1 = tf.nn.relu(((tf.matmul(aff_in, w1) + b1) + aff_in))
w2 = tf.get_variable('w2', shape=[fil_num, fil_num])
b2 = tf.get_variable('b2', shape=[fil_num])
h2 = tf.nn.relu(((tf.matmul(h1, w2) + b2) + h1))
w3 = tf.get_variable('w3', shape=[fil_num, fil_num])
b3 = tf.get_variable('b3', shape=[fil_num])
h3 = ((tf.matmul(h2, w3) + b3) + h2)
M_affect = tf.reshape(h3, [(- 1), FLAGS.No, fil_num])
M_i_M_affect = tf.concat([S, M_affect], 2)
out_in = tf.reshape(M_i_M_affect, [(- 1), (FLAGS.Ds + fil_num)])
with tf.variable_scope(('Output' + str(idx))):
w1 = tf.get_variable('w1', shape=[(FLAGS.Ds + fil_num), fil_num])
b1 = tf.get_variable('b1', shape=[fil_num])
h1 = tf.nn.relu((tf.matmul(out_in, w1) + b1))
w2 = tf.get_variable('w2', shape=[fil_num, FLAGS.Ds])
b2 = tf.get_variable('b2', shape=[FLAGS.Ds])
h2 = (tf.matmul(h1, w2) + b2)
h2_out = tf.reshape(h2, [(- 1), FLAGS.No, FLAGS.Ds])
return h2_out
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def core_r2(S, FLAGS, idx):
fil_num = 64
M = tf.unstack(S, FLAGS.No, 1)
M_self = np.zeros(FLAGS.No, dtype=object)
for i in range(FLAGS.No):
with tf.variable_scope(((('self-dynamics' + str(idx)) + '_') + str((i + 1)))):
w1 = tf.get_variable('w1', shape=[FLAGS.Ds, fil_num])
b1 = tf.get_variable('b1', shape=[fil_num])
h1 = tf.nn.relu((tf.matmul(M[i], w1) + b1))
w2 = tf.get_variable('w2', shape=[fil_num, fil_num])
b2 = tf.get_variable('b2', shape=[fil_num])
h2 = (tf.matmul(h1, w2) + b2)
M_self[i] = h2
rel_in = []
for row_idx in range(FLAGS.No):
for col_idx in range(FLAGS.No):
if (row_idx != col_idx):
rel_in += [tf.concat([M[row_idx], M[col_idx]], 1)]
rel_out = []
for i in range(FLAGS.No):
rel_in_part = tf.concat(rel_in[(i * (FLAGS.No - 1)):((i + 1) * (FLAGS.No - 1))], 0)
with tf.variable_scope(((('Relation' + str(idx)) + '_') + str((i + 1)))):
w1 = tf.get_variable('w1', shape=[(FLAGS.Ds * 2), fil_num])
b1 = tf.get_variable('b1', shape=[fil_num])
h1 = tf.nn.relu((tf.matmul(rel_in_part, w1) + b1))
w2 = tf.get_variable('w2', shape=[fil_num, fil_num])
b2 = tf.get_variable('b2', shape=[fil_num])
h2 = tf.nn.relu((tf.matmul(h1, w2) + b2))
w3 = tf.get_variable('w3', shape=[fil_num, fil_num])
b3 = tf.get_variable('b3', shape=[fil_num])
h3 = (tf.matmul(h2, w3) + b3)
for j in range((FLAGS.No - 1)):
rel_out += [tf.slice(h3, [(FLAGS.batch_num * j), 0], [FLAGS.batch_num, (- 1)])]
M_rel2 = np.zeros(FLAGS.No, dtype=object)
for i in range(FLAGS.No):
for j in range((FLAGS.No - 1)):
M_rel2[i] += rel_out[((i * (FLAGS.No - 1)) + j)]
M_update = np.zeros(FLAGS.No, dtype=object)
for i in range(FLAGS.No):
M_update[i] = (M_self[i] + M_rel2[i])
M_affect = np.zeros(FLAGS.No, dtype=object)
for i in range(FLAGS.No):
with tf.variable_scope(((('Affector' + str(idx)) + '_') + str((i + 1)))):
w1 = tf.get_variable('w1', shape=[fil_num, fil_num])
b1 = tf.get_variable('b1', shape=[fil_num])
h1 = tf.nn.relu((tf.matmul(M_update[i], w1) + b1))
w2 = tf.get_variable('w2', shape=[fil_num, fil_num])
b2 = tf.get_variable('b2', shape=[fil_num])
h2 = tf.nn.relu((tf.matmul(h1, w2) + b2))
w3 = tf.get_variable('w3', shape=[fil_num, fil_num])
b3 = tf.get_variable('b3', shape=[fil_num])
h3 = (tf.matmul(h2, w3) + b3)
M_affect[i] = h3
M_affect = tf.stack(list(M_affect), 1)
M_i_M_affect = tf.concat([S, M_affect], 2)
out_in = tf.reshape(M_i_M_affect, [(- 1), (FLAGS.Ds + fil_num)])
with tf.variable_scope(('Output' + str(idx))):
w1 = tf.get_variable('w1', shape=[(FLAGS.Ds + fil_num), fil_num])
b1 = tf.get_variable('b1', shape=[fil_num])
h1 = tf.nn.relu((tf.matmul(out_in, w1) + b1))
w2 = tf.get_variable('w2', shape=[fil_num, FLAGS.Ds])
b2 = tf.get_variable('b2', shape=[FLAGS.Ds])
h2 = (tf.matmul(h1, w2) + b2)
h2_out = tf.reshape(h2, [(- 1), FLAGS.No, FLAGS.Ds])
return h2_out
|
def DP(S1, S2, S3, S4, FLAGS):
Sc1 = core_r1(S1, FLAGS, 4)
Sc3 = core_r1(S3, FLAGS, 2)
Sc4 = core_r1(S4, FLAGS, 1)
fil_num = 64
S = tf.concat([Sc1, Sc3, Sc4], 2)
S = tf.reshape(S, [(- 1), (FLAGS.Ds * 3)])
with tf.variable_scope('DP'):
w1 = tf.get_variable('w1', shape=[(FLAGS.Ds * 3), fil_num])
b1 = tf.get_variable('b1', shape=[fil_num])
h1 = tf.nn.relu((tf.matmul(S, w1) + b1))
w2 = tf.get_variable('w2', shape=[fil_num, FLAGS.Ds])
b2 = tf.get_variable('b2', shape=[FLAGS.Ds])
h2 = (tf.matmul(h1, w2) + b2)
h2 = tf.reshape(h2, [(- 1), FLAGS.No, FLAGS.Ds])
return h2
|
def SD(output_dp, FLAGS):
input_sd = tf.reshape(output_dp, [(- 1), FLAGS.Ds])
w1 = tf.Variable(tf.truncated_normal([FLAGS.Ds, 4], stddev=0.1), dtype=tf.float32)
b1 = tf.Variable(tf.zeros([4]), dtype=tf.float32)
h1 = (tf.matmul(input_sd, w1) + b1)
h1 = tf.reshape(h1, [(- 1), FLAGS.No, 4])
return h1
|
def add_path(path):
if (path not in sys.path):
sys.path.insert(0, path)
|
def update_config(config_file):
exp_config = None
with open(config_file) as f:
exp_config = edict(yaml.load(f, Loader=yaml.SafeLoader))
for (k, v) in exp_config.items():
if (k in config):
if isinstance(v, dict):
if (k == 'TRAIN'):
if ('BBOX_WEIGHTS' in v):
v['BBOX_WEIGHTS'] = np.array(v['BBOX_WEIGHTS'])
elif (k == 'network'):
if ('PIXEL_MEANS' in v):
v['PIXEL_MEANS'] = np.array(v['PIXEL_MEANS'])
for (vk, vv) in v.items():
config[k][vk] = vv
elif (k == 'SCALES'):
config[k][0] = tuple(v)
else:
config[k] = v
else:
raise ValueError('key must exist in config.py')
|
def _load_general(data, targets, major_axis):
'Load a list of arrays into a list of arrays specified by slices'
for (d_src, d_targets) in zip(data, targets):
if isinstance(d_targets, nd.NDArray):
d_src.copyto(d_targets)
elif isinstance(d_src, (list, tuple)):
for (src, dst) in zip(d_src, d_targets):
src.copyto(dst)
else:
raise NotImplementedError
|
def _load_data(batch, targets, major_axis):
'Load data into sliced arrays'
_load_general(batch.data, targets, major_axis)
|
def _load_label(batch, targets, major_axis):
'Load label into sliced arrays'
_load_general(batch.label, targets, major_axis)
|
def _merge_multi_context(outputs, major_axis):
'Merge outputs that lives on multiple context into one, so that they look\n like living on one context.\n '
rets = []
for (tensors, axis) in zip(outputs, major_axis):
if (axis >= 0):
rets.append(nd.concatenate(tensors, axis=axis, always_copy=False))
else:
rets.append(tensors[0])
return rets
|
class DataParallelExecutorGroup(object):
"DataParallelExecutorGroup is a group of executors that lives on a group of devices.\n This is a helper class used to implement data parallelization. Each mini-batch will\n be split and run on the devices.\n\n Parameters\n ----------\n symbol : Symbol\n The common symbolic computation graph for all executors.\n contexts : list\n A list of contexts.\n workload : list\n If not `None`, could be a list of numbers that specify the workload to be assigned\n to different context. Larger number indicate heavier workload.\n data_shapes : list\n Should be a list of (name, shape) tuples, for the shapes of data. Note the order is\n important and should be the same as the order that the `DataIter` provide the data.\n label_shapes : list\n Should be a list of (name, shape) tuples, for the shapes of label. Note the order is\n important and should be the same as the order that the `DataIter` provide the label.\n param_names : list\n A list of strings, indicating the names of parameters (e.g. weights, filters, etc.)\n in the computation graph.\n for_training : bool\n Indicate whether the executors should be bind for training. When not doing training,\n the memory for gradients will not be allocated.\n inputs_need_grad : bool\n Indicate whether the gradients for the input data should be computed. This is currently\n not used. It will be useful for implementing composition of modules.\n shared_group : DataParallelExecutorGroup\n Default is `None`. This is used in bucketing. When not `None`, it should be a executor\n group corresponding to a different bucket. In other words, it will correspond to a different\n symbol but with the same set of parameters (e.g. unrolled RNNs with different lengths).\n In this case, many memory will be shared.\n logger : Logger\n Default is `logging`.\n fixed_param_names: list of str\n Indicate parameters to be fixed during training. Parameters in this list will not allocate\n space for gradient, nor do gradient calculation.\n grad_req : str, list of str, dict of str to str\n Requirement for gradient accumulation. Can be 'write', 'add', or 'null'\n (default to 'write').\n Can be specified globally (str) or for each argument (list, dict).\n "
def __init__(self, symbol, contexts, workload, data_shapes, label_shapes, param_names, for_training, inputs_need_grad, shared_group=None, logger=logging, fixed_param_names=None, grad_req='write', state_names=None):
self.param_names = param_names
self.arg_names = symbol.list_arguments()
self.aux_names = symbol.list_auxiliary_states()
self.symbol = symbol
self.contexts = contexts
self.workload = workload
self.for_training = for_training
self.inputs_need_grad = inputs_need_grad
self.logger = logger
self.fixed_param_names = fixed_param_names
if (self.fixed_param_names is None):
self.fixed_param_names = []
self.state_names = state_names
if (self.state_names is None):
self.state_names = []
if (not for_training):
grad_req = 'null'
data_names = [x.name for x in data_shapes[0]]
if isinstance(grad_req, str):
self.grad_req = {}
for k in self.arg_names:
if (k in self.param_names):
self.grad_req[k] = ('null' if (k in self.fixed_param_names) else grad_req)
elif (k in data_names):
self.grad_req[k] = (grad_req if self.inputs_need_grad else 'null')
else:
self.grad_req[k] = 'null'
elif isinstance(grad_req, (list, tuple)):
assert (len(grad_req) == len(self.arg_names))
self.grad_req = dict(zip(self.arg_names, grad_req))
elif isinstance(grad_req, dict):
self.grad_req = {}
for k in self.arg_names:
if (k in self.param_names):
self.grad_req[k] = ('null' if (k in self.fixed_param_names) else 'write')
elif (k in data_names):
self.grad_req[k] = ('write' if self.inputs_need_grad else 'null')
else:
self.grad_req[k] = 'null'
self.grad_req.update(grad_req)
else:
raise ValueError('grad_req must be one of str, list, tuple, or dict.')
if (shared_group is not None):
self.shared_data_arrays = shared_group.shared_data_arrays
else:
self.shared_data_arrays = [{} for _ in contexts]
self.batch_size = len(data_shapes)
self.slices = None
self.execs = []
self._default_execs = None
self.data_arrays = None
self.label_arrays = None
self.param_arrays = None
self.state_arrays = None
self.grad_arrays = None
self.aux_arrays = None
self.input_grad_arrays = None
self.data_shapes = None
self.label_shapes = None
self.data_layouts = None
self.label_layouts = None
self.output_layouts = [DataDesc.get_batch_axis(self.symbol[name].attr('__layout__')) for name in self.symbol.list_outputs()]
self.bind_exec(data_shapes, label_shapes, shared_group)
def decide_slices(self, data_shapes):
'Decide the slices for each context according to the workload.\n\n Parameters\n ----------\n data_shapes : list\n list of (name, shape) specifying the shapes for the input data or label.\n '
assert (len(data_shapes) > 0)
major_axis = [DataDesc.get_batch_axis(x.layout) for x in data_shapes]
for ((name, shape), axis) in zip(data_shapes, major_axis):
if (axis == (- 1)):
continue
batch_size = shape[axis]
if (self.batch_size is not None):
assert (batch_size == self.batch_size), (('all data must have the same batch size: ' + ('batch_size = %d, but ' % self.batch_size)) + ('%s has shape %s' % (name, shape)))
else:
self.batch_size = batch_size
self.slices = _split_input_slice(self.batch_size, self.workload)
return major_axis
def _collect_arrays(self):
'Collect internal arrays from executors.'
self.data_arrays = [[e.arg_dict[name] for (name, _) in self.data_shapes[0]] for e in self.execs]
self.state_arrays = [[e.arg_dict[name] for e in self.execs] for name in self.state_names]
if (self.label_shapes is not None):
self.label_arrays = [[e.arg_dict[name] for (name, _) in self.label_shapes[0]] for e in self.execs]
else:
self.label_arrays = None
self.param_arrays = [[exec_.arg_arrays[i] for exec_ in self.execs] for (i, name) in enumerate(self.arg_names) if (name in self.param_names)]
if self.for_training:
self.grad_arrays = [[exec_.grad_arrays[i] for exec_ in self.execs] for (i, name) in enumerate(self.arg_names) if (name in self.param_names)]
else:
self.grad_arrays = None
data_names = [x[0] for x in self.data_shapes]
if self.inputs_need_grad:
self.input_grad_arrays = [[exec_.grad_arrays[i] for exec_ in self.execs] for (i, name) in enumerate(self.arg_names) if (name in data_names)]
else:
self.input_grad_arrays = None
self.aux_arrays = [[exec_.aux_arrays[i] for exec_ in self.execs] for i in range(len(self.aux_names))]
def bind_exec(self, data_shapes, label_shapes, shared_group=None, reshape=False):
'Bind executors on their respective devices.\n\n Parameters\n ----------\n data_shapes : list\n label_shapes : list\n shared_group : DataParallelExecutorGroup\n reshape : bool\n '
assert (reshape or (not self.execs))
for i in range(len(self.contexts)):
data_shapes_i = data_shapes[i]
if (label_shapes is not None):
label_shapes_i = label_shapes[i]
else:
label_shapes_i = []
if reshape:
self.execs[i] = self._default_execs[i].reshape(allow_up_sizing=True, **dict((data_shapes_i + label_shapes_i)))
else:
self.execs.append(self._bind_ith_exec(i, data_shapes_i, label_shapes_i, shared_group))
self.data_shapes = data_shapes
self.label_shapes = label_shapes
self._collect_arrays()
def reshape(self, data_shapes, label_shapes):
'Reshape executors.\n\n Parameters\n ----------\n data_shapes : list\n label_shapes : list\n '
if (self._default_execs is None):
self._default_execs = [i for i in self.execs]
for i in range(len(self.contexts)):
self.execs[i] = self._default_execs[i].reshape(allow_up_sizing=True, **dict((data_shapes[i] + (label_shapes[i] if (label_shapes is not None) else []))))
self.data_shapes = data_shapes
self.label_shapes = label_shapes
self._collect_arrays()
def set_params(self, arg_params, aux_params):
'Assign, i.e. copy parameters to all the executors.\n\n Parameters\n ----------\n arg_params : dict\n A dictionary of name to `NDArray` parameter mapping.\n aux_params : dict\n A dictionary of name to `NDArray` auxiliary variable mapping.\n '
for exec_ in self.execs:
exec_.copy_params_from(arg_params, aux_params)
def get_params(self, arg_params, aux_params):
' Copy data from each executor to `arg_params` and `aux_params`.\n\n Parameters\n ----------\n arg_params : list of NDArray\n target parameter arrays\n aux_params : list of NDArray\n target aux arrays\n\n Notes\n -----\n - This function will inplace update the NDArrays in arg_params and aux_params.\n '
for (name, block) in zip(self.param_names, self.param_arrays):
weight = (sum((w.copyto(ctx.cpu()) for w in block)) / len(block))
weight.astype(arg_params[name].dtype).copyto(arg_params[name])
for (name, block) in zip(self.aux_names, self.aux_arrays):
weight = (sum((w.copyto(ctx.cpu()) for w in block)) / len(block))
weight.astype(aux_params[name].dtype).copyto(aux_params[name])
def forward(self, data_batch, is_train=None):
'Split `data_batch` according to workload and run forward on each devices.\n\n Parameters\n ----------\n data_batch : DataBatch\n Or could be any object implementing similar interface.\n is_train : bool\n The hint for the backend, indicating whether we are during training phase.\n Default is `None`, then the value `self.for_training` will be used.\n Returns\n -------\n\n '
if LEONID_PROFILING_ENABLED:
import time
tic = time.time()
_load_data(data_batch, self.data_arrays, self.data_layouts)
if (is_train is None):
is_train = self.for_training
if LEONID_PROFILING_ENABLED:
toc = time.time()
print('DataParallelExecutorGroup::forward 1 {0}'.format((toc - tic)))
tic = time.time()
if (self.label_arrays is not None):
assert ((not is_train) or data_batch.label)
if data_batch.label:
_load_label(data_batch, self.label_arrays, self.label_layouts)
if LEONID_PROFILING_ENABLED:
toc = time.time()
print('DataParallelExecutorGroup::forward 2 {0}'.format((toc - tic)))
tic = time.time()
for exec_ in self.execs:
exec_.forward(is_train=is_train)
if LEONID_PROFILING_ENABLED:
toc = time.time()
print('DataParallelExecutorGroup::forward 3 {0}'.format((toc - tic)))
def get_outputs(self, merge_multi_context=True):
'Get outputs of the previous forward computation.\n\n Parameters\n ----------\n merge_multi_context : bool\n Default is `True`. In the case when data-parallelism is used, the outputs\n will be collected from multiple devices. A `True` value indicate that we\n should merge the collected results so that they look like from a single\n executor.\n\n Returns\n -------\n If `merge_multi_context` is `True`, it is like `[out1, out2]`. Otherwise, it\n is like `[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]`. All the output\n elements are `NDArray`.\n '
outputs = [[exec_.outputs[i] for exec_ in self.execs] for i in range(len(self.execs[0].outputs))]
if merge_multi_context:
outputs = _merge_multi_context(outputs, self.output_layouts)
return outputs
def get_states(self, merge_multi_context=True):
'Get states from all devices\n\n Parameters\n ----------\n merge_multi_context : bool\n Default is `True`. In the case when data-parallelism is used, the states\n will be collected from multiple devices. A `True` value indicate that we\n should merge the collected results so that they look like from a single\n executor.\n\n Returns\n -------\n If `merge_multi_context` is `True`, it is like `[out1, out2]`. Otherwise, it\n is like `[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]`. All the output\n elements are `NDArray`.\n '
assert (not merge_multi_context), 'merge_multi_context=True is not supported for get_states yet.'
return self.state_arrays
def set_states(self, states=None, value=None):
'Set value for states. Only one of states & value can be specified.\n\n Parameters\n ----------\n states : list of list of NDArrays\n source states arrays formatted like [[state1_dev1, state1_dev2],\n [state2_dev1, state2_dev2]].\n value : number\n a single scalar value for all state arrays.\n '
if (states is not None):
assert (value is None), 'Only one of states & value can be specified.'
_load_general(states, self.state_arrays, ((0,) * len(states)))
else:
assert (value is not None), 'At least one of states & value must be specified.'
assert (states is None), 'Only one of states & value can be specified.'
for d_dst in self.state_arrays:
for dst in d_dst:
dst[:] = value
def get_input_grads(self, merge_multi_context=True):
'Get the gradients with respect to the inputs of the module.\n\n Parameters\n ----------\n merge_multi_context : bool\n Default is `True`. In the case when data-parallelism is used, the outputs\n will be collected from multiple devices. A `True` value indicate that we\n should merge the collected results so that they look like from a single\n executor.\n\n Returns\n -------\n If `merge_multi_context` is `True`, it is like `[grad1, grad2]`. Otherwise, it\n is like `[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]`. All the output\n elements are `NDArray`.\n '
assert self.inputs_need_grad
if merge_multi_context:
return _merge_multi_context(self.input_grad_arrays, self.data_layouts)
return self.input_grad_arrays
def backward(self, out_grads=None):
'Run backward on all devices. A backward should be called after\n a call to the forward function. Backward cannot be called unless\n `self.for_training` is `True`.\n\n Parameters\n ----------\n out_grads : NDArray or list of NDArray, optional\n Gradient on the outputs to be propagated back.\n This parameter is only needed when bind is called\n on outputs that are not a loss function.\n '
assert self.for_training, 're-bind with for_training=True to run backward'
if (out_grads is None):
out_grads = []
for (i, exec_) in enumerate(self.execs):
out_grads_slice = []
exec_.backward(out_grads=out_grads_slice)
def update_metric(self, eval_metric, labels):
'Accumulate the performance according to `eval_metric` on all devices.\n\n Parameters\n ----------\n eval_metric : EvalMetric\n The metric used for evaluation.\n labels : list of NDArray\n Typically comes from `label` of a `DataBatch`.\n '
for (texec, labels) in zip(self.execs, labels):
eval_metric.update(labels, texec.outputs)
def _bind_ith_exec(self, i, data_shapes, label_shapes, shared_group):
'Internal utility function to bind the i-th executor.\n '
shared_exec = (None if (shared_group is None) else shared_group.execs[i])
context = self.contexts[i]
shared_data_arrays = self.shared_data_arrays[i]
input_shapes = dict(data_shapes)
if (label_shapes is not None):
input_shapes.update(dict(label_shapes))
(arg_shapes, _, aux_shapes) = self.symbol.infer_shape(**input_shapes)
assert (arg_shapes is not None), 'shape inference failed'
input_types = {x.name: x.dtype for x in data_shapes}
if (label_shapes is not None):
input_types.update({x.name: x.dtype for x in label_shapes})
(arg_types, _, aux_types) = self.symbol.infer_type(**input_types)
assert (arg_types is not None), 'type inference failed'
arg_arrays = []
grad_arrays = ({} if self.for_training else None)
def _get_or_reshape(name, shared_data_arrays, arg_shape, arg_type, context, logger):
'Internal helper to get a memory block or re-use by re-shaping'
if (name in shared_data_arrays):
arg_arr = shared_data_arrays[name]
if (np.prod(arg_arr.shape) >= np.prod(arg_shape)):
assert (arg_arr.dtype == arg_type)
arg_arr = arg_arr.reshape(arg_shape)
else:
logger.warning(((((((('bucketing: data "%s" has a shape %s' % (name, arg_shape)) + ', which is larger than already allocated ') + ('shape %s' % (arg_arr.shape,))) + '. Need to re-allocate. Consider putting ') + 'default_bucket_key to') + ' be the bucket taking the largest input for better ') + 'memory sharing.'))
arg_arr = nd.zeros(arg_shape, context, dtype=arg_type)
shared_data_arrays[name] = arg_arr
else:
arg_arr = nd.zeros(arg_shape, context, dtype=arg_type)
shared_data_arrays[name] = arg_arr
return arg_arr
for j in range(len(self.arg_names)):
name = self.arg_names[j]
if (name in self.param_names):
if (shared_exec is None):
arg_arr = nd.zeros(arg_shapes[j], context, dtype=arg_types[j])
if (self.grad_req[name] != 'null'):
grad_arr = nd.zeros(arg_shapes[j], context, dtype=arg_types[j])
grad_arrays[name] = grad_arr
else:
arg_arr = shared_exec.arg_dict[name]
assert (arg_arr.shape == arg_shapes[j])
assert (arg_arr.dtype == arg_types[j])
if (self.grad_req[name] != 'null'):
grad_arrays[name] = shared_exec.grad_dict[name]
else:
arg_arr = _get_or_reshape(name, shared_data_arrays, arg_shapes[j], arg_types[j], context, self.logger)
if (self.grad_req[name] != 'null'):
grad_arrays[name] = _get_or_reshape(('grad of ' + name), shared_data_arrays, arg_shapes[j], arg_types[j], context, self.logger)
arg_arrays.append(arg_arr)
if (shared_exec is None):
aux_arrays = [nd.zeros(s, context, dtype=t) for (s, t) in zip(aux_shapes, aux_types)]
else:
for (j, arr) in enumerate(shared_exec.aux_arrays):
assert (aux_shapes[j] == arr.shape)
assert (aux_types[j] == arr.dtype)
aux_arrays = shared_exec.aux_arrays[:]
executor = self.symbol.bind(ctx=context, args=arg_arrays, args_grad=grad_arrays, aux_states=aux_arrays, grad_req=self.grad_req, shared_exec=shared_exec)
return executor
def _sliced_shape(self, shapes, i, major_axis):
'Get the sliced shapes for the i-th executor.\n\n Parameters\n ----------\n shapes : list of (str, tuple)\n The original (name, shape) pairs.\n i : int\n Which executor we are dealing with.\n '
sliced_shapes = []
for (desc, axis) in zip(shapes, major_axis):
shape = list(desc.shape)
if (axis >= 0):
shape[axis] = (self.slices[i].stop - self.slices[i].start)
sliced_shapes.append(DataDesc(desc.name, tuple(shape), desc.dtype, desc.layout))
return sliced_shapes
def install_monitor(self, mon):
'Install monitor on all executors'
for exe in self.execs:
mon.install(exe)
|
class Speedometer(object):
def __init__(self, batch_size, frequent=50):
self.batch_size = batch_size
self.frequent = frequent
self.init = False
self.tic = 0
self.last_count = 0
def __call__(self, param):
'Callback to Show speed.'
count = param.nbatch
if (self.last_count > count):
self.init = False
self.last_count = count
if self.init:
if ((count % self.frequent) == 0):
speed = ((self.frequent * self.batch_size) / (time.time() - self.tic))
s = ''
if (param.eval_metric is not None):
(name, value) = param.eval_metric.get()
s = ('Epoch[%d] Batch [%d]\tSpeed: %.2f samples/sec\tTrain-' % (param.epoch, count, speed))
for (n, v) in zip(name, value):
s += ('%s=%f,\t' % (n, v))
else:
s = ('Iter[%d] Batch [%d]\tSpeed: %.2f samples/sec' % (param.epoch, count, speed))
logging.info(s)
print(s)
self.tic = time.time()
else:
self.init = True
self.tic = time.time()
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def do_checkpoint(prefix, means, stds):
def _callback(iter_no, sym, arg, aux):
arg['bbox_pred_weight_test'] = (arg['bbox_pred_weight'].T * mx.nd.array(stds)).T
arg['bbox_pred_bias_test'] = ((arg['bbox_pred_bias'] * mx.nd.array(stds)) + mx.nd.array(means))
mx.model.save_checkpoint(prefix, (iter_no + 1), sym, arg, aux)
arg.pop('bbox_pred_weight_test')
arg.pop('bbox_pred_bias_test')
return _callback
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def test_rcnn(cfg, dataset, image_set, root_path, dataset_path, ctx, prefix, epoch, vis, ignore_cache, shuffle, has_rpn, proposal, thresh, logger=None, output_path=None, nms_dets=None, is_docker=False):
if (not logger):
assert False, 'require a logger'
datasets = dataset.split(';')
dataset_paths = dataset_path.split(';')
imagesets = image_set.split(';')
output_paths = output_path.split(';')
categ_index_offs = 20
for (dataset, dataset_path, image_set, output_path) in zip(datasets, dataset_paths, imagesets, output_paths):
if (len(image_set.strip()) <= 0):
continue
if ('classes_list_fname' not in cfg.dataset):
classes_list_fname = ''
else:
classes_list_fname = cfg.dataset.classes_list_fname
if has_rpn:
sym_instance = eval(((cfg.symbol + '.') + cfg.symbol))()
sym = sym_instance.get_symbol(cfg, is_train=False)
imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path, classes_list_fname=classes_list_fname, categ_index_offs=categ_index_offs)
roidb = imdb.gt_roidb()
else:
sym_instance = eval(((cfg.symbol + '.') + cfg.symbol))()
sym = sym_instance.get_symbol_rcnn(cfg, is_train=False)
imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path)
gt_roidb = imdb.gt_roidb()
roidb = eval((('imdb.' + proposal) + '_roidb'))(gt_roidb)
categ_index_offs += imdb.num_classes
test_data = TestLoader(roidb, cfg, batch_size=len(ctx), shuffle=shuffle, has_rpn=has_rpn)
if (not is_docker):
(arg_params, aux_params) = load_param(prefix, epoch, process=True)
data_shape_dict = dict(test_data.provide_data_single)
sym_instance.infer_shape(data_shape_dict)
sym_instance.check_parameter_shapes(arg_params, aux_params, data_shape_dict, is_train=False)
data_names = [k[0] for k in test_data.provide_data_single]
label_names = None
max_data_shape = [[('data', (1, 3, max([v[0] for v in cfg.SCALES]), max([v[1] for v in cfg.SCALES])))]]
if (not has_rpn):
max_data_shape.append(('rois', ((cfg.TEST.PROPOSAL_POST_NMS_TOP_N + 30), 5)))
predictor = Predictor(sym, data_names, label_names, context=ctx, max_data_shapes=max_data_shape, provide_data=test_data.provide_data, provide_label=test_data.provide_label, arg_params=arg_params, aux_params=aux_params)
imdb.num_classes = cfg.dataset.NUM_CLASSES
else:
predictor = None
pred_eval(predictor, test_data, imdb, cfg, vis=vis, ignore_cache=ignore_cache, thresh=thresh, logger=logger, nms_dets=nms_dets)
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def train_rcnn(cfg, dataset, image_set, root_path, dataset_path, frequent, kvstore, flip, shuffle, resume, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, train_shared, lr, lr_step, proposal, logger=None, output_path=None):
mx.random.seed(np.random.randint(10000))
np.random.seed(np.random.randint(10000))
if (not logger):
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
sym_instance = eval(((cfg.symbol + '.') + cfg.symbol))()
sym = sym_instance.get_symbol_rcnn(cfg, is_train=True)
batch_size = len(ctx)
input_batch_size = (cfg.TRAIN.BATCH_IMAGES * batch_size)
pprint.pprint(cfg)
logger.info('training rcnn cfg:{}\n'.format(pprint.pformat(cfg)))
image_sets = [iset for iset in image_set.split('+')]
roidbs = [load_proposal_roidb(dataset, image_set, root_path, dataset_path, proposal=proposal, append_gt=True, flip=flip, result_path=output_path) for image_set in image_sets]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, cfg)
(means, stds) = add_bbox_regression_targets(roidb, cfg)
train_data = ROIIter(roidb, cfg, batch_size=input_batch_size, shuffle=shuffle, ctx=ctx, aspect_grouping=cfg.TRAIN.ASPECT_GROUPING)
max_data_shape = [('data', (cfg.TRAIN.BATCH_IMAGES, 3, max([v[0] for v in cfg.SCALES]), max([v[1] for v in cfg.SCALES])))]
data_shape_dict = dict((train_data.provide_data_single + train_data.provide_label_single))
sym_instance.infer_shape(data_shape_dict)
if resume:
print('continue training from ', begin_epoch)
(arg_params, aux_params) = load_param(prefix, begin_epoch, convert=True)
else:
(arg_params, aux_params) = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight_rcnn(cfg, arg_params, aux_params)
sym_instance.check_parameter_shapes(arg_params, aux_params, data_shape_dict)
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
if train_shared:
fixed_param_prefix = cfg.network.FIXED_PARAMS_SHARED
else:
fixed_param_prefix = cfg.network.FIXED_PARAMS
mod = MutableModule(sym, data_names=data_names, label_names=label_names, logger=logger, context=ctx, max_data_shapes=[max_data_shape for _ in range(batch_size)], fixed_param_prefix=fixed_param_prefix)
if cfg.TRAIN.RESUME:
mod._preload_opt_states = ('%s-%04d.states' % (prefix, begin_epoch))
eval_metric = metric.RCNNAccMetric(cfg)
cls_metric = metric.RCNNLogLossMetric(cfg)
bbox_metric = metric.RCNNL1LossMetric(cfg)
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [eval_metric, cls_metric, bbox_metric]:
eval_metrics.add(child_metric)
batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=frequent)
epoch_end_callback = [mx.callback.module_checkpoint(mod, prefix, period=1, save_optimizer_states=True), callback.do_checkpoint(prefix, means, stds)]
base_lr = lr
lr_factor = cfg.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [(epoch - begin_epoch) for epoch in lr_epoch if (epoch > begin_epoch)]
lr = (base_lr * (lr_factor ** (len(lr_epoch) - len(lr_epoch_diff))))
lr_iters = [int(((epoch * len(roidb)) / batch_size)) for epoch in lr_epoch_diff]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor, cfg.TRAIN.warmup, cfg.TRAIN.warmup_lr, cfg.TRAIN.warmup_step)
optimizer_params = {'momentum': cfg.TRAIN.momentum, 'wd': cfg.TRAIN.wd, 'learning_rate': lr, 'lr_scheduler': lr_scheduler, 'rescale_grad': 1.0, 'clip_gradient': None}
if (not isinstance(train_data, PrefetchingIter)):
train_data = PrefetchingIter(train_data)
mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback, batch_end_callback=batch_end_callback, kvstore=kvstore, optimizer='sgd', optimizer_params=optimizer_params, arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)
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class BoxAnnotatorOHEMOperator(mx.operator.CustomOp):
def __init__(self, num_classes, num_reg_classes, rm_last, roi_per_img):
super(BoxAnnotatorOHEMOperator, self).__init__()
self._num_classes = num_classes
self._num_reg_classes = num_reg_classes
self._roi_per_img = roi_per_img
self._rm_last = rm_last
def forward(self, is_train, req, in_data, out_data, aux):
cls_score = in_data[0]
bbox_pred = in_data[1]
labels = in_data[2].asnumpy()
bbox_targets = in_data[3]
bbox_weights = in_data[4]
if config.network.SOFTMAX_ENABLED:
per_roi_loss_cls = (mx.nd.SoftmaxActivation(cls_score) + 1e-14)
else:
per_roi_loss_cls = (cls_score + 1e-14)
per_roi_loss_cls = per_roi_loss_cls.asnumpy()
per_roi_loss_cls = per_roi_loss_cls[(np.arange(per_roi_loss_cls.shape[0], dtype='int'), labels.astype('int'))]
per_roi_loss_cls = ((- 1) * np.log(per_roi_loss_cls))
per_roi_loss_cls = np.reshape(per_roi_loss_cls, newshape=((- 1),))
per_roi_loss_bbox = (bbox_weights * mx.nd.smooth_l1((bbox_pred - bbox_targets), scalar=1.0))
per_roi_loss_bbox = mx.nd.sum(per_roi_loss_bbox, axis=1).asnumpy()
total_loss = (per_roi_loss_cls + per_roi_loss_bbox)
top_k_per_roi_loss = np.argsort(total_loss)
labels_ohem = labels
if (self._rm_last == 1):
labels_ohem[np.where((labels_ohem == (self._num_classes - 1)))] = (- 1)
labels_ohem[top_k_per_roi_loss[::(- 1)][self._roi_per_img:]] = (- 1)
bbox_weights_ohem = bbox_weights.asnumpy()
bbox_weights_ohem[top_k_per_roi_loss[::(- 1)][self._roi_per_img:]] = 0
labels_ohem = mx.nd.array(labels_ohem)
bbox_weights_ohem = mx.nd.array(bbox_weights_ohem)
for (ind, val) in enumerate([labels_ohem, bbox_weights_ohem]):
self.assign(out_data[ind], req[ind], val)
def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
for i in range(len(in_grad)):
self.assign(in_grad[i], req[i], 0)
|
@mx.operator.register('BoxAnnotatorOHEM')
class BoxAnnotatorOHEMProp(mx.operator.CustomOpProp):
def __init__(self, num_classes, num_reg_classes, rm_last, roi_per_img):
super(BoxAnnotatorOHEMProp, self).__init__(need_top_grad=False)
self._num_classes = int(num_classes)
self._num_reg_classes = int(num_reg_classes)
self._roi_per_img = int(roi_per_img)
self._rm_last = rm_last
def list_arguments(self):
return ['cls_score', 'bbox_pred', 'labels', 'bbox_targets', 'bbox_weights']
def list_outputs(self):
return ['labels_ohem', 'bbox_weights_ohem']
def infer_shape(self, in_shape):
labels_shape = in_shape[2]
bbox_weights_shape = in_shape[4]
return (in_shape, [labels_shape, bbox_weights_shape])
def create_operator(self, ctx, shapes, dtypes):
return BoxAnnotatorOHEMOperator(self._num_classes, self._num_reg_classes, self._rm_last, self._roi_per_img)
def declare_backward_dependency(self, out_grad, in_data, out_data):
return []
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class resnet_v1_101_fpn_dcn_rcnn(Symbol):
def __init__(self):
'\n Use __init__ to define parameter network needs\n '
self.shared_param_list = ['offset_p2', 'offset_p3', 'offset_p4', 'offset_p5', 'rpn_conv', 'rpn_cls_score', 'rpn_bbox_pred']
self.shared_param_dict = {}
for name in self.shared_param_list:
self.shared_param_dict[(name + '_weight')] = mx.sym.Variable((name + '_weight'))
self.shared_param_dict[(name + '_bias')] = mx.sym.Variable((name + '_bias'))
def get_resnet_backbone(self, data, with_dilated=False, with_dconv=False, with_dpyramid=False, eps=1e-05):
conv1 = mx.symbol.Convolution(name='conv1', data=data, num_filter=64, pad=(3, 3), kernel=(7, 7), stride=(2, 2), no_bias=True)
bn_conv1 = mx.symbol.BatchNorm(name='bn_conv1', data=conv1, use_global_stats=True, fix_gamma=False, eps=eps)
scale_conv1 = bn_conv1
conv1_relu = mx.symbol.Activation(name='conv1_relu', data=scale_conv1, act_type='relu')
pool1 = mx.symbol.Pooling(name='pool1', data=conv1_relu, pooling_convention='full', pad=(0, 0), kernel=(3, 3), stride=(2, 2), pool_type='max')
res2a_branch1 = mx.symbol.Convolution(name='res2a_branch1', data=pool1, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2a_branch1 = mx.symbol.BatchNorm(name='bn2a_branch1', data=res2a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch1 = bn2a_branch1
res2a_branch2a = mx.symbol.Convolution(name='res2a_branch2a', data=pool1, num_filter=64, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2a_branch2a = mx.symbol.BatchNorm(name='bn2a_branch2a', data=res2a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch2a = bn2a_branch2a
res2a_branch2a_relu = mx.symbol.Activation(name='res2a_branch2a_relu', data=scale2a_branch2a, act_type='relu')
res2a_branch2b = mx.symbol.Convolution(name='res2a_branch2b', data=res2a_branch2a_relu, num_filter=64, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn2a_branch2b = mx.symbol.BatchNorm(name='bn2a_branch2b', data=res2a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch2b = bn2a_branch2b
res2a_branch2b_relu = mx.symbol.Activation(name='res2a_branch2b_relu', data=scale2a_branch2b, act_type='relu')
res2a_branch2c = mx.symbol.Convolution(name='res2a_branch2c', data=res2a_branch2b_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2a_branch2c = mx.symbol.BatchNorm(name='bn2a_branch2c', data=res2a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch2c = bn2a_branch2c
res2a = mx.symbol.broadcast_add(*[scale2a_branch1, scale2a_branch2c], name='res2a')
res2a_relu = mx.symbol.Activation(name='res2a_relu', data=res2a, act_type='relu')
res2b_branch2a = mx.symbol.Convolution(name='res2b_branch2a', data=res2a_relu, num_filter=64, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2b_branch2a = mx.symbol.BatchNorm(name='bn2b_branch2a', data=res2b_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale2b_branch2a = bn2b_branch2a
res2b_branch2a_relu = mx.symbol.Activation(name='res2b_branch2a_relu', data=scale2b_branch2a, act_type='relu')
res2b_branch2b = mx.symbol.Convolution(name='res2b_branch2b', data=res2b_branch2a_relu, num_filter=64, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn2b_branch2b = mx.symbol.BatchNorm(name='bn2b_branch2b', data=res2b_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale2b_branch2b = bn2b_branch2b
res2b_branch2b_relu = mx.symbol.Activation(name='res2b_branch2b_relu', data=scale2b_branch2b, act_type='relu')
res2b_branch2c = mx.symbol.Convolution(name='res2b_branch2c', data=res2b_branch2b_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2b_branch2c = mx.symbol.BatchNorm(name='bn2b_branch2c', data=res2b_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale2b_branch2c = bn2b_branch2c
res2b = mx.symbol.broadcast_add(*[res2a_relu, scale2b_branch2c], name='res2b')
res2b_relu = mx.symbol.Activation(name='res2b_relu', data=res2b, act_type='relu')
res2c_branch2a = mx.symbol.Convolution(name='res2c_branch2a', data=res2b_relu, num_filter=64, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2c_branch2a = mx.symbol.BatchNorm(name='bn2c_branch2a', data=res2c_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale2c_branch2a = bn2c_branch2a
res2c_branch2a_relu = mx.symbol.Activation(name='res2c_branch2a_relu', data=scale2c_branch2a, act_type='relu')
res2c_branch2b = mx.symbol.Convolution(name='res2c_branch2b', data=res2c_branch2a_relu, num_filter=64, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn2c_branch2b = mx.symbol.BatchNorm(name='bn2c_branch2b', data=res2c_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale2c_branch2b = bn2c_branch2b
res2c_branch2b_relu = mx.symbol.Activation(name='res2c_branch2b_relu', data=scale2c_branch2b, act_type='relu')
res2c_branch2c = mx.symbol.Convolution(name='res2c_branch2c', data=res2c_branch2b_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2c_branch2c = mx.symbol.BatchNorm(name='bn2c_branch2c', data=res2c_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale2c_branch2c = bn2c_branch2c
res2c = mx.symbol.broadcast_add(*[res2b_relu, scale2c_branch2c], name='res2c')
res2c_relu = mx.symbol.Activation(name='res2c_relu', data=res2c, act_type='relu')
res3a_branch1 = mx.symbol.Convolution(name='res3a_branch1', data=res2c_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn3a_branch1 = mx.symbol.BatchNorm(name='bn3a_branch1', data=res3a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch1 = bn3a_branch1
res3a_branch2a = mx.symbol.Convolution(name='res3a_branch2a', data=res2c_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn3a_branch2a = mx.symbol.BatchNorm(name='bn3a_branch2a', data=res3a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch2a = bn3a_branch2a
res3a_branch2a_relu = mx.symbol.Activation(name='res3a_branch2a_relu', data=scale3a_branch2a, act_type='relu')
res3a_branch2b = mx.symbol.Convolution(name='res3a_branch2b', data=res3a_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3a_branch2b = mx.symbol.BatchNorm(name='bn3a_branch2b', data=res3a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch2b = bn3a_branch2b
res3a_branch2b_relu = mx.symbol.Activation(name='res3a_branch2b_relu', data=scale3a_branch2b, act_type='relu')
res3a_branch2c = mx.symbol.Convolution(name='res3a_branch2c', data=res3a_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3a_branch2c = mx.symbol.BatchNorm(name='bn3a_branch2c', data=res3a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch2c = bn3a_branch2c
res3a = mx.symbol.broadcast_add(*[scale3a_branch1, scale3a_branch2c], name='res3a')
res3a_relu = mx.symbol.Activation(name='res3a_relu', data=res3a, act_type='relu')
res3b1_branch2a = mx.symbol.Convolution(name='res3b1_branch2a', data=res3a_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b1_branch2a = mx.symbol.BatchNorm(name='bn3b1_branch2a', data=res3b1_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b1_branch2a = bn3b1_branch2a
res3b1_branch2a_relu = mx.symbol.Activation(name='res3b1_branch2a_relu', data=scale3b1_branch2a, act_type='relu')
res3b1_branch2b = mx.symbol.Convolution(name='res3b1_branch2b', data=res3b1_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3b1_branch2b = mx.symbol.BatchNorm(name='bn3b1_branch2b', data=res3b1_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b1_branch2b = bn3b1_branch2b
res3b1_branch2b_relu = mx.symbol.Activation(name='res3b1_branch2b_relu', data=scale3b1_branch2b, act_type='relu')
res3b1_branch2c = mx.symbol.Convolution(name='res3b1_branch2c', data=res3b1_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b1_branch2c = mx.symbol.BatchNorm(name='bn3b1_branch2c', data=res3b1_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b1_branch2c = bn3b1_branch2c
res3b1 = mx.symbol.broadcast_add(*[res3a_relu, scale3b1_branch2c], name='res3b1')
res3b1_relu = mx.symbol.Activation(name='res3b1_relu', data=res3b1, act_type='relu')
res3b2_branch2a = mx.symbol.Convolution(name='res3b2_branch2a', data=res3b1_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b2_branch2a = mx.symbol.BatchNorm(name='bn3b2_branch2a', data=res3b2_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b2_branch2a = bn3b2_branch2a
res3b2_branch2a_relu = mx.symbol.Activation(name='res3b2_branch2a_relu', data=scale3b2_branch2a, act_type='relu')
res3b2_branch2b = mx.symbol.Convolution(name='res3b2_branch2b', data=res3b2_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3b2_branch2b = mx.symbol.BatchNorm(name='bn3b2_branch2b', data=res3b2_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b2_branch2b = bn3b2_branch2b
res3b2_branch2b_relu = mx.symbol.Activation(name='res3b2_branch2b_relu', data=scale3b2_branch2b, act_type='relu')
res3b2_branch2c = mx.symbol.Convolution(name='res3b2_branch2c', data=res3b2_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b2_branch2c = mx.symbol.BatchNorm(name='bn3b2_branch2c', data=res3b2_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b2_branch2c = bn3b2_branch2c
res3b2 = mx.symbol.broadcast_add(*[res3b1_relu, scale3b2_branch2c], name='res3b2')
res3b2_relu = mx.symbol.Activation(name='res3b2_relu', data=res3b2, act_type='relu')
res3b3_branch2a = mx.symbol.Convolution(name='res3b3_branch2a', data=res3b2_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b3_branch2a = mx.symbol.BatchNorm(name='bn3b3_branch2a', data=res3b3_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b3_branch2a = bn3b3_branch2a
res3b3_branch2a_relu = mx.symbol.Activation(name='res3b3_branch2a_relu', data=scale3b3_branch2a, act_type='relu')
if with_dpyramid:
res3b3_branch2b_offset = mx.symbol.Convolution(name='res3b3_branch2b_offset', data=res3b3_branch2a_relu, num_filter=72, pad=(1, 1), kernel=(3, 3), stride=(1, 1))
res3b3_branch2b = mx.contrib.symbol.DeformableConvolution(name='res3b3_branch2b', data=res3b3_branch2a_relu, offset=res3b3_branch2b_offset, num_filter=128, pad=(1, 1), kernel=(3, 3), num_deformable_group=4, stride=(1, 1), no_bias=True)
else:
res3b3_branch2b = mx.symbol.Convolution(name='res3b3_branch2b', data=res3b3_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3b3_branch2b = mx.symbol.BatchNorm(name='bn3b3_branch2b', data=res3b3_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b3_branch2b = bn3b3_branch2b
res3b3_branch2b_relu = mx.symbol.Activation(name='res3b3_branch2b_relu', data=scale3b3_branch2b, act_type='relu')
res3b3_branch2c = mx.symbol.Convolution(name='res3b3_branch2c', data=res3b3_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b3_branch2c = mx.symbol.BatchNorm(name='bn3b3_branch2c', data=res3b3_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b3_branch2c = bn3b3_branch2c
res3b3 = mx.symbol.broadcast_add(*[res3b2_relu, scale3b3_branch2c], name='res3b3')
res3b3_relu = mx.symbol.Activation(name='res3b3_relu', data=res3b3, act_type='relu')
res4a_branch1 = mx.symbol.Convolution(name='res4a_branch1', data=res3b3_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn4a_branch1 = mx.symbol.BatchNorm(name='bn4a_branch1', data=res4a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch1 = bn4a_branch1
res4a_branch2a = mx.symbol.Convolution(name='res4a_branch2a', data=res3b3_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn4a_branch2a = mx.symbol.BatchNorm(name='bn4a_branch2a', data=res4a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch2a = bn4a_branch2a
res4a_branch2a_relu = mx.symbol.Activation(name='res4a_branch2a_relu', data=scale4a_branch2a, act_type='relu')
res4a_branch2b = mx.symbol.Convolution(name='res4a_branch2b', data=res4a_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4a_branch2b = mx.symbol.BatchNorm(name='bn4a_branch2b', data=res4a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch2b = bn4a_branch2b
res4a_branch2b_relu = mx.symbol.Activation(name='res4a_branch2b_relu', data=scale4a_branch2b, act_type='relu')
res4a_branch2c = mx.symbol.Convolution(name='res4a_branch2c', data=res4a_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4a_branch2c = mx.symbol.BatchNorm(name='bn4a_branch2c', data=res4a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch2c = bn4a_branch2c
res4a = mx.symbol.broadcast_add(*[scale4a_branch1, scale4a_branch2c], name='res4a')
res4a_relu = mx.symbol.Activation(name='res4a_relu', data=res4a, act_type='relu')
res4b1_branch2a = mx.symbol.Convolution(name='res4b1_branch2a', data=res4a_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b1_branch2a = mx.symbol.BatchNorm(name='bn4b1_branch2a', data=res4b1_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b1_branch2a = bn4b1_branch2a
res4b1_branch2a_relu = mx.symbol.Activation(name='res4b1_branch2a_relu', data=scale4b1_branch2a, act_type='relu')
res4b1_branch2b = mx.symbol.Convolution(name='res4b1_branch2b', data=res4b1_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b1_branch2b = mx.symbol.BatchNorm(name='bn4b1_branch2b', data=res4b1_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b1_branch2b = bn4b1_branch2b
res4b1_branch2b_relu = mx.symbol.Activation(name='res4b1_branch2b_relu', data=scale4b1_branch2b, act_type='relu')
res4b1_branch2c = mx.symbol.Convolution(name='res4b1_branch2c', data=res4b1_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b1_branch2c = mx.symbol.BatchNorm(name='bn4b1_branch2c', data=res4b1_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b1_branch2c = bn4b1_branch2c
res4b1 = mx.symbol.broadcast_add(*[res4a_relu, scale4b1_branch2c], name='res4b1')
res4b1_relu = mx.symbol.Activation(name='res4b1_relu', data=res4b1, act_type='relu')
res4b2_branch2a = mx.symbol.Convolution(name='res4b2_branch2a', data=res4b1_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b2_branch2a = mx.symbol.BatchNorm(name='bn4b2_branch2a', data=res4b2_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b2_branch2a = bn4b2_branch2a
res4b2_branch2a_relu = mx.symbol.Activation(name='res4b2_branch2a_relu', data=scale4b2_branch2a, act_type='relu')
res4b2_branch2b = mx.symbol.Convolution(name='res4b2_branch2b', data=res4b2_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b2_branch2b = mx.symbol.BatchNorm(name='bn4b2_branch2b', data=res4b2_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b2_branch2b = bn4b2_branch2b
res4b2_branch2b_relu = mx.symbol.Activation(name='res4b2_branch2b_relu', data=scale4b2_branch2b, act_type='relu')
res4b2_branch2c = mx.symbol.Convolution(name='res4b2_branch2c', data=res4b2_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b2_branch2c = mx.symbol.BatchNorm(name='bn4b2_branch2c', data=res4b2_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b2_branch2c = bn4b2_branch2c
res4b2 = mx.symbol.broadcast_add(*[res4b1_relu, scale4b2_branch2c], name='res4b2')
res4b2_relu = mx.symbol.Activation(name='res4b2_relu', data=res4b2, act_type='relu')
res4b3_branch2a = mx.symbol.Convolution(name='res4b3_branch2a', data=res4b2_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b3_branch2a = mx.symbol.BatchNorm(name='bn4b3_branch2a', data=res4b3_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b3_branch2a = bn4b3_branch2a
res4b3_branch2a_relu = mx.symbol.Activation(name='res4b3_branch2a_relu', data=scale4b3_branch2a, act_type='relu')
res4b3_branch2b = mx.symbol.Convolution(name='res4b3_branch2b', data=res4b3_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b3_branch2b = mx.symbol.BatchNorm(name='bn4b3_branch2b', data=res4b3_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b3_branch2b = bn4b3_branch2b
res4b3_branch2b_relu = mx.symbol.Activation(name='res4b3_branch2b_relu', data=scale4b3_branch2b, act_type='relu')
res4b3_branch2c = mx.symbol.Convolution(name='res4b3_branch2c', data=res4b3_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b3_branch2c = mx.symbol.BatchNorm(name='bn4b3_branch2c', data=res4b3_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b3_branch2c = bn4b3_branch2c
res4b3 = mx.symbol.broadcast_add(*[res4b2_relu, scale4b3_branch2c], name='res4b3')
res4b3_relu = mx.symbol.Activation(name='res4b3_relu', data=res4b3, act_type='relu')
res4b4_branch2a = mx.symbol.Convolution(name='res4b4_branch2a', data=res4b3_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b4_branch2a = mx.symbol.BatchNorm(name='bn4b4_branch2a', data=res4b4_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b4_branch2a = bn4b4_branch2a
res4b4_branch2a_relu = mx.symbol.Activation(name='res4b4_branch2a_relu', data=scale4b4_branch2a, act_type='relu')
res4b4_branch2b = mx.symbol.Convolution(name='res4b4_branch2b', data=res4b4_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b4_branch2b = mx.symbol.BatchNorm(name='bn4b4_branch2b', data=res4b4_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b4_branch2b = bn4b4_branch2b
res4b4_branch2b_relu = mx.symbol.Activation(name='res4b4_branch2b_relu', data=scale4b4_branch2b, act_type='relu')
res4b4_branch2c = mx.symbol.Convolution(name='res4b4_branch2c', data=res4b4_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b4_branch2c = mx.symbol.BatchNorm(name='bn4b4_branch2c', data=res4b4_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b4_branch2c = bn4b4_branch2c
res4b4 = mx.symbol.broadcast_add(*[res4b3_relu, scale4b4_branch2c], name='res4b4')
res4b4_relu = mx.symbol.Activation(name='res4b4_relu', data=res4b4, act_type='relu')
res4b5_branch2a = mx.symbol.Convolution(name='res4b5_branch2a', data=res4b4_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b5_branch2a = mx.symbol.BatchNorm(name='bn4b5_branch2a', data=res4b5_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b5_branch2a = bn4b5_branch2a
res4b5_branch2a_relu = mx.symbol.Activation(name='res4b5_branch2a_relu', data=scale4b5_branch2a, act_type='relu')
res4b5_branch2b = mx.symbol.Convolution(name='res4b5_branch2b', data=res4b5_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b5_branch2b = mx.symbol.BatchNorm(name='bn4b5_branch2b', data=res4b5_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b5_branch2b = bn4b5_branch2b
res4b5_branch2b_relu = mx.symbol.Activation(name='res4b5_branch2b_relu', data=scale4b5_branch2b, act_type='relu')
res4b5_branch2c = mx.symbol.Convolution(name='res4b5_branch2c', data=res4b5_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b5_branch2c = mx.symbol.BatchNorm(name='bn4b5_branch2c', data=res4b5_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b5_branch2c = bn4b5_branch2c
res4b5 = mx.symbol.broadcast_add(*[res4b4_relu, scale4b5_branch2c], name='res4b5')
res4b5_relu = mx.symbol.Activation(name='res4b5_relu', data=res4b5, act_type='relu')
res4b6_branch2a = mx.symbol.Convolution(name='res4b6_branch2a', data=res4b5_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b6_branch2a = mx.symbol.BatchNorm(name='bn4b6_branch2a', data=res4b6_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b6_branch2a = bn4b6_branch2a
res4b6_branch2a_relu = mx.symbol.Activation(name='res4b6_branch2a_relu', data=scale4b6_branch2a, act_type='relu')
res4b6_branch2b = mx.symbol.Convolution(name='res4b6_branch2b', data=res4b6_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b6_branch2b = mx.symbol.BatchNorm(name='bn4b6_branch2b', data=res4b6_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b6_branch2b = bn4b6_branch2b
res4b6_branch2b_relu = mx.symbol.Activation(name='res4b6_branch2b_relu', data=scale4b6_branch2b, act_type='relu')
res4b6_branch2c = mx.symbol.Convolution(name='res4b6_branch2c', data=res4b6_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b6_branch2c = mx.symbol.BatchNorm(name='bn4b6_branch2c', data=res4b6_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b6_branch2c = bn4b6_branch2c
res4b6 = mx.symbol.broadcast_add(*[res4b5_relu, scale4b6_branch2c], name='res4b6')
res4b6_relu = mx.symbol.Activation(name='res4b6_relu', data=res4b6, act_type='relu')
res4b7_branch2a = mx.symbol.Convolution(name='res4b7_branch2a', data=res4b6_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b7_branch2a = mx.symbol.BatchNorm(name='bn4b7_branch2a', data=res4b7_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b7_branch2a = bn4b7_branch2a
res4b7_branch2a_relu = mx.symbol.Activation(name='res4b7_branch2a_relu', data=scale4b7_branch2a, act_type='relu')
res4b7_branch2b = mx.symbol.Convolution(name='res4b7_branch2b', data=res4b7_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b7_branch2b = mx.symbol.BatchNorm(name='bn4b7_branch2b', data=res4b7_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b7_branch2b = bn4b7_branch2b
res4b7_branch2b_relu = mx.symbol.Activation(name='res4b7_branch2b_relu', data=scale4b7_branch2b, act_type='relu')
res4b7_branch2c = mx.symbol.Convolution(name='res4b7_branch2c', data=res4b7_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b7_branch2c = mx.symbol.BatchNorm(name='bn4b7_branch2c', data=res4b7_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b7_branch2c = bn4b7_branch2c
res4b7 = mx.symbol.broadcast_add(*[res4b6_relu, scale4b7_branch2c], name='res4b7')
res4b7_relu = mx.symbol.Activation(name='res4b7_relu', data=res4b7, act_type='relu')
res4b8_branch2a = mx.symbol.Convolution(name='res4b8_branch2a', data=res4b7_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b8_branch2a = mx.symbol.BatchNorm(name='bn4b8_branch2a', data=res4b8_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b8_branch2a = bn4b8_branch2a
res4b8_branch2a_relu = mx.symbol.Activation(name='res4b8_branch2a_relu', data=scale4b8_branch2a, act_type='relu')
res4b8_branch2b = mx.symbol.Convolution(name='res4b8_branch2b', data=res4b8_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b8_branch2b = mx.symbol.BatchNorm(name='bn4b8_branch2b', data=res4b8_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b8_branch2b = bn4b8_branch2b
res4b8_branch2b_relu = mx.symbol.Activation(name='res4b8_branch2b_relu', data=scale4b8_branch2b, act_type='relu')
res4b8_branch2c = mx.symbol.Convolution(name='res4b8_branch2c', data=res4b8_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b8_branch2c = mx.symbol.BatchNorm(name='bn4b8_branch2c', data=res4b8_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b8_branch2c = bn4b8_branch2c
res4b8 = mx.symbol.broadcast_add(*[res4b7_relu, scale4b8_branch2c], name='res4b8')
res4b8_relu = mx.symbol.Activation(name='res4b8_relu', data=res4b8, act_type='relu')
res4b9_branch2a = mx.symbol.Convolution(name='res4b9_branch2a', data=res4b8_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b9_branch2a = mx.symbol.BatchNorm(name='bn4b9_branch2a', data=res4b9_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b9_branch2a = bn4b9_branch2a
res4b9_branch2a_relu = mx.symbol.Activation(name='res4b9_branch2a_relu', data=scale4b9_branch2a, act_type='relu')
res4b9_branch2b = mx.symbol.Convolution(name='res4b9_branch2b', data=res4b9_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b9_branch2b = mx.symbol.BatchNorm(name='bn4b9_branch2b', data=res4b9_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b9_branch2b = bn4b9_branch2b
res4b9_branch2b_relu = mx.symbol.Activation(name='res4b9_branch2b_relu', data=scale4b9_branch2b, act_type='relu')
res4b9_branch2c = mx.symbol.Convolution(name='res4b9_branch2c', data=res4b9_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b9_branch2c = mx.symbol.BatchNorm(name='bn4b9_branch2c', data=res4b9_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b9_branch2c = bn4b9_branch2c
res4b9 = mx.symbol.broadcast_add(*[res4b8_relu, scale4b9_branch2c], name='res4b9')
res4b9_relu = mx.symbol.Activation(name='res4b9_relu', data=res4b9, act_type='relu')
res4b10_branch2a = mx.symbol.Convolution(name='res4b10_branch2a', data=res4b9_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b10_branch2a = mx.symbol.BatchNorm(name='bn4b10_branch2a', data=res4b10_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b10_branch2a = bn4b10_branch2a
res4b10_branch2a_relu = mx.symbol.Activation(name='res4b10_branch2a_relu', data=scale4b10_branch2a, act_type='relu')
res4b10_branch2b = mx.symbol.Convolution(name='res4b10_branch2b', data=res4b10_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b10_branch2b = mx.symbol.BatchNorm(name='bn4b10_branch2b', data=res4b10_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b10_branch2b = bn4b10_branch2b
res4b10_branch2b_relu = mx.symbol.Activation(name='res4b10_branch2b_relu', data=scale4b10_branch2b, act_type='relu')
res4b10_branch2c = mx.symbol.Convolution(name='res4b10_branch2c', data=res4b10_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b10_branch2c = mx.symbol.BatchNorm(name='bn4b10_branch2c', data=res4b10_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b10_branch2c = bn4b10_branch2c
res4b10 = mx.symbol.broadcast_add(*[res4b9_relu, scale4b10_branch2c], name='res4b10')
res4b10_relu = mx.symbol.Activation(name='res4b10_relu', data=res4b10, act_type='relu')
res4b11_branch2a = mx.symbol.Convolution(name='res4b11_branch2a', data=res4b10_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b11_branch2a = mx.symbol.BatchNorm(name='bn4b11_branch2a', data=res4b11_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b11_branch2a = bn4b11_branch2a
res4b11_branch2a_relu = mx.symbol.Activation(name='res4b11_branch2a_relu', data=scale4b11_branch2a, act_type='relu')
res4b11_branch2b = mx.symbol.Convolution(name='res4b11_branch2b', data=res4b11_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b11_branch2b = mx.symbol.BatchNorm(name='bn4b11_branch2b', data=res4b11_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b11_branch2b = bn4b11_branch2b
res4b11_branch2b_relu = mx.symbol.Activation(name='res4b11_branch2b_relu', data=scale4b11_branch2b, act_type='relu')
res4b11_branch2c = mx.symbol.Convolution(name='res4b11_branch2c', data=res4b11_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b11_branch2c = mx.symbol.BatchNorm(name='bn4b11_branch2c', data=res4b11_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b11_branch2c = bn4b11_branch2c
res4b11 = mx.symbol.broadcast_add(*[res4b10_relu, scale4b11_branch2c], name='res4b11')
res4b11_relu = mx.symbol.Activation(name='res4b11_relu', data=res4b11, act_type='relu')
res4b12_branch2a = mx.symbol.Convolution(name='res4b12_branch2a', data=res4b11_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b12_branch2a = mx.symbol.BatchNorm(name='bn4b12_branch2a', data=res4b12_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b12_branch2a = bn4b12_branch2a
res4b12_branch2a_relu = mx.symbol.Activation(name='res4b12_branch2a_relu', data=scale4b12_branch2a, act_type='relu')
res4b12_branch2b = mx.symbol.Convolution(name='res4b12_branch2b', data=res4b12_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b12_branch2b = mx.symbol.BatchNorm(name='bn4b12_branch2b', data=res4b12_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b12_branch2b = bn4b12_branch2b
res4b12_branch2b_relu = mx.symbol.Activation(name='res4b12_branch2b_relu', data=scale4b12_branch2b, act_type='relu')
res4b12_branch2c = mx.symbol.Convolution(name='res4b12_branch2c', data=res4b12_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b12_branch2c = mx.symbol.BatchNorm(name='bn4b12_branch2c', data=res4b12_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b12_branch2c = bn4b12_branch2c
res4b12 = mx.symbol.broadcast_add(*[res4b11_relu, scale4b12_branch2c], name='res4b12')
res4b12_relu = mx.symbol.Activation(name='res4b12_relu', data=res4b12, act_type='relu')
res4b13_branch2a = mx.symbol.Convolution(name='res4b13_branch2a', data=res4b12_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b13_branch2a = mx.symbol.BatchNorm(name='bn4b13_branch2a', data=res4b13_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b13_branch2a = bn4b13_branch2a
res4b13_branch2a_relu = mx.symbol.Activation(name='res4b13_branch2a_relu', data=scale4b13_branch2a, act_type='relu')
res4b13_branch2b = mx.symbol.Convolution(name='res4b13_branch2b', data=res4b13_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b13_branch2b = mx.symbol.BatchNorm(name='bn4b13_branch2b', data=res4b13_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b13_branch2b = bn4b13_branch2b
res4b13_branch2b_relu = mx.symbol.Activation(name='res4b13_branch2b_relu', data=scale4b13_branch2b, act_type='relu')
res4b13_branch2c = mx.symbol.Convolution(name='res4b13_branch2c', data=res4b13_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b13_branch2c = mx.symbol.BatchNorm(name='bn4b13_branch2c', data=res4b13_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b13_branch2c = bn4b13_branch2c
res4b13 = mx.symbol.broadcast_add(*[res4b12_relu, scale4b13_branch2c], name='res4b13')
res4b13_relu = mx.symbol.Activation(name='res4b13_relu', data=res4b13, act_type='relu')
res4b14_branch2a = mx.symbol.Convolution(name='res4b14_branch2a', data=res4b13_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b14_branch2a = mx.symbol.BatchNorm(name='bn4b14_branch2a', data=res4b14_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b14_branch2a = bn4b14_branch2a
res4b14_branch2a_relu = mx.symbol.Activation(name='res4b14_branch2a_relu', data=scale4b14_branch2a, act_type='relu')
res4b14_branch2b = mx.symbol.Convolution(name='res4b14_branch2b', data=res4b14_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b14_branch2b = mx.symbol.BatchNorm(name='bn4b14_branch2b', data=res4b14_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b14_branch2b = bn4b14_branch2b
res4b14_branch2b_relu = mx.symbol.Activation(name='res4b14_branch2b_relu', data=scale4b14_branch2b, act_type='relu')
res4b14_branch2c = mx.symbol.Convolution(name='res4b14_branch2c', data=res4b14_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b14_branch2c = mx.symbol.BatchNorm(name='bn4b14_branch2c', data=res4b14_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b14_branch2c = bn4b14_branch2c
res4b14 = mx.symbol.broadcast_add(*[res4b13_relu, scale4b14_branch2c], name='res4b14')
res4b14_relu = mx.symbol.Activation(name='res4b14_relu', data=res4b14, act_type='relu')
res4b15_branch2a = mx.symbol.Convolution(name='res4b15_branch2a', data=res4b14_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b15_branch2a = mx.symbol.BatchNorm(name='bn4b15_branch2a', data=res4b15_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b15_branch2a = bn4b15_branch2a
res4b15_branch2a_relu = mx.symbol.Activation(name='res4b15_branch2a_relu', data=scale4b15_branch2a, act_type='relu')
res4b15_branch2b = mx.symbol.Convolution(name='res4b15_branch2b', data=res4b15_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b15_branch2b = mx.symbol.BatchNorm(name='bn4b15_branch2b', data=res4b15_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b15_branch2b = bn4b15_branch2b
res4b15_branch2b_relu = mx.symbol.Activation(name='res4b15_branch2b_relu', data=scale4b15_branch2b, act_type='relu')
res4b15_branch2c = mx.symbol.Convolution(name='res4b15_branch2c', data=res4b15_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b15_branch2c = mx.symbol.BatchNorm(name='bn4b15_branch2c', data=res4b15_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b15_branch2c = bn4b15_branch2c
res4b15 = mx.symbol.broadcast_add(*[res4b14_relu, scale4b15_branch2c], name='res4b15')
res4b15_relu = mx.symbol.Activation(name='res4b15_relu', data=res4b15, act_type='relu')
res4b16_branch2a = mx.symbol.Convolution(name='res4b16_branch2a', data=res4b15_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b16_branch2a = mx.symbol.BatchNorm(name='bn4b16_branch2a', data=res4b16_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b16_branch2a = bn4b16_branch2a
res4b16_branch2a_relu = mx.symbol.Activation(name='res4b16_branch2a_relu', data=scale4b16_branch2a, act_type='relu')
res4b16_branch2b = mx.symbol.Convolution(name='res4b16_branch2b', data=res4b16_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b16_branch2b = mx.symbol.BatchNorm(name='bn4b16_branch2b', data=res4b16_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b16_branch2b = bn4b16_branch2b
res4b16_branch2b_relu = mx.symbol.Activation(name='res4b16_branch2b_relu', data=scale4b16_branch2b, act_type='relu')
res4b16_branch2c = mx.symbol.Convolution(name='res4b16_branch2c', data=res4b16_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b16_branch2c = mx.symbol.BatchNorm(name='bn4b16_branch2c', data=res4b16_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b16_branch2c = bn4b16_branch2c
res4b16 = mx.symbol.broadcast_add(*[res4b15_relu, scale4b16_branch2c], name='res4b16')
res4b16_relu = mx.symbol.Activation(name='res4b16_relu', data=res4b16, act_type='relu')
res4b17_branch2a = mx.symbol.Convolution(name='res4b17_branch2a', data=res4b16_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b17_branch2a = mx.symbol.BatchNorm(name='bn4b17_branch2a', data=res4b17_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b17_branch2a = bn4b17_branch2a
res4b17_branch2a_relu = mx.symbol.Activation(name='res4b17_branch2a_relu', data=scale4b17_branch2a, act_type='relu')
res4b17_branch2b = mx.symbol.Convolution(name='res4b17_branch2b', data=res4b17_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b17_branch2b = mx.symbol.BatchNorm(name='bn4b17_branch2b', data=res4b17_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b17_branch2b = bn4b17_branch2b
res4b17_branch2b_relu = mx.symbol.Activation(name='res4b17_branch2b_relu', data=scale4b17_branch2b, act_type='relu')
res4b17_branch2c = mx.symbol.Convolution(name='res4b17_branch2c', data=res4b17_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b17_branch2c = mx.symbol.BatchNorm(name='bn4b17_branch2c', data=res4b17_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b17_branch2c = bn4b17_branch2c
res4b17 = mx.symbol.broadcast_add(*[res4b16_relu, scale4b17_branch2c], name='res4b17')
res4b17_relu = mx.symbol.Activation(name='res4b17_relu', data=res4b17, act_type='relu')
res4b18_branch2a = mx.symbol.Convolution(name='res4b18_branch2a', data=res4b17_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b18_branch2a = mx.symbol.BatchNorm(name='bn4b18_branch2a', data=res4b18_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b18_branch2a = bn4b18_branch2a
res4b18_branch2a_relu = mx.symbol.Activation(name='res4b18_branch2a_relu', data=scale4b18_branch2a, act_type='relu')
res4b18_branch2b = mx.symbol.Convolution(name='res4b18_branch2b', data=res4b18_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b18_branch2b = mx.symbol.BatchNorm(name='bn4b18_branch2b', data=res4b18_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b18_branch2b = bn4b18_branch2b
res4b18_branch2b_relu = mx.symbol.Activation(name='res4b18_branch2b_relu', data=scale4b18_branch2b, act_type='relu')
res4b18_branch2c = mx.symbol.Convolution(name='res4b18_branch2c', data=res4b18_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b18_branch2c = mx.symbol.BatchNorm(name='bn4b18_branch2c', data=res4b18_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b18_branch2c = bn4b18_branch2c
res4b18 = mx.symbol.broadcast_add(*[res4b17_relu, scale4b18_branch2c], name='res4b18')
res4b18_relu = mx.symbol.Activation(name='res4b18_relu', data=res4b18, act_type='relu')
res4b19_branch2a = mx.symbol.Convolution(name='res4b19_branch2a', data=res4b18_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b19_branch2a = mx.symbol.BatchNorm(name='bn4b19_branch2a', data=res4b19_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b19_branch2a = bn4b19_branch2a
res4b19_branch2a_relu = mx.symbol.Activation(name='res4b19_branch2a_relu', data=scale4b19_branch2a, act_type='relu')
res4b19_branch2b = mx.symbol.Convolution(name='res4b19_branch2b', data=res4b19_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b19_branch2b = mx.symbol.BatchNorm(name='bn4b19_branch2b', data=res4b19_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b19_branch2b = bn4b19_branch2b
res4b19_branch2b_relu = mx.symbol.Activation(name='res4b19_branch2b_relu', data=scale4b19_branch2b, act_type='relu')
res4b19_branch2c = mx.symbol.Convolution(name='res4b19_branch2c', data=res4b19_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b19_branch2c = mx.symbol.BatchNorm(name='bn4b19_branch2c', data=res4b19_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b19_branch2c = bn4b19_branch2c
res4b19 = mx.symbol.broadcast_add(*[res4b18_relu, scale4b19_branch2c], name='res4b19')
res4b19_relu = mx.symbol.Activation(name='res4b19_relu', data=res4b19, act_type='relu')
res4b20_branch2a = mx.symbol.Convolution(name='res4b20_branch2a', data=res4b19_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b20_branch2a = mx.symbol.BatchNorm(name='bn4b20_branch2a', data=res4b20_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b20_branch2a = bn4b20_branch2a
res4b20_branch2a_relu = mx.symbol.Activation(name='res4b20_branch2a_relu', data=scale4b20_branch2a, act_type='relu')
res4b20_branch2b = mx.symbol.Convolution(name='res4b20_branch2b', data=res4b20_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b20_branch2b = mx.symbol.BatchNorm(name='bn4b20_branch2b', data=res4b20_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b20_branch2b = bn4b20_branch2b
res4b20_branch2b_relu = mx.symbol.Activation(name='res4b20_branch2b_relu', data=scale4b20_branch2b, act_type='relu')
res4b20_branch2c = mx.symbol.Convolution(name='res4b20_branch2c', data=res4b20_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b20_branch2c = mx.symbol.BatchNorm(name='bn4b20_branch2c', data=res4b20_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b20_branch2c = bn4b20_branch2c
res4b20 = mx.symbol.broadcast_add(*[res4b19_relu, scale4b20_branch2c], name='res4b20')
res4b20_relu = mx.symbol.Activation(name='res4b20_relu', data=res4b20, act_type='relu')
res4b21_branch2a = mx.symbol.Convolution(name='res4b21_branch2a', data=res4b20_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b21_branch2a = mx.symbol.BatchNorm(name='bn4b21_branch2a', data=res4b21_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b21_branch2a = bn4b21_branch2a
res4b21_branch2a_relu = mx.symbol.Activation(name='res4b21_branch2a_relu', data=scale4b21_branch2a, act_type='relu')
res4b21_branch2b = mx.symbol.Convolution(name='res4b21_branch2b', data=res4b21_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b21_branch2b = mx.symbol.BatchNorm(name='bn4b21_branch2b', data=res4b21_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b21_branch2b = bn4b21_branch2b
res4b21_branch2b_relu = mx.symbol.Activation(name='res4b21_branch2b_relu', data=scale4b21_branch2b, act_type='relu')
res4b21_branch2c = mx.symbol.Convolution(name='res4b21_branch2c', data=res4b21_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b21_branch2c = mx.symbol.BatchNorm(name='bn4b21_branch2c', data=res4b21_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b21_branch2c = bn4b21_branch2c
res4b21 = mx.symbol.broadcast_add(*[res4b20_relu, scale4b21_branch2c], name='res4b21')
res4b21_relu = mx.symbol.Activation(name='res4b21_relu', data=res4b21, act_type='relu')
res4b22_branch2a = mx.symbol.Convolution(name='res4b22_branch2a', data=res4b21_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b22_branch2a = mx.symbol.BatchNorm(name='bn4b22_branch2a', data=res4b22_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b22_branch2a = bn4b22_branch2a
res4b22_branch2a_relu = mx.symbol.Activation(name='res4b22_branch2a_relu', data=scale4b22_branch2a, act_type='relu')
if with_dpyramid:
res4b22_branch2b_offset = mx.symbol.Convolution(name='res4b22_branch2b_offset', data=res4b22_branch2a_relu, num_filter=72, pad=(1, 1), kernel=(3, 3), stride=(1, 1))
res4b22_branch2b = mx.contrib.symbol.DeformableConvolution(name='res4b22_branch2b', data=res4b22_branch2a_relu, offset=res4b22_branch2b_offset, num_filter=256, pad=(1, 1), kernel=(3, 3), num_deformable_group=4, stride=(1, 1), no_bias=True)
else:
res4b22_branch2b = mx.symbol.Convolution(name='res4b22_branch2b', data=res4b22_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b22_branch2b = mx.symbol.BatchNorm(name='bn4b22_branch2b', data=res4b22_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b22_branch2b = bn4b22_branch2b
res4b22_branch2b_relu = mx.symbol.Activation(name='res4b22_branch2b_relu', data=scale4b22_branch2b, act_type='relu')
res4b22_branch2c = mx.symbol.Convolution(name='res4b22_branch2c', data=res4b22_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b22_branch2c = mx.symbol.BatchNorm(name='bn4b22_branch2c', data=res4b22_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b22_branch2c = bn4b22_branch2c
res4b22 = mx.symbol.broadcast_add(*[res4b21_relu, scale4b22_branch2c], name='res4b22')
res4b22_relu = mx.symbol.Activation(name='res4b22_relu', data=res4b22, act_type='relu')
if with_dilated:
res5_stride = (1, 1)
res5_dilate = (2, 2)
else:
res5_stride = (2, 2)
res5_dilate = (1, 1)
res5a_branch2a = mx.symbol.Convolution(name='res5a_branch2a', data=res4b22_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=res5_stride, no_bias=True)
bn5a_branch2a = mx.symbol.BatchNorm(name='bn5a_branch2a', data=res5a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch2a = bn5a_branch2a
res5a_branch2a_relu = mx.symbol.Activation(name='res5a_branch2a_relu', data=scale5a_branch2a, act_type='relu')
if with_dconv:
res5a_branch2b_offset = mx.symbol.Convolution(name='res5a_branch2b_offset', data=res5a_branch2a_relu, num_filter=72, pad=res5_dilate, kernel=(3, 3), dilate=res5_dilate)
res5a_branch2b = mx.contrib.symbol.DeformableConvolution(name='res5a_branch2b', data=res5a_branch2a_relu, offset=res5a_branch2b_offset, num_filter=512, pad=res5_dilate, kernel=(3, 3), num_deformable_group=4, stride=(1, 1), dilate=res5_dilate, no_bias=True)
else:
res5a_branch2b = mx.symbol.Convolution(name='res5a_branch2b', data=res5a_branch2a_relu, num_filter=512, pad=res5_dilate, kernel=(3, 3), stride=(1, 1), dilate=res5_dilate, no_bias=True)
bn5a_branch2b = mx.symbol.BatchNorm(name='bn5a_branch2b', data=res5a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch2b = bn5a_branch2b
res5a_branch2b_relu = mx.symbol.Activation(name='res5a_branch2b_relu', data=scale5a_branch2b, act_type='relu')
res5a_branch2c = mx.symbol.Convolution(name='res5a_branch2c', data=res5a_branch2b_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5a_branch2c = mx.symbol.BatchNorm(name='bn5a_branch2c', data=res5a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch2c = bn5a_branch2c
res5a_branch1 = mx.symbol.Convolution(name='res5a_branch1', data=res4b22_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=res5_stride, no_bias=True)
bn5a_branch1 = mx.symbol.BatchNorm(name='bn5a_branch1', data=res5a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch1 = bn5a_branch1
res5a = mx.symbol.broadcast_add(*[scale5a_branch1, scale5a_branch2c], name='res5a')
res5a_relu = mx.symbol.Activation(name='res5a_relu', data=res5a, act_type='relu')
res5b_branch2a = mx.symbol.Convolution(name='res5b_branch2a', data=res5a_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5b_branch2a = mx.symbol.BatchNorm(name='bn5b_branch2a', data=res5b_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale5b_branch2a = bn5b_branch2a
res5b_branch2a_relu = mx.symbol.Activation(name='res5b_branch2a_relu', data=scale5b_branch2a, act_type='relu')
if with_dconv:
res5b_branch2b_offset = mx.symbol.Convolution(name='res5b_branch2b_offset', data=res5b_branch2a_relu, num_filter=72, pad=res5_dilate, kernel=(3, 3), dilate=res5_dilate)
res5b_branch2b = mx.contrib.symbol.DeformableConvolution(name='res5b_branch2b', data=res5b_branch2a_relu, offset=res5b_branch2b_offset, num_filter=512, pad=res5_dilate, kernel=(3, 3), num_deformable_group=4, dilate=res5_dilate, no_bias=True)
else:
res5b_branch2b = mx.symbol.Convolution(name='res5b_branch2b', data=res5b_branch2a_relu, num_filter=512, pad=res5_dilate, kernel=(3, 3), stride=(1, 1), dilate=res5_dilate, no_bias=True)
bn5b_branch2b = mx.symbol.BatchNorm(name='bn5b_branch2b', data=res5b_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale5b_branch2b = bn5b_branch2b
res5b_branch2b_relu = mx.symbol.Activation(name='res5b_branch2b_relu', data=scale5b_branch2b, act_type='relu')
res5b_branch2c = mx.symbol.Convolution(name='res5b_branch2c', data=res5b_branch2b_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5b_branch2c = mx.symbol.BatchNorm(name='bn5b_branch2c', data=res5b_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale5b_branch2c = bn5b_branch2c
res5b = mx.symbol.broadcast_add(*[res5a_relu, scale5b_branch2c], name='res5b')
res5b_relu = mx.symbol.Activation(name='res5b_relu', data=res5b, act_type='relu')
res5c_branch2a = mx.symbol.Convolution(name='res5c_branch2a', data=res5b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5c_branch2a = mx.symbol.BatchNorm(name='bn5c_branch2a', data=res5c_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale5c_branch2a = bn5c_branch2a
res5c_branch2a_relu = mx.symbol.Activation(name='res5c_branch2a_relu', data=scale5c_branch2a, act_type='relu')
if with_dconv:
res5c_branch2b_offset = mx.symbol.Convolution(name='res5c_branch2b_offset', data=res5c_branch2a_relu, num_filter=72, pad=res5_dilate, kernel=(3, 3), dilate=res5_dilate)
res5c_branch2b = mx.contrib.symbol.DeformableConvolution(name='res5c_branch2b', data=res5c_branch2a_relu, offset=res5c_branch2b_offset, num_filter=512, pad=res5_dilate, kernel=(3, 3), num_deformable_group=4, dilate=res5_dilate, no_bias=True)
else:
res5c_branch2b = mx.symbol.Convolution(name='res5c_branch2b', data=res5c_branch2a_relu, num_filter=512, pad=res5_dilate, kernel=(3, 3), stride=(1, 1), dilate=res5_dilate, no_bias=True)
bn5c_branch2b = mx.symbol.BatchNorm(name='bn5c_branch2b', data=res5c_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale5c_branch2b = bn5c_branch2b
res5c_branch2b_relu = mx.symbol.Activation(name='res5c_branch2b_relu', data=scale5c_branch2b, act_type='relu')
res5c_branch2c = mx.symbol.Convolution(name='res5c_branch2c', data=res5c_branch2b_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5c_branch2c = mx.symbol.BatchNorm(name='bn5c_branch2c', data=res5c_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale5c_branch2c = bn5c_branch2c
res5c = mx.symbol.broadcast_add(*[res5b_relu, scale5c_branch2c], name='res5c')
res5c_relu = mx.symbol.Activation(name='res5c_relu', data=res5c, act_type='relu')
return (res2c_relu, res3b3_relu, res4b22_relu, res5c_relu)
def get_fpn_feature(self, c2, c3, c4, c5, feature_dim=256):
fpn_p5_1x1 = mx.symbol.Convolution(data=c5, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p5_1x1')
fpn_p4_1x1 = mx.symbol.Convolution(data=c4, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p4_1x1')
fpn_p3_1x1 = mx.symbol.Convolution(data=c3, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p3_1x1')
fpn_p2_1x1 = mx.symbol.Convolution(data=c2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p2_1x1')
fpn_p5_upsample = mx.symbol.UpSampling(fpn_p5_1x1, scale=2, sample_type='nearest', name='fpn_p5_upsample')
fpn_p4_plus = mx.sym.ElementWiseSum(*[fpn_p5_upsample, fpn_p4_1x1], name='fpn_p4_sum')
fpn_p4_upsample = mx.symbol.UpSampling(fpn_p4_plus, scale=2, sample_type='nearest', name='fpn_p4_upsample')
fpn_p3_plus = mx.sym.ElementWiseSum(*[fpn_p4_upsample, fpn_p3_1x1], name='fpn_p3_sum')
fpn_p3_upsample = mx.symbol.UpSampling(fpn_p3_plus, scale=2, sample_type='nearest', name='fpn_p3_upsample')
fpn_p2_plus = mx.sym.ElementWiseSum(*[fpn_p3_upsample, fpn_p2_1x1], name='fpn_p2_sum')
fpn_p6 = mx.sym.Convolution(data=c5, kernel=(3, 3), pad=(1, 1), stride=(2, 2), num_filter=feature_dim, name='fpn_p6')
fpn_p5 = mx.symbol.Convolution(data=fpn_p5_1x1, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p5')
fpn_p4 = mx.symbol.Convolution(data=fpn_p4_plus, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p4')
fpn_p3 = mx.symbol.Convolution(data=fpn_p3_plus, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p3')
fpn_p2 = mx.symbol.Convolution(data=fpn_p2_plus, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p2')
return (fpn_p2, fpn_p3, fpn_p4, fpn_p5, fpn_p6)
def get_rpn_subnet(self, data, num_anchors, suffix):
rpn_conv = mx.sym.Convolution(data=data, kernel=(3, 3), pad=(1, 1), num_filter=512, name=('rpn_conv_' + suffix), weight=self.shared_param_dict['rpn_conv_weight'], bias=self.shared_param_dict['rpn_conv_bias'])
rpn_relu = mx.sym.Activation(data=rpn_conv, act_type='relu', name=('rpn_relu_' + suffix))
rpn_cls_score = mx.sym.Convolution(data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=(2 * num_anchors), name=('rpn_cls_score_' + suffix), weight=self.shared_param_dict['rpn_cls_score_weight'], bias=self.shared_param_dict['rpn_cls_score_bias'])
rpn_bbox_pred = mx.sym.Convolution(data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=(4 * num_anchors), name=('rpn_bbox_pred_' + suffix), weight=self.shared_param_dict['rpn_bbox_pred_weight'], bias=self.shared_param_dict['rpn_bbox_pred_bias'])
rpn_cls_score_t1 = mx.sym.Reshape(data=rpn_cls_score, shape=(0, 2, (- 1), 0), name=('rpn_cls_score_t1_' + suffix))
rpn_cls_score_t2 = mx.sym.Reshape(data=rpn_cls_score_t1, shape=(0, 2, (- 1)), name=('rpn_cls_score_t2_' + suffix))
rpn_cls_prob = mx.sym.SoftmaxActivation(data=rpn_cls_score_t1, mode='channel', name=('rpn_cls_prob_' + suffix))
rpn_cls_prob_t = mx.sym.Reshape(data=rpn_cls_prob, shape=(0, (2 * num_anchors), (- 1), 0), name=('rpn_cls_prob_t_' + suffix))
rpn_bbox_pred_t = mx.sym.Reshape(data=rpn_bbox_pred, shape=(0, 0, (- 1)), name=('rpn_bbox_pred_t_' + suffix))
return (rpn_cls_score_t2, rpn_cls_prob_t, rpn_bbox_pred_t, rpn_bbox_pred)
def get_deformable_roipooling(self, name, data, rois, output_dim, spatial_scale, param_name, group_size=1, pooled_size=7, sample_per_part=4, part_size=7):
offset = mx.contrib.sym.DeformablePSROIPooling(name=(('offset_' + name) + '_t'), data=data, rois=rois, group_size=group_size, pooled_size=pooled_size, sample_per_part=sample_per_part, no_trans=True, part_size=part_size, output_dim=output_dim, spatial_scale=spatial_scale)
offset = mx.sym.FullyConnected(name=('offset_' + name), data=offset, num_hidden=((part_size * part_size) * 2), lr_mult=0.01, weight=self.shared_param_dict[(('offset_' + param_name) + '_weight')], bias=self.shared_param_dict[(('offset_' + param_name) + '_bias')])
offset_reshape = mx.sym.Reshape(data=offset, shape=((- 1), 2, part_size, part_size), name=('offset_reshape_' + name))
output = mx.contrib.sym.DeformablePSROIPooling(name=('deformable_roi_pool_' + name), data=data, rois=rois, trans=offset_reshape, group_size=group_size, pooled_size=pooled_size, sample_per_part=sample_per_part, no_trans=False, part_size=part_size, output_dim=output_dim, spatial_scale=spatial_scale, trans_std=0.1)
return output
def get_symbol(self, cfg, is_train=True):
num_classes = cfg.dataset.NUM_CLASSES
num_reg_classes = (2 if cfg.CLASS_AGNOSTIC else num_classes)
data = mx.sym.Variable(name='data')
im_info = mx.sym.Variable(name='im_info')
(res2, res3, res4, res5) = self.get_resnet_backbone(data, with_dpyramid=True, with_dconv=True)
(fpn_p2, fpn_p3, fpn_p4, fpn_p5, fpn_p6) = self.get_fpn_feature(res2, res3, res4, res5)
(rpn_cls_score_p2, rpn_prob_p2, rpn_bbox_loss_p2, rpn_bbox_pred_p2) = self.get_rpn_subnet(fpn_p2, cfg.network.NUM_ANCHORS, 'p2')
(rpn_cls_score_p3, rpn_prob_p3, rpn_bbox_loss_p3, rpn_bbox_pred_p3) = self.get_rpn_subnet(fpn_p3, cfg.network.NUM_ANCHORS, 'p3')
(rpn_cls_score_p4, rpn_prob_p4, rpn_bbox_loss_p4, rpn_bbox_pred_p4) = self.get_rpn_subnet(fpn_p4, cfg.network.NUM_ANCHORS, 'p4')
(rpn_cls_score_p5, rpn_prob_p5, rpn_bbox_loss_p5, rpn_bbox_pred_p5) = self.get_rpn_subnet(fpn_p5, cfg.network.NUM_ANCHORS, 'p5')
(rpn_cls_score_p6, rpn_prob_p6, rpn_bbox_loss_p6, rpn_bbox_pred_p6) = self.get_rpn_subnet(fpn_p6, cfg.network.NUM_ANCHORS, 'p6')
rpn_cls_prob_dict = {'rpn_cls_prob_stride64': rpn_prob_p6, 'rpn_cls_prob_stride32': rpn_prob_p5, 'rpn_cls_prob_stride16': rpn_prob_p4, 'rpn_cls_prob_stride8': rpn_prob_p3, 'rpn_cls_prob_stride4': rpn_prob_p2}
rpn_bbox_pred_dict = {'rpn_bbox_pred_stride64': rpn_bbox_pred_p6, 'rpn_bbox_pred_stride32': rpn_bbox_pred_p5, 'rpn_bbox_pred_stride16': rpn_bbox_pred_p4, 'rpn_bbox_pred_stride8': rpn_bbox_pred_p3, 'rpn_bbox_pred_stride4': rpn_bbox_pred_p2}
arg_dict = dict((rpn_cls_prob_dict.items() + rpn_bbox_pred_dict.items()))
if is_train:
rpn_label = mx.sym.Variable(name='label')
rpn_bbox_target = mx.sym.Variable(name='bbox_target')
rpn_bbox_weight = mx.sym.Variable(name='bbox_weight')
gt_boxes = mx.sym.Variable(name='gt_boxes')
rpn_cls_score = mx.sym.Concat(rpn_cls_score_p2, rpn_cls_score_p3, rpn_cls_score_p4, rpn_cls_score_p5, rpn_cls_score_p6, dim=2)
rpn_bbox_loss = mx.sym.Concat(rpn_bbox_loss_p2, rpn_bbox_loss_p3, rpn_bbox_loss_p4, rpn_bbox_loss_p5, rpn_bbox_loss_p6, dim=2)
rpn_cls_output = mx.sym.SoftmaxOutput(data=rpn_cls_score, label=rpn_label, multi_output=True, normalization='valid', use_ignore=True, ignore_label=(- 1), name='rpn_cls_prob')
rpn_bbox_loss = (rpn_bbox_weight * mx.sym.smooth_l1(name='rpn_bbox_loss_l1', scalar=3.0, data=(rpn_bbox_loss - rpn_bbox_target)))
rpn_bbox_loss = mx.sym.MakeLoss(name='rpn_bbox_loss', data=rpn_bbox_loss, grad_scale=(1.0 / cfg.TRAIN.RPN_BATCH_SIZE))
aux_dict = {'op_type': 'pyramid_proposal', 'name': 'rois', 'im_info': im_info, 'feat_stride': tuple(cfg.network.RPN_FEAT_STRIDE), 'scales': tuple(cfg.network.ANCHOR_SCALES), 'ratios': tuple(cfg.network.ANCHOR_RATIOS), 'rpn_pre_nms_top_n': cfg.TRAIN.RPN_PRE_NMS_TOP_N, 'rpn_post_nms_top_n': cfg.TRAIN.RPN_POST_NMS_TOP_N, 'threshold': cfg.TRAIN.RPN_NMS_THRESH, 'rpn_min_size': cfg.TRAIN.RPN_MIN_SIZE}
rois = mx.sym.Custom(**dict((arg_dict.items() + aux_dict.items())))
gt_boxes_reshape = mx.sym.Reshape(data=gt_boxes, shape=((- 1), 5), name='gt_boxes_reshape')
(rois, label, bbox_target, bbox_weight) = mx.sym.Custom(rois=rois, gt_boxes=gt_boxes_reshape, op_type='proposal_target', num_classes=num_reg_classes, batch_images=cfg.TRAIN.BATCH_IMAGES, batch_rois=cfg.TRAIN.BATCH_ROIS, cfg=cPickle.dumps(cfg), fg_fraction=cfg.TRAIN.FG_FRACTION)
else:
aux_dict = {'op_type': 'pyramid_proposal', 'name': 'rois', 'im_info': im_info, 'feat_stride': tuple(cfg.network.RPN_FEAT_STRIDE), 'scales': tuple(cfg.network.ANCHOR_SCALES), 'ratios': tuple(cfg.network.ANCHOR_RATIOS), 'rpn_pre_nms_top_n': cfg.TEST.RPN_PRE_NMS_TOP_N, 'rpn_post_nms_top_n': cfg.TEST.RPN_POST_NMS_TOP_N, 'threshold': cfg.TEST.RPN_NMS_THRESH, 'rpn_min_size': cfg.TEST.RPN_MIN_SIZE}
rois = mx.sym.Custom(**dict((arg_dict.items() + aux_dict.items())))
offset_p2_weight = mx.sym.Variable(name='offset_p2_weight', dtype=np.float32, lr_mult=0.01)
offset_p3_weight = mx.sym.Variable(name='offset_p3_weight', dtype=np.float32, lr_mult=0.01)
offset_p4_weight = mx.sym.Variable(name='offset_p4_weight', dtype=np.float32, lr_mult=0.01)
offset_p5_weight = mx.sym.Variable(name='offset_p5_weight', dtype=np.float32, lr_mult=0.01)
offset_p2_bias = mx.sym.Variable(name='offset_p2_bias', dtype=np.float32, lr_mult=0.01)
offset_p3_bias = mx.sym.Variable(name='offset_p3_bias', dtype=np.float32, lr_mult=0.01)
offset_p4_bias = mx.sym.Variable(name='offset_p4_bias', dtype=np.float32, lr_mult=0.01)
offset_p5_bias = mx.sym.Variable(name='offset_p5_bias', dtype=np.float32, lr_mult=0.01)
roi_pool = mx.symbol.Custom(data_p2=fpn_p2, data_p3=fpn_p3, data_p4=fpn_p4, data_p5=fpn_p5, offset_weight_p2=offset_p2_weight, offset_bias_p2=offset_p2_bias, offset_weight_p3=offset_p3_weight, offset_bias_p3=offset_p3_bias, offset_weight_p4=offset_p4_weight, offset_bias_p4=offset_p4_bias, offset_weight_p5=offset_p5_weight, offset_bias_p5=offset_p5_bias, rois=rois, op_type='fpn_roi_pooling', name='fpn_roi_pooling', with_deformable=True)
fc_new_1 = mx.symbol.FullyConnected(name='fc_new_1', data=roi_pool, num_hidden=1024)
fc_new_1_relu = mx.sym.Activation(data=fc_new_1, act_type='relu', name='fc_new_1_relu')
fc_new_2 = mx.symbol.FullyConnected(name='fc_new_2', data=fc_new_1_relu, num_hidden=1024)
fc_new_2_relu = mx.sym.Activation(data=fc_new_2, act_type='relu', name='fc_new_2_relu')
cls_score = mx.symbol.FullyConnected(name='cls_score', data=fc_new_2_relu, num_hidden=num_classes)
bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=fc_new_2_relu, num_hidden=(num_reg_classes * 4))
if is_train:
if cfg.TRAIN.ENABLE_OHEM:
(labels_ohem, bbox_weights_ohem) = mx.sym.Custom(op_type='BoxAnnotatorOHEM', num_classes=num_classes, num_reg_classes=num_reg_classes, roi_per_img=cfg.TRAIN.BATCH_ROIS_OHEM, cls_score=cls_score, bbox_pred=bbox_pred, labels=label, bbox_targets=bbox_target, bbox_weights=bbox_weight)
cls_prob = mx.sym.SoftmaxOutput(name='cls_prob', data=cls_score, label=labels_ohem, normalization='valid', use_ignore=True, ignore_label=(- 1))
bbox_loss_ = (bbox_weights_ohem * mx.sym.smooth_l1(name='bbox_loss_', scalar=1.0, data=(bbox_pred - bbox_target)))
bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=(1.0 / cfg.TRAIN.BATCH_ROIS_OHEM))
rcnn_label = labels_ohem
else:
cls_prob = mx.sym.SoftmaxOutput(name='cls_prob', data=cls_score, label=label, normalization='valid')
bbox_loss_ = (bbox_weight * mx.sym.smooth_l1(name='bbox_loss_', scalar=1.0, data=(bbox_pred - bbox_target)))
bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=(1.0 / cfg.TRAIN.BATCH_ROIS))
rcnn_label = label
rcnn_label = mx.sym.Reshape(data=rcnn_label, shape=(cfg.TRAIN.BATCH_IMAGES, (- 1)), name='label_reshape')
cls_prob = mx.sym.Reshape(data=cls_prob, shape=(cfg.TRAIN.BATCH_IMAGES, (- 1), num_classes), name='cls_prob_reshape')
bbox_loss = mx.sym.Reshape(data=bbox_loss, shape=(cfg.TRAIN.BATCH_IMAGES, (- 1), (4 * num_reg_classes)), name='bbox_loss_reshape')
group = mx.sym.Group([rpn_cls_output, rpn_bbox_loss, cls_prob, bbox_loss, mx.sym.BlockGrad(rcnn_label)])
else:
cls_prob = mx.sym.SoftmaxActivation(name='cls_prob', data=cls_score)
cls_prob = mx.sym.Reshape(data=cls_prob, shape=(cfg.TEST.BATCH_IMAGES, (- 1), num_classes), name='cls_prob_reshape')
bbox_pred = mx.sym.Reshape(data=bbox_pred, shape=(cfg.TEST.BATCH_IMAGES, (- 1), (4 * num_reg_classes)), name='bbox_pred_reshape')
group = mx.sym.Group([rois, cls_prob, bbox_pred])
self.sym = group
return group
def init_weight_rcnn(self, cfg, arg_params, aux_params):
arg_params['fc_new_1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_1_weight'])
arg_params['fc_new_1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_1_bias'])
arg_params['fc_new_2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_2_weight'])
arg_params['fc_new_2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_2_bias'])
arg_params['cls_score_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['cls_score_weight'])
arg_params['cls_score_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['cls_score_bias'])
arg_params['bbox_pred_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['bbox_pred_weight'])
arg_params['bbox_pred_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['bbox_pred_bias'])
def init_deformable_convnet(self, cfg, arg_params, aux_params):
arg_params['res5a_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res5a_branch2b_offset_weight'])
arg_params['res5a_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res5a_branch2b_offset_bias'])
arg_params['res5b_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res5b_branch2b_offset_weight'])
arg_params['res5b_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res5b_branch2b_offset_bias'])
arg_params['res5c_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res5c_branch2b_offset_weight'])
arg_params['res5c_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res5c_branch2b_offset_bias'])
arg_params['res3b3_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res3b3_branch2b_offset_weight'])
arg_params['res3b3_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res3b3_branch2b_offset_bias'])
arg_params['res4b22_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res4b22_branch2b_offset_weight'])
arg_params['res4b22_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res4b22_branch2b_offset_bias'])
def init_weight_fpn(self, cfg, arg_params, aux_params):
arg_params['fpn_p6_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p6_weight'])
arg_params['fpn_p6_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p6_bias'])
arg_params['fpn_p5_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p5_weight'])
arg_params['fpn_p5_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p5_bias'])
arg_params['fpn_p4_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p4_weight'])
arg_params['fpn_p4_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p4_bias'])
arg_params['fpn_p3_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p3_weight'])
arg_params['fpn_p3_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p3_bias'])
arg_params['fpn_p2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p2_weight'])
arg_params['fpn_p2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p2_bias'])
arg_params['fpn_p5_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p5_1x1_weight'])
arg_params['fpn_p5_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p5_1x1_bias'])
arg_params['fpn_p4_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p4_1x1_weight'])
arg_params['fpn_p4_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p4_1x1_bias'])
arg_params['fpn_p3_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p3_1x1_weight'])
arg_params['fpn_p3_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p3_1x1_bias'])
arg_params['fpn_p2_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p2_1x1_weight'])
arg_params['fpn_p2_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p2_1x1_bias'])
def init_weight(self, cfg, arg_params, aux_params):
(arg_params2, aux_params2) = ({}, {})
for name in self.shared_param_list:
if ('offset' in name):
arg_params2[(name + '_weight')] = mx.nd.zeros(shape=self.arg_shape_dict[(name + '_weight')])
else:
arg_params2[(name + '_weight')] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict[(name + '_weight')])
arg_params2[(name + '_bias')] = mx.nd.zeros(shape=self.arg_shape_dict[(name + '_bias')])
self.init_deformable_convnet(cfg, arg_params2, aux_params2)
self.init_weight_rcnn(cfg, arg_params2, aux_params2)
self.init_weight_fpn(cfg, arg_params2, aux_params2)
for k in arg_params2:
if ((k not in arg_params) or (arg_params[k].shape != arg_params2[k].shape)):
arg_params[k] = arg_params2[k]
for k in aux_params2:
if (k not in aux_params):
aux_params[k] = aux_params2[k]
|
class resnet_v1_101_fpn_dcn_rcnn_oneshot_v3(Symbol):
def __init__(self):
'\n Use __init__ to define parameter network needs\n '
self.shared_param_list = ['offset_p2', 'offset_p3', 'offset_p4', 'offset_p5', 'rpn_conv', 'rpn_cls_score', 'rpn_bbox_pred']
self.shared_param_dict = {}
for name in self.shared_param_list:
self.shared_param_dict[(name + '_weight')] = mx.sym.Variable((name + '_weight'))
self.shared_param_dict[(name + '_bias')] = mx.sym.Variable((name + '_bias'))
self.constants_dict = {}
def get_resnet_backbone(self, data, with_dilated=False, with_dconv=False, with_dpyramid=False, eps=1e-05):
conv1 = mx.symbol.Convolution(name='conv1', data=data, num_filter=64, pad=(3, 3), kernel=(7, 7), stride=(2, 2), no_bias=True)
bn_conv1 = mx.symbol.BatchNorm(name='bn_conv1', data=conv1, use_global_stats=True, fix_gamma=False, eps=eps)
scale_conv1 = bn_conv1
conv1_relu = mx.symbol.Activation(name='conv1_relu', data=scale_conv1, act_type='relu')
pool1 = mx.symbol.Pooling(name='pool1', data=conv1_relu, pooling_convention='full', pad=(0, 0), kernel=(3, 3), stride=(2, 2), pool_type='max')
res2a_branch1 = mx.symbol.Convolution(name='res2a_branch1', data=pool1, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2a_branch1 = mx.symbol.BatchNorm(name='bn2a_branch1', data=res2a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch1 = bn2a_branch1
res2a_branch2a = mx.symbol.Convolution(name='res2a_branch2a', data=pool1, num_filter=64, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2a_branch2a = mx.symbol.BatchNorm(name='bn2a_branch2a', data=res2a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch2a = bn2a_branch2a
res2a_branch2a_relu = mx.symbol.Activation(name='res2a_branch2a_relu', data=scale2a_branch2a, act_type='relu')
res2a_branch2b = mx.symbol.Convolution(name='res2a_branch2b', data=res2a_branch2a_relu, num_filter=64, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn2a_branch2b = mx.symbol.BatchNorm(name='bn2a_branch2b', data=res2a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch2b = bn2a_branch2b
res2a_branch2b_relu = mx.symbol.Activation(name='res2a_branch2b_relu', data=scale2a_branch2b, act_type='relu')
res2a_branch2c = mx.symbol.Convolution(name='res2a_branch2c', data=res2a_branch2b_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2a_branch2c = mx.symbol.BatchNorm(name='bn2a_branch2c', data=res2a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch2c = bn2a_branch2c
res2a = mx.symbol.broadcast_add(*[scale2a_branch1, scale2a_branch2c], name='res2a')
res2a_relu = mx.symbol.Activation(name='res2a_relu', data=res2a, act_type='relu')
res2b_branch2a = mx.symbol.Convolution(name='res2b_branch2a', data=res2a_relu, num_filter=64, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2b_branch2a = mx.symbol.BatchNorm(name='bn2b_branch2a', data=res2b_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale2b_branch2a = bn2b_branch2a
res2b_branch2a_relu = mx.symbol.Activation(name='res2b_branch2a_relu', data=scale2b_branch2a, act_type='relu')
res2b_branch2b = mx.symbol.Convolution(name='res2b_branch2b', data=res2b_branch2a_relu, num_filter=64, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn2b_branch2b = mx.symbol.BatchNorm(name='bn2b_branch2b', data=res2b_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale2b_branch2b = bn2b_branch2b
res2b_branch2b_relu = mx.symbol.Activation(name='res2b_branch2b_relu', data=scale2b_branch2b, act_type='relu')
res2b_branch2c = mx.symbol.Convolution(name='res2b_branch2c', data=res2b_branch2b_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2b_branch2c = mx.symbol.BatchNorm(name='bn2b_branch2c', data=res2b_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale2b_branch2c = bn2b_branch2c
res2b = mx.symbol.broadcast_add(*[res2a_relu, scale2b_branch2c], name='res2b')
res2b_relu = mx.symbol.Activation(name='res2b_relu', data=res2b, act_type='relu')
res2c_branch2a = mx.symbol.Convolution(name='res2c_branch2a', data=res2b_relu, num_filter=64, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2c_branch2a = mx.symbol.BatchNorm(name='bn2c_branch2a', data=res2c_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale2c_branch2a = bn2c_branch2a
res2c_branch2a_relu = mx.symbol.Activation(name='res2c_branch2a_relu', data=scale2c_branch2a, act_type='relu')
res2c_branch2b = mx.symbol.Convolution(name='res2c_branch2b', data=res2c_branch2a_relu, num_filter=64, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn2c_branch2b = mx.symbol.BatchNorm(name='bn2c_branch2b', data=res2c_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale2c_branch2b = bn2c_branch2b
res2c_branch2b_relu = mx.symbol.Activation(name='res2c_branch2b_relu', data=scale2c_branch2b, act_type='relu')
res2c_branch2c = mx.symbol.Convolution(name='res2c_branch2c', data=res2c_branch2b_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2c_branch2c = mx.symbol.BatchNorm(name='bn2c_branch2c', data=res2c_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale2c_branch2c = bn2c_branch2c
res2c = mx.symbol.broadcast_add(*[res2b_relu, scale2c_branch2c], name='res2c')
res2c_relu = mx.symbol.Activation(name='res2c_relu', data=res2c, act_type='relu')
res3a_branch1 = mx.symbol.Convolution(name='res3a_branch1', data=res2c_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn3a_branch1 = mx.symbol.BatchNorm(name='bn3a_branch1', data=res3a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch1 = bn3a_branch1
res3a_branch2a = mx.symbol.Convolution(name='res3a_branch2a', data=res2c_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn3a_branch2a = mx.symbol.BatchNorm(name='bn3a_branch2a', data=res3a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch2a = bn3a_branch2a
res3a_branch2a_relu = mx.symbol.Activation(name='res3a_branch2a_relu', data=scale3a_branch2a, act_type='relu')
res3a_branch2b = mx.symbol.Convolution(name='res3a_branch2b', data=res3a_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3a_branch2b = mx.symbol.BatchNorm(name='bn3a_branch2b', data=res3a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch2b = bn3a_branch2b
res3a_branch2b_relu = mx.symbol.Activation(name='res3a_branch2b_relu', data=scale3a_branch2b, act_type='relu')
res3a_branch2c = mx.symbol.Convolution(name='res3a_branch2c', data=res3a_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3a_branch2c = mx.symbol.BatchNorm(name='bn3a_branch2c', data=res3a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch2c = bn3a_branch2c
res3a = mx.symbol.broadcast_add(*[scale3a_branch1, scale3a_branch2c], name='res3a')
res3a_relu = mx.symbol.Activation(name='res3a_relu', data=res3a, act_type='relu')
res3b1_branch2a = mx.symbol.Convolution(name='res3b1_branch2a', data=res3a_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b1_branch2a = mx.symbol.BatchNorm(name='bn3b1_branch2a', data=res3b1_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b1_branch2a = bn3b1_branch2a
res3b1_branch2a_relu = mx.symbol.Activation(name='res3b1_branch2a_relu', data=scale3b1_branch2a, act_type='relu')
res3b1_branch2b = mx.symbol.Convolution(name='res3b1_branch2b', data=res3b1_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3b1_branch2b = mx.symbol.BatchNorm(name='bn3b1_branch2b', data=res3b1_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b1_branch2b = bn3b1_branch2b
res3b1_branch2b_relu = mx.symbol.Activation(name='res3b1_branch2b_relu', data=scale3b1_branch2b, act_type='relu')
res3b1_branch2c = mx.symbol.Convolution(name='res3b1_branch2c', data=res3b1_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b1_branch2c = mx.symbol.BatchNorm(name='bn3b1_branch2c', data=res3b1_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b1_branch2c = bn3b1_branch2c
res3b1 = mx.symbol.broadcast_add(*[res3a_relu, scale3b1_branch2c], name='res3b1')
res3b1_relu = mx.symbol.Activation(name='res3b1_relu', data=res3b1, act_type='relu')
res3b2_branch2a = mx.symbol.Convolution(name='res3b2_branch2a', data=res3b1_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b2_branch2a = mx.symbol.BatchNorm(name='bn3b2_branch2a', data=res3b2_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b2_branch2a = bn3b2_branch2a
res3b2_branch2a_relu = mx.symbol.Activation(name='res3b2_branch2a_relu', data=scale3b2_branch2a, act_type='relu')
res3b2_branch2b = mx.symbol.Convolution(name='res3b2_branch2b', data=res3b2_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3b2_branch2b = mx.symbol.BatchNorm(name='bn3b2_branch2b', data=res3b2_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b2_branch2b = bn3b2_branch2b
res3b2_branch2b_relu = mx.symbol.Activation(name='res3b2_branch2b_relu', data=scale3b2_branch2b, act_type='relu')
res3b2_branch2c = mx.symbol.Convolution(name='res3b2_branch2c', data=res3b2_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b2_branch2c = mx.symbol.BatchNorm(name='bn3b2_branch2c', data=res3b2_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b2_branch2c = bn3b2_branch2c
res3b2 = mx.symbol.broadcast_add(*[res3b1_relu, scale3b2_branch2c], name='res3b2')
res3b2_relu = mx.symbol.Activation(name='res3b2_relu', data=res3b2, act_type='relu')
res3b3_branch2a = mx.symbol.Convolution(name='res3b3_branch2a', data=res3b2_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b3_branch2a = mx.symbol.BatchNorm(name='bn3b3_branch2a', data=res3b3_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b3_branch2a = bn3b3_branch2a
res3b3_branch2a_relu = mx.symbol.Activation(name='res3b3_branch2a_relu', data=scale3b3_branch2a, act_type='relu')
if with_dpyramid:
res3b3_branch2b_offset = mx.symbol.Convolution(name='res3b3_branch2b_offset', data=res3b3_branch2a_relu, num_filter=72, pad=(1, 1), kernel=(3, 3), stride=(1, 1))
res3b3_branch2b = mx.contrib.symbol.DeformableConvolution(name='res3b3_branch2b', data=res3b3_branch2a_relu, offset=res3b3_branch2b_offset, num_filter=128, pad=(1, 1), kernel=(3, 3), num_deformable_group=4, stride=(1, 1), no_bias=True)
else:
res3b3_branch2b = mx.symbol.Convolution(name='res3b3_branch2b', data=res3b3_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3b3_branch2b = mx.symbol.BatchNorm(name='bn3b3_branch2b', data=res3b3_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b3_branch2b = bn3b3_branch2b
res3b3_branch2b_relu = mx.symbol.Activation(name='res3b3_branch2b_relu', data=scale3b3_branch2b, act_type='relu')
res3b3_branch2c = mx.symbol.Convolution(name='res3b3_branch2c', data=res3b3_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b3_branch2c = mx.symbol.BatchNorm(name='bn3b3_branch2c', data=res3b3_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b3_branch2c = bn3b3_branch2c
res3b3 = mx.symbol.broadcast_add(*[res3b2_relu, scale3b3_branch2c], name='res3b3')
res3b3_relu = mx.symbol.Activation(name='res3b3_relu', data=res3b3, act_type='relu')
res4a_branch1 = mx.symbol.Convolution(name='res4a_branch1', data=res3b3_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn4a_branch1 = mx.symbol.BatchNorm(name='bn4a_branch1', data=res4a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch1 = bn4a_branch1
res4a_branch2a = mx.symbol.Convolution(name='res4a_branch2a', data=res3b3_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn4a_branch2a = mx.symbol.BatchNorm(name='bn4a_branch2a', data=res4a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch2a = bn4a_branch2a
res4a_branch2a_relu = mx.symbol.Activation(name='res4a_branch2a_relu', data=scale4a_branch2a, act_type='relu')
res4a_branch2b = mx.symbol.Convolution(name='res4a_branch2b', data=res4a_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4a_branch2b = mx.symbol.BatchNorm(name='bn4a_branch2b', data=res4a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch2b = bn4a_branch2b
res4a_branch2b_relu = mx.symbol.Activation(name='res4a_branch2b_relu', data=scale4a_branch2b, act_type='relu')
res4a_branch2c = mx.symbol.Convolution(name='res4a_branch2c', data=res4a_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4a_branch2c = mx.symbol.BatchNorm(name='bn4a_branch2c', data=res4a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch2c = bn4a_branch2c
res4a = mx.symbol.broadcast_add(*[scale4a_branch1, scale4a_branch2c], name='res4a')
res4a_relu = mx.symbol.Activation(name='res4a_relu', data=res4a, act_type='relu')
res4b1_branch2a = mx.symbol.Convolution(name='res4b1_branch2a', data=res4a_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b1_branch2a = mx.symbol.BatchNorm(name='bn4b1_branch2a', data=res4b1_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b1_branch2a = bn4b1_branch2a
res4b1_branch2a_relu = mx.symbol.Activation(name='res4b1_branch2a_relu', data=scale4b1_branch2a, act_type='relu')
res4b1_branch2b = mx.symbol.Convolution(name='res4b1_branch2b', data=res4b1_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b1_branch2b = mx.symbol.BatchNorm(name='bn4b1_branch2b', data=res4b1_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b1_branch2b = bn4b1_branch2b
res4b1_branch2b_relu = mx.symbol.Activation(name='res4b1_branch2b_relu', data=scale4b1_branch2b, act_type='relu')
res4b1_branch2c = mx.symbol.Convolution(name='res4b1_branch2c', data=res4b1_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b1_branch2c = mx.symbol.BatchNorm(name='bn4b1_branch2c', data=res4b1_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b1_branch2c = bn4b1_branch2c
res4b1 = mx.symbol.broadcast_add(*[res4a_relu, scale4b1_branch2c], name='res4b1')
res4b1_relu = mx.symbol.Activation(name='res4b1_relu', data=res4b1, act_type='relu')
res4b2_branch2a = mx.symbol.Convolution(name='res4b2_branch2a', data=res4b1_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b2_branch2a = mx.symbol.BatchNorm(name='bn4b2_branch2a', data=res4b2_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b2_branch2a = bn4b2_branch2a
res4b2_branch2a_relu = mx.symbol.Activation(name='res4b2_branch2a_relu', data=scale4b2_branch2a, act_type='relu')
res4b2_branch2b = mx.symbol.Convolution(name='res4b2_branch2b', data=res4b2_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b2_branch2b = mx.symbol.BatchNorm(name='bn4b2_branch2b', data=res4b2_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b2_branch2b = bn4b2_branch2b
res4b2_branch2b_relu = mx.symbol.Activation(name='res4b2_branch2b_relu', data=scale4b2_branch2b, act_type='relu')
res4b2_branch2c = mx.symbol.Convolution(name='res4b2_branch2c', data=res4b2_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b2_branch2c = mx.symbol.BatchNorm(name='bn4b2_branch2c', data=res4b2_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b2_branch2c = bn4b2_branch2c
res4b2 = mx.symbol.broadcast_add(*[res4b1_relu, scale4b2_branch2c], name='res4b2')
res4b2_relu = mx.symbol.Activation(name='res4b2_relu', data=res4b2, act_type='relu')
res4b3_branch2a = mx.symbol.Convolution(name='res4b3_branch2a', data=res4b2_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b3_branch2a = mx.symbol.BatchNorm(name='bn4b3_branch2a', data=res4b3_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b3_branch2a = bn4b3_branch2a
res4b3_branch2a_relu = mx.symbol.Activation(name='res4b3_branch2a_relu', data=scale4b3_branch2a, act_type='relu')
res4b3_branch2b = mx.symbol.Convolution(name='res4b3_branch2b', data=res4b3_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b3_branch2b = mx.symbol.BatchNorm(name='bn4b3_branch2b', data=res4b3_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b3_branch2b = bn4b3_branch2b
res4b3_branch2b_relu = mx.symbol.Activation(name='res4b3_branch2b_relu', data=scale4b3_branch2b, act_type='relu')
res4b3_branch2c = mx.symbol.Convolution(name='res4b3_branch2c', data=res4b3_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b3_branch2c = mx.symbol.BatchNorm(name='bn4b3_branch2c', data=res4b3_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b3_branch2c = bn4b3_branch2c
res4b3 = mx.symbol.broadcast_add(*[res4b2_relu, scale4b3_branch2c], name='res4b3')
res4b3_relu = mx.symbol.Activation(name='res4b3_relu', data=res4b3, act_type='relu')
res4b4_branch2a = mx.symbol.Convolution(name='res4b4_branch2a', data=res4b3_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b4_branch2a = mx.symbol.BatchNorm(name='bn4b4_branch2a', data=res4b4_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b4_branch2a = bn4b4_branch2a
res4b4_branch2a_relu = mx.symbol.Activation(name='res4b4_branch2a_relu', data=scale4b4_branch2a, act_type='relu')
res4b4_branch2b = mx.symbol.Convolution(name='res4b4_branch2b', data=res4b4_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b4_branch2b = mx.symbol.BatchNorm(name='bn4b4_branch2b', data=res4b4_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b4_branch2b = bn4b4_branch2b
res4b4_branch2b_relu = mx.symbol.Activation(name='res4b4_branch2b_relu', data=scale4b4_branch2b, act_type='relu')
res4b4_branch2c = mx.symbol.Convolution(name='res4b4_branch2c', data=res4b4_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b4_branch2c = mx.symbol.BatchNorm(name='bn4b4_branch2c', data=res4b4_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b4_branch2c = bn4b4_branch2c
res4b4 = mx.symbol.broadcast_add(*[res4b3_relu, scale4b4_branch2c], name='res4b4')
res4b4_relu = mx.symbol.Activation(name='res4b4_relu', data=res4b4, act_type='relu')
res4b5_branch2a = mx.symbol.Convolution(name='res4b5_branch2a', data=res4b4_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b5_branch2a = mx.symbol.BatchNorm(name='bn4b5_branch2a', data=res4b5_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b5_branch2a = bn4b5_branch2a
res4b5_branch2a_relu = mx.symbol.Activation(name='res4b5_branch2a_relu', data=scale4b5_branch2a, act_type='relu')
res4b5_branch2b = mx.symbol.Convolution(name='res4b5_branch2b', data=res4b5_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b5_branch2b = mx.symbol.BatchNorm(name='bn4b5_branch2b', data=res4b5_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b5_branch2b = bn4b5_branch2b
res4b5_branch2b_relu = mx.symbol.Activation(name='res4b5_branch2b_relu', data=scale4b5_branch2b, act_type='relu')
res4b5_branch2c = mx.symbol.Convolution(name='res4b5_branch2c', data=res4b5_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b5_branch2c = mx.symbol.BatchNorm(name='bn4b5_branch2c', data=res4b5_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b5_branch2c = bn4b5_branch2c
res4b5 = mx.symbol.broadcast_add(*[res4b4_relu, scale4b5_branch2c], name='res4b5')
res4b5_relu = mx.symbol.Activation(name='res4b5_relu', data=res4b5, act_type='relu')
res4b6_branch2a = mx.symbol.Convolution(name='res4b6_branch2a', data=res4b5_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b6_branch2a = mx.symbol.BatchNorm(name='bn4b6_branch2a', data=res4b6_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b6_branch2a = bn4b6_branch2a
res4b6_branch2a_relu = mx.symbol.Activation(name='res4b6_branch2a_relu', data=scale4b6_branch2a, act_type='relu')
res4b6_branch2b = mx.symbol.Convolution(name='res4b6_branch2b', data=res4b6_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b6_branch2b = mx.symbol.BatchNorm(name='bn4b6_branch2b', data=res4b6_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b6_branch2b = bn4b6_branch2b
res4b6_branch2b_relu = mx.symbol.Activation(name='res4b6_branch2b_relu', data=scale4b6_branch2b, act_type='relu')
res4b6_branch2c = mx.symbol.Convolution(name='res4b6_branch2c', data=res4b6_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b6_branch2c = mx.symbol.BatchNorm(name='bn4b6_branch2c', data=res4b6_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b6_branch2c = bn4b6_branch2c
res4b6 = mx.symbol.broadcast_add(*[res4b5_relu, scale4b6_branch2c], name='res4b6')
res4b6_relu = mx.symbol.Activation(name='res4b6_relu', data=res4b6, act_type='relu')
res4b7_branch2a = mx.symbol.Convolution(name='res4b7_branch2a', data=res4b6_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b7_branch2a = mx.symbol.BatchNorm(name='bn4b7_branch2a', data=res4b7_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b7_branch2a = bn4b7_branch2a
res4b7_branch2a_relu = mx.symbol.Activation(name='res4b7_branch2a_relu', data=scale4b7_branch2a, act_type='relu')
res4b7_branch2b = mx.symbol.Convolution(name='res4b7_branch2b', data=res4b7_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b7_branch2b = mx.symbol.BatchNorm(name='bn4b7_branch2b', data=res4b7_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b7_branch2b = bn4b7_branch2b
res4b7_branch2b_relu = mx.symbol.Activation(name='res4b7_branch2b_relu', data=scale4b7_branch2b, act_type='relu')
res4b7_branch2c = mx.symbol.Convolution(name='res4b7_branch2c', data=res4b7_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b7_branch2c = mx.symbol.BatchNorm(name='bn4b7_branch2c', data=res4b7_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b7_branch2c = bn4b7_branch2c
res4b7 = mx.symbol.broadcast_add(*[res4b6_relu, scale4b7_branch2c], name='res4b7')
res4b7_relu = mx.symbol.Activation(name='res4b7_relu', data=res4b7, act_type='relu')
res4b8_branch2a = mx.symbol.Convolution(name='res4b8_branch2a', data=res4b7_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b8_branch2a = mx.symbol.BatchNorm(name='bn4b8_branch2a', data=res4b8_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b8_branch2a = bn4b8_branch2a
res4b8_branch2a_relu = mx.symbol.Activation(name='res4b8_branch2a_relu', data=scale4b8_branch2a, act_type='relu')
res4b8_branch2b = mx.symbol.Convolution(name='res4b8_branch2b', data=res4b8_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b8_branch2b = mx.symbol.BatchNorm(name='bn4b8_branch2b', data=res4b8_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b8_branch2b = bn4b8_branch2b
res4b8_branch2b_relu = mx.symbol.Activation(name='res4b8_branch2b_relu', data=scale4b8_branch2b, act_type='relu')
res4b8_branch2c = mx.symbol.Convolution(name='res4b8_branch2c', data=res4b8_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b8_branch2c = mx.symbol.BatchNorm(name='bn4b8_branch2c', data=res4b8_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b8_branch2c = bn4b8_branch2c
res4b8 = mx.symbol.broadcast_add(*[res4b7_relu, scale4b8_branch2c], name='res4b8')
res4b8_relu = mx.symbol.Activation(name='res4b8_relu', data=res4b8, act_type='relu')
res4b9_branch2a = mx.symbol.Convolution(name='res4b9_branch2a', data=res4b8_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b9_branch2a = mx.symbol.BatchNorm(name='bn4b9_branch2a', data=res4b9_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b9_branch2a = bn4b9_branch2a
res4b9_branch2a_relu = mx.symbol.Activation(name='res4b9_branch2a_relu', data=scale4b9_branch2a, act_type='relu')
res4b9_branch2b = mx.symbol.Convolution(name='res4b9_branch2b', data=res4b9_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b9_branch2b = mx.symbol.BatchNorm(name='bn4b9_branch2b', data=res4b9_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b9_branch2b = bn4b9_branch2b
res4b9_branch2b_relu = mx.symbol.Activation(name='res4b9_branch2b_relu', data=scale4b9_branch2b, act_type='relu')
res4b9_branch2c = mx.symbol.Convolution(name='res4b9_branch2c', data=res4b9_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b9_branch2c = mx.symbol.BatchNorm(name='bn4b9_branch2c', data=res4b9_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b9_branch2c = bn4b9_branch2c
res4b9 = mx.symbol.broadcast_add(*[res4b8_relu, scale4b9_branch2c], name='res4b9')
res4b9_relu = mx.symbol.Activation(name='res4b9_relu', data=res4b9, act_type='relu')
res4b10_branch2a = mx.symbol.Convolution(name='res4b10_branch2a', data=res4b9_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b10_branch2a = mx.symbol.BatchNorm(name='bn4b10_branch2a', data=res4b10_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b10_branch2a = bn4b10_branch2a
res4b10_branch2a_relu = mx.symbol.Activation(name='res4b10_branch2a_relu', data=scale4b10_branch2a, act_type='relu')
res4b10_branch2b = mx.symbol.Convolution(name='res4b10_branch2b', data=res4b10_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b10_branch2b = mx.symbol.BatchNorm(name='bn4b10_branch2b', data=res4b10_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b10_branch2b = bn4b10_branch2b
res4b10_branch2b_relu = mx.symbol.Activation(name='res4b10_branch2b_relu', data=scale4b10_branch2b, act_type='relu')
res4b10_branch2c = mx.symbol.Convolution(name='res4b10_branch2c', data=res4b10_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b10_branch2c = mx.symbol.BatchNorm(name='bn4b10_branch2c', data=res4b10_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b10_branch2c = bn4b10_branch2c
res4b10 = mx.symbol.broadcast_add(*[res4b9_relu, scale4b10_branch2c], name='res4b10')
res4b10_relu = mx.symbol.Activation(name='res4b10_relu', data=res4b10, act_type='relu')
res4b11_branch2a = mx.symbol.Convolution(name='res4b11_branch2a', data=res4b10_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b11_branch2a = mx.symbol.BatchNorm(name='bn4b11_branch2a', data=res4b11_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b11_branch2a = bn4b11_branch2a
res4b11_branch2a_relu = mx.symbol.Activation(name='res4b11_branch2a_relu', data=scale4b11_branch2a, act_type='relu')
res4b11_branch2b = mx.symbol.Convolution(name='res4b11_branch2b', data=res4b11_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b11_branch2b = mx.symbol.BatchNorm(name='bn4b11_branch2b', data=res4b11_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b11_branch2b = bn4b11_branch2b
res4b11_branch2b_relu = mx.symbol.Activation(name='res4b11_branch2b_relu', data=scale4b11_branch2b, act_type='relu')
res4b11_branch2c = mx.symbol.Convolution(name='res4b11_branch2c', data=res4b11_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b11_branch2c = mx.symbol.BatchNorm(name='bn4b11_branch2c', data=res4b11_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b11_branch2c = bn4b11_branch2c
res4b11 = mx.symbol.broadcast_add(*[res4b10_relu, scale4b11_branch2c], name='res4b11')
res4b11_relu = mx.symbol.Activation(name='res4b11_relu', data=res4b11, act_type='relu')
res4b12_branch2a = mx.symbol.Convolution(name='res4b12_branch2a', data=res4b11_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b12_branch2a = mx.symbol.BatchNorm(name='bn4b12_branch2a', data=res4b12_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b12_branch2a = bn4b12_branch2a
res4b12_branch2a_relu = mx.symbol.Activation(name='res4b12_branch2a_relu', data=scale4b12_branch2a, act_type='relu')
res4b12_branch2b = mx.symbol.Convolution(name='res4b12_branch2b', data=res4b12_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b12_branch2b = mx.symbol.BatchNorm(name='bn4b12_branch2b', data=res4b12_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b12_branch2b = bn4b12_branch2b
res4b12_branch2b_relu = mx.symbol.Activation(name='res4b12_branch2b_relu', data=scale4b12_branch2b, act_type='relu')
res4b12_branch2c = mx.symbol.Convolution(name='res4b12_branch2c', data=res4b12_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b12_branch2c = mx.symbol.BatchNorm(name='bn4b12_branch2c', data=res4b12_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b12_branch2c = bn4b12_branch2c
res4b12 = mx.symbol.broadcast_add(*[res4b11_relu, scale4b12_branch2c], name='res4b12')
res4b12_relu = mx.symbol.Activation(name='res4b12_relu', data=res4b12, act_type='relu')
res4b13_branch2a = mx.symbol.Convolution(name='res4b13_branch2a', data=res4b12_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b13_branch2a = mx.symbol.BatchNorm(name='bn4b13_branch2a', data=res4b13_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b13_branch2a = bn4b13_branch2a
res4b13_branch2a_relu = mx.symbol.Activation(name='res4b13_branch2a_relu', data=scale4b13_branch2a, act_type='relu')
res4b13_branch2b = mx.symbol.Convolution(name='res4b13_branch2b', data=res4b13_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b13_branch2b = mx.symbol.BatchNorm(name='bn4b13_branch2b', data=res4b13_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b13_branch2b = bn4b13_branch2b
res4b13_branch2b_relu = mx.symbol.Activation(name='res4b13_branch2b_relu', data=scale4b13_branch2b, act_type='relu')
res4b13_branch2c = mx.symbol.Convolution(name='res4b13_branch2c', data=res4b13_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b13_branch2c = mx.symbol.BatchNorm(name='bn4b13_branch2c', data=res4b13_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b13_branch2c = bn4b13_branch2c
res4b13 = mx.symbol.broadcast_add(*[res4b12_relu, scale4b13_branch2c], name='res4b13')
res4b13_relu = mx.symbol.Activation(name='res4b13_relu', data=res4b13, act_type='relu')
res4b14_branch2a = mx.symbol.Convolution(name='res4b14_branch2a', data=res4b13_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b14_branch2a = mx.symbol.BatchNorm(name='bn4b14_branch2a', data=res4b14_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b14_branch2a = bn4b14_branch2a
res4b14_branch2a_relu = mx.symbol.Activation(name='res4b14_branch2a_relu', data=scale4b14_branch2a, act_type='relu')
res4b14_branch2b = mx.symbol.Convolution(name='res4b14_branch2b', data=res4b14_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b14_branch2b = mx.symbol.BatchNorm(name='bn4b14_branch2b', data=res4b14_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b14_branch2b = bn4b14_branch2b
res4b14_branch2b_relu = mx.symbol.Activation(name='res4b14_branch2b_relu', data=scale4b14_branch2b, act_type='relu')
res4b14_branch2c = mx.symbol.Convolution(name='res4b14_branch2c', data=res4b14_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b14_branch2c = mx.symbol.BatchNorm(name='bn4b14_branch2c', data=res4b14_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b14_branch2c = bn4b14_branch2c
res4b14 = mx.symbol.broadcast_add(*[res4b13_relu, scale4b14_branch2c], name='res4b14')
res4b14_relu = mx.symbol.Activation(name='res4b14_relu', data=res4b14, act_type='relu')
res4b15_branch2a = mx.symbol.Convolution(name='res4b15_branch2a', data=res4b14_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b15_branch2a = mx.symbol.BatchNorm(name='bn4b15_branch2a', data=res4b15_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b15_branch2a = bn4b15_branch2a
res4b15_branch2a_relu = mx.symbol.Activation(name='res4b15_branch2a_relu', data=scale4b15_branch2a, act_type='relu')
res4b15_branch2b = mx.symbol.Convolution(name='res4b15_branch2b', data=res4b15_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b15_branch2b = mx.symbol.BatchNorm(name='bn4b15_branch2b', data=res4b15_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b15_branch2b = bn4b15_branch2b
res4b15_branch2b_relu = mx.symbol.Activation(name='res4b15_branch2b_relu', data=scale4b15_branch2b, act_type='relu')
res4b15_branch2c = mx.symbol.Convolution(name='res4b15_branch2c', data=res4b15_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b15_branch2c = mx.symbol.BatchNorm(name='bn4b15_branch2c', data=res4b15_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b15_branch2c = bn4b15_branch2c
res4b15 = mx.symbol.broadcast_add(*[res4b14_relu, scale4b15_branch2c], name='res4b15')
res4b15_relu = mx.symbol.Activation(name='res4b15_relu', data=res4b15, act_type='relu')
res4b16_branch2a = mx.symbol.Convolution(name='res4b16_branch2a', data=res4b15_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b16_branch2a = mx.symbol.BatchNorm(name='bn4b16_branch2a', data=res4b16_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b16_branch2a = bn4b16_branch2a
res4b16_branch2a_relu = mx.symbol.Activation(name='res4b16_branch2a_relu', data=scale4b16_branch2a, act_type='relu')
res4b16_branch2b = mx.symbol.Convolution(name='res4b16_branch2b', data=res4b16_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b16_branch2b = mx.symbol.BatchNorm(name='bn4b16_branch2b', data=res4b16_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b16_branch2b = bn4b16_branch2b
res4b16_branch2b_relu = mx.symbol.Activation(name='res4b16_branch2b_relu', data=scale4b16_branch2b, act_type='relu')
res4b16_branch2c = mx.symbol.Convolution(name='res4b16_branch2c', data=res4b16_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b16_branch2c = mx.symbol.BatchNorm(name='bn4b16_branch2c', data=res4b16_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b16_branch2c = bn4b16_branch2c
res4b16 = mx.symbol.broadcast_add(*[res4b15_relu, scale4b16_branch2c], name='res4b16')
res4b16_relu = mx.symbol.Activation(name='res4b16_relu', data=res4b16, act_type='relu')
res4b17_branch2a = mx.symbol.Convolution(name='res4b17_branch2a', data=res4b16_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b17_branch2a = mx.symbol.BatchNorm(name='bn4b17_branch2a', data=res4b17_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b17_branch2a = bn4b17_branch2a
res4b17_branch2a_relu = mx.symbol.Activation(name='res4b17_branch2a_relu', data=scale4b17_branch2a, act_type='relu')
res4b17_branch2b = mx.symbol.Convolution(name='res4b17_branch2b', data=res4b17_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b17_branch2b = mx.symbol.BatchNorm(name='bn4b17_branch2b', data=res4b17_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b17_branch2b = bn4b17_branch2b
res4b17_branch2b_relu = mx.symbol.Activation(name='res4b17_branch2b_relu', data=scale4b17_branch2b, act_type='relu')
res4b17_branch2c = mx.symbol.Convolution(name='res4b17_branch2c', data=res4b17_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b17_branch2c = mx.symbol.BatchNorm(name='bn4b17_branch2c', data=res4b17_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b17_branch2c = bn4b17_branch2c
res4b17 = mx.symbol.broadcast_add(*[res4b16_relu, scale4b17_branch2c], name='res4b17')
res4b17_relu = mx.symbol.Activation(name='res4b17_relu', data=res4b17, act_type='relu')
res4b18_branch2a = mx.symbol.Convolution(name='res4b18_branch2a', data=res4b17_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b18_branch2a = mx.symbol.BatchNorm(name='bn4b18_branch2a', data=res4b18_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b18_branch2a = bn4b18_branch2a
res4b18_branch2a_relu = mx.symbol.Activation(name='res4b18_branch2a_relu', data=scale4b18_branch2a, act_type='relu')
res4b18_branch2b = mx.symbol.Convolution(name='res4b18_branch2b', data=res4b18_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b18_branch2b = mx.symbol.BatchNorm(name='bn4b18_branch2b', data=res4b18_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b18_branch2b = bn4b18_branch2b
res4b18_branch2b_relu = mx.symbol.Activation(name='res4b18_branch2b_relu', data=scale4b18_branch2b, act_type='relu')
res4b18_branch2c = mx.symbol.Convolution(name='res4b18_branch2c', data=res4b18_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b18_branch2c = mx.symbol.BatchNorm(name='bn4b18_branch2c', data=res4b18_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b18_branch2c = bn4b18_branch2c
res4b18 = mx.symbol.broadcast_add(*[res4b17_relu, scale4b18_branch2c], name='res4b18')
res4b18_relu = mx.symbol.Activation(name='res4b18_relu', data=res4b18, act_type='relu')
res4b19_branch2a = mx.symbol.Convolution(name='res4b19_branch2a', data=res4b18_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b19_branch2a = mx.symbol.BatchNorm(name='bn4b19_branch2a', data=res4b19_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b19_branch2a = bn4b19_branch2a
res4b19_branch2a_relu = mx.symbol.Activation(name='res4b19_branch2a_relu', data=scale4b19_branch2a, act_type='relu')
res4b19_branch2b = mx.symbol.Convolution(name='res4b19_branch2b', data=res4b19_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b19_branch2b = mx.symbol.BatchNorm(name='bn4b19_branch2b', data=res4b19_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b19_branch2b = bn4b19_branch2b
res4b19_branch2b_relu = mx.symbol.Activation(name='res4b19_branch2b_relu', data=scale4b19_branch2b, act_type='relu')
res4b19_branch2c = mx.symbol.Convolution(name='res4b19_branch2c', data=res4b19_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b19_branch2c = mx.symbol.BatchNorm(name='bn4b19_branch2c', data=res4b19_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b19_branch2c = bn4b19_branch2c
res4b19 = mx.symbol.broadcast_add(*[res4b18_relu, scale4b19_branch2c], name='res4b19')
res4b19_relu = mx.symbol.Activation(name='res4b19_relu', data=res4b19, act_type='relu')
res4b20_branch2a = mx.symbol.Convolution(name='res4b20_branch2a', data=res4b19_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b20_branch2a = mx.symbol.BatchNorm(name='bn4b20_branch2a', data=res4b20_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b20_branch2a = bn4b20_branch2a
res4b20_branch2a_relu = mx.symbol.Activation(name='res4b20_branch2a_relu', data=scale4b20_branch2a, act_type='relu')
res4b20_branch2b = mx.symbol.Convolution(name='res4b20_branch2b', data=res4b20_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b20_branch2b = mx.symbol.BatchNorm(name='bn4b20_branch2b', data=res4b20_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b20_branch2b = bn4b20_branch2b
res4b20_branch2b_relu = mx.symbol.Activation(name='res4b20_branch2b_relu', data=scale4b20_branch2b, act_type='relu')
res4b20_branch2c = mx.symbol.Convolution(name='res4b20_branch2c', data=res4b20_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b20_branch2c = mx.symbol.BatchNorm(name='bn4b20_branch2c', data=res4b20_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b20_branch2c = bn4b20_branch2c
res4b20 = mx.symbol.broadcast_add(*[res4b19_relu, scale4b20_branch2c], name='res4b20')
res4b20_relu = mx.symbol.Activation(name='res4b20_relu', data=res4b20, act_type='relu')
res4b21_branch2a = mx.symbol.Convolution(name='res4b21_branch2a', data=res4b20_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b21_branch2a = mx.symbol.BatchNorm(name='bn4b21_branch2a', data=res4b21_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b21_branch2a = bn4b21_branch2a
res4b21_branch2a_relu = mx.symbol.Activation(name='res4b21_branch2a_relu', data=scale4b21_branch2a, act_type='relu')
res4b21_branch2b = mx.symbol.Convolution(name='res4b21_branch2b', data=res4b21_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b21_branch2b = mx.symbol.BatchNorm(name='bn4b21_branch2b', data=res4b21_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b21_branch2b = bn4b21_branch2b
res4b21_branch2b_relu = mx.symbol.Activation(name='res4b21_branch2b_relu', data=scale4b21_branch2b, act_type='relu')
res4b21_branch2c = mx.symbol.Convolution(name='res4b21_branch2c', data=res4b21_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b21_branch2c = mx.symbol.BatchNorm(name='bn4b21_branch2c', data=res4b21_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b21_branch2c = bn4b21_branch2c
res4b21 = mx.symbol.broadcast_add(*[res4b20_relu, scale4b21_branch2c], name='res4b21')
res4b21_relu = mx.symbol.Activation(name='res4b21_relu', data=res4b21, act_type='relu')
res4b22_branch2a = mx.symbol.Convolution(name='res4b22_branch2a', data=res4b21_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b22_branch2a = mx.symbol.BatchNorm(name='bn4b22_branch2a', data=res4b22_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b22_branch2a = bn4b22_branch2a
res4b22_branch2a_relu = mx.symbol.Activation(name='res4b22_branch2a_relu', data=scale4b22_branch2a, act_type='relu')
if with_dpyramid:
res4b22_branch2b_offset = mx.symbol.Convolution(name='res4b22_branch2b_offset', data=res4b22_branch2a_relu, num_filter=72, pad=(1, 1), kernel=(3, 3), stride=(1, 1))
res4b22_branch2b = mx.contrib.symbol.DeformableConvolution(name='res4b22_branch2b', data=res4b22_branch2a_relu, offset=res4b22_branch2b_offset, num_filter=256, pad=(1, 1), kernel=(3, 3), num_deformable_group=4, stride=(1, 1), no_bias=True)
else:
res4b22_branch2b = mx.symbol.Convolution(name='res4b22_branch2b', data=res4b22_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b22_branch2b = mx.symbol.BatchNorm(name='bn4b22_branch2b', data=res4b22_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b22_branch2b = bn4b22_branch2b
res4b22_branch2b_relu = mx.symbol.Activation(name='res4b22_branch2b_relu', data=scale4b22_branch2b, act_type='relu')
res4b22_branch2c = mx.symbol.Convolution(name='res4b22_branch2c', data=res4b22_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b22_branch2c = mx.symbol.BatchNorm(name='bn4b22_branch2c', data=res4b22_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b22_branch2c = bn4b22_branch2c
res4b22 = mx.symbol.broadcast_add(*[res4b21_relu, scale4b22_branch2c], name='res4b22')
res4b22_relu = mx.symbol.Activation(name='res4b22_relu', data=res4b22, act_type='relu')
if with_dilated:
res5_stride = (1, 1)
res5_dilate = (2, 2)
else:
res5_stride = (2, 2)
res5_dilate = (1, 1)
res5a_branch2a = mx.symbol.Convolution(name='res5a_branch2a', data=res4b22_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=res5_stride, no_bias=True)
bn5a_branch2a = mx.symbol.BatchNorm(name='bn5a_branch2a', data=res5a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch2a = bn5a_branch2a
res5a_branch2a_relu = mx.symbol.Activation(name='res5a_branch2a_relu', data=scale5a_branch2a, act_type='relu')
if with_dconv:
res5a_branch2b_offset = mx.symbol.Convolution(name='res5a_branch2b_offset', data=res5a_branch2a_relu, num_filter=72, pad=res5_dilate, kernel=(3, 3), dilate=res5_dilate)
res5a_branch2b = mx.contrib.symbol.DeformableConvolution(name='res5a_branch2b', data=res5a_branch2a_relu, offset=res5a_branch2b_offset, num_filter=512, pad=res5_dilate, kernel=(3, 3), num_deformable_group=4, stride=(1, 1), dilate=res5_dilate, no_bias=True)
else:
res5a_branch2b = mx.symbol.Convolution(name='res5a_branch2b', data=res5a_branch2a_relu, num_filter=512, pad=res5_dilate, kernel=(3, 3), stride=(1, 1), dilate=res5_dilate, no_bias=True)
bn5a_branch2b = mx.symbol.BatchNorm(name='bn5a_branch2b', data=res5a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch2b = bn5a_branch2b
res5a_branch2b_relu = mx.symbol.Activation(name='res5a_branch2b_relu', data=scale5a_branch2b, act_type='relu')
res5a_branch2c = mx.symbol.Convolution(name='res5a_branch2c', data=res5a_branch2b_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5a_branch2c = mx.symbol.BatchNorm(name='bn5a_branch2c', data=res5a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch2c = bn5a_branch2c
res5a_branch1 = mx.symbol.Convolution(name='res5a_branch1', data=res4b22_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=res5_stride, no_bias=True)
bn5a_branch1 = mx.symbol.BatchNorm(name='bn5a_branch1', data=res5a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch1 = bn5a_branch1
res5a = mx.symbol.broadcast_add(*[scale5a_branch1, scale5a_branch2c], name='res5a')
res5a_relu = mx.symbol.Activation(name='res5a_relu', data=res5a, act_type='relu')
res5b_branch2a = mx.symbol.Convolution(name='res5b_branch2a', data=res5a_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5b_branch2a = mx.symbol.BatchNorm(name='bn5b_branch2a', data=res5b_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale5b_branch2a = bn5b_branch2a
res5b_branch2a_relu = mx.symbol.Activation(name='res5b_branch2a_relu', data=scale5b_branch2a, act_type='relu')
if with_dconv:
res5b_branch2b_offset = mx.symbol.Convolution(name='res5b_branch2b_offset', data=res5b_branch2a_relu, num_filter=72, pad=res5_dilate, kernel=(3, 3), dilate=res5_dilate)
res5b_branch2b = mx.contrib.symbol.DeformableConvolution(name='res5b_branch2b', data=res5b_branch2a_relu, offset=res5b_branch2b_offset, num_filter=512, pad=res5_dilate, kernel=(3, 3), num_deformable_group=4, dilate=res5_dilate, no_bias=True)
else:
res5b_branch2b = mx.symbol.Convolution(name='res5b_branch2b', data=res5b_branch2a_relu, num_filter=512, pad=res5_dilate, kernel=(3, 3), stride=(1, 1), dilate=res5_dilate, no_bias=True)
bn5b_branch2b = mx.symbol.BatchNorm(name='bn5b_branch2b', data=res5b_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale5b_branch2b = bn5b_branch2b
res5b_branch2b_relu = mx.symbol.Activation(name='res5b_branch2b_relu', data=scale5b_branch2b, act_type='relu')
res5b_branch2c = mx.symbol.Convolution(name='res5b_branch2c', data=res5b_branch2b_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5b_branch2c = mx.symbol.BatchNorm(name='bn5b_branch2c', data=res5b_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale5b_branch2c = bn5b_branch2c
res5b = mx.symbol.broadcast_add(*[res5a_relu, scale5b_branch2c], name='res5b')
res5b_relu = mx.symbol.Activation(name='res5b_relu', data=res5b, act_type='relu')
res5c_branch2a = mx.symbol.Convolution(name='res5c_branch2a', data=res5b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5c_branch2a = mx.symbol.BatchNorm(name='bn5c_branch2a', data=res5c_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale5c_branch2a = bn5c_branch2a
res5c_branch2a_relu = mx.symbol.Activation(name='res5c_branch2a_relu', data=scale5c_branch2a, act_type='relu')
if with_dconv:
res5c_branch2b_offset = mx.symbol.Convolution(name='res5c_branch2b_offset', data=res5c_branch2a_relu, num_filter=72, pad=res5_dilate, kernel=(3, 3), dilate=res5_dilate)
res5c_branch2b = mx.contrib.symbol.DeformableConvolution(name='res5c_branch2b', data=res5c_branch2a_relu, offset=res5c_branch2b_offset, num_filter=512, pad=res5_dilate, kernel=(3, 3), num_deformable_group=4, dilate=res5_dilate, no_bias=True)
else:
res5c_branch2b = mx.symbol.Convolution(name='res5c_branch2b', data=res5c_branch2a_relu, num_filter=512, pad=res5_dilate, kernel=(3, 3), stride=(1, 1), dilate=res5_dilate, no_bias=True)
bn5c_branch2b = mx.symbol.BatchNorm(name='bn5c_branch2b', data=res5c_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale5c_branch2b = bn5c_branch2b
res5c_branch2b_relu = mx.symbol.Activation(name='res5c_branch2b_relu', data=scale5c_branch2b, act_type='relu')
res5c_branch2c = mx.symbol.Convolution(name='res5c_branch2c', data=res5c_branch2b_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5c_branch2c = mx.symbol.BatchNorm(name='bn5c_branch2c', data=res5c_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale5c_branch2c = bn5c_branch2c
res5c = mx.symbol.broadcast_add(*[res5b_relu, scale5c_branch2c], name='res5c')
res5c_relu = mx.symbol.Activation(name='res5c_relu', data=res5c, act_type='relu')
return (res2c_relu, res3b3_relu, res4b22_relu, res5c_relu)
def get_fpn_feature(self, c2, c3, c4, c5, feature_dim=256):
fpn_p5_1x1 = mx.symbol.Convolution(data=c5, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p5_1x1')
fpn_p4_1x1 = mx.symbol.Convolution(data=c4, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p4_1x1')
fpn_p3_1x1 = mx.symbol.Convolution(data=c3, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p3_1x1')
fpn_p2_1x1 = mx.symbol.Convolution(data=c2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p2_1x1')
fpn_p5_upsample = mx.symbol.UpSampling(fpn_p5_1x1, scale=2, sample_type='nearest', name='fpn_p5_upsample')
fpn_p4_plus = mx.sym.ElementWiseSum(*[fpn_p5_upsample, fpn_p4_1x1], name='fpn_p4_sum')
fpn_p4_upsample = mx.symbol.UpSampling(fpn_p4_plus, scale=2, sample_type='nearest', name='fpn_p4_upsample')
fpn_p3_plus = mx.sym.ElementWiseSum(*[fpn_p4_upsample, fpn_p3_1x1], name='fpn_p3_sum')
fpn_p3_upsample = mx.symbol.UpSampling(fpn_p3_plus, scale=2, sample_type='nearest', name='fpn_p3_upsample')
fpn_p2_plus = mx.sym.ElementWiseSum(*[fpn_p3_upsample, fpn_p2_1x1], name='fpn_p2_sum')
fpn_p6 = mx.sym.Convolution(data=c5, kernel=(3, 3), pad=(1, 1), stride=(2, 2), num_filter=feature_dim, name='fpn_p6')
fpn_p5 = mx.symbol.Convolution(data=fpn_p5_1x1, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p5')
fpn_p4 = mx.symbol.Convolution(data=fpn_p4_plus, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p4')
fpn_p3 = mx.symbol.Convolution(data=fpn_p3_plus, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p3')
fpn_p2 = mx.symbol.Convolution(data=fpn_p2_plus, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p2')
return (fpn_p2, fpn_p3, fpn_p4, fpn_p5, fpn_p6)
def get_rpn_subnet(self, data, num_anchors, suffix):
rpn_conv = mx.sym.Convolution(data=data, kernel=(3, 3), pad=(1, 1), num_filter=512, name=('rpn_conv_' + suffix), weight=self.shared_param_dict['rpn_conv_weight'], bias=self.shared_param_dict['rpn_conv_bias'])
rpn_relu = mx.sym.Activation(data=rpn_conv, act_type='relu', name=('rpn_relu_' + suffix))
rpn_cls_score = mx.sym.Convolution(data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=(2 * num_anchors), name=('rpn_cls_score_' + suffix), weight=self.shared_param_dict['rpn_cls_score_weight'], bias=self.shared_param_dict['rpn_cls_score_bias'])
rpn_bbox_pred = mx.sym.Convolution(data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=(4 * num_anchors), name=('rpn_bbox_pred_' + suffix), weight=self.shared_param_dict['rpn_bbox_pred_weight'], bias=self.shared_param_dict['rpn_bbox_pred_bias'])
rpn_cls_score_t1 = mx.sym.Reshape(data=rpn_cls_score, shape=(0, 2, (- 1), 0), name=('rpn_cls_score_t1_' + suffix))
rpn_cls_score_t2 = mx.sym.Reshape(data=rpn_cls_score_t1, shape=(0, 2, (- 1)), name=('rpn_cls_score_t2_' + suffix))
rpn_cls_prob = mx.sym.SoftmaxActivation(data=rpn_cls_score_t1, mode='channel', name=('rpn_cls_prob_' + suffix))
rpn_cls_prob_t = mx.sym.Reshape(data=rpn_cls_prob, shape=(0, (2 * num_anchors), (- 1), 0), name=('rpn_cls_prob_t_' + suffix))
rpn_bbox_pred_t = mx.sym.Reshape(data=rpn_bbox_pred, shape=(0, 0, (- 1)), name=('rpn_bbox_pred_t_' + suffix))
return (rpn_cls_score_t2, rpn_cls_prob_t, rpn_bbox_pred_t, rpn_bbox_pred)
def get_deformable_roipooling(self, name, data, rois, output_dim, spatial_scale, param_name, group_size=1, pooled_size=7, sample_per_part=4, part_size=7):
offset = mx.contrib.sym.DeformablePSROIPooling(name=(('offset_' + name) + '_t'), data=data, rois=rois, group_size=group_size, pooled_size=pooled_size, sample_per_part=sample_per_part, no_trans=True, part_size=part_size, output_dim=output_dim, spatial_scale=spatial_scale)
offset = mx.sym.FullyConnected(name=('offset_' + name), data=offset, num_hidden=((part_size * part_size) * 2), lr_mult=0.01, weight=self.shared_param_dict[(('offset_' + param_name) + '_weight')], bias=self.shared_param_dict[(('offset_' + param_name) + '_bias')])
offset_reshape = mx.sym.Reshape(data=offset, shape=((- 1), 2, part_size, part_size), name=('offset_reshape_' + name))
output = mx.contrib.sym.DeformablePSROIPooling(name=('deformable_roi_pool_' + name), data=data, rois=rois, trans=offset_reshape, group_size=group_size, pooled_size=pooled_size, sample_per_part=sample_per_part, no_trans=False, part_size=part_size, output_dim=output_dim, spatial_scale=spatial_scale, trans_std=0.1)
return output
def get_constant_symbol(self, const_val):
if (const_val in self.constants_dict):
return self.constants_dict[const_val]
name = 'const_eq_{0}'.format(const_val)
c = mx.sym.Variable(name, shape=(1,), init=MyConstant(value=[const_val]))
c = mx.sym.BlockGrad(c)
self.constants_dict[const_val] = c
return c
def cos_sim_2_dist_generic(self, cos_sim, x=None, y=None, x_is_norm=True, y_is_norm=True):
if x_is_norm:
x_norm = self.get_constant_symbol(1)
else:
assert (x is not None), 'if x is not L2 normalized then x must be provided'
x_norm = mx.sym.sum_axis(mx.sym.square(x), axis=0, keepdims=True)
x_norm = mx.sym.transpose(x_norm, axes=(1, 0))
if y_is_norm:
y_norm = self.get_constant_symbol(1)
else:
assert (y is not None), 'if y is not L2 normalized then y must be provided'
y_norm = mx.sym.sum_axis(mx.sym.square(y), axis=0, keepdims=True)
dist = mx.sym.broadcast_add(mx.sym.broadcast_sub(x_norm, mx.sym.broadcast_mul(self.get_constant_symbol(2), cos_sim)), y_norm)
return dist
def cos_sim_2_dist(self, cos_sim, cfg=None, embd=None, reps=None):
if cfg.network.EMBED_L2_NORM:
embd_norm = self.get_constant_symbol(1)
else:
assert (embd is not None), 'if embedding is not L2 normalized then embd must be provided'
embd_norm = mx.sym.sum_axis(mx.sym.square(embd), axis=1, keepdims=True)
embd_norm = mx.sym.reshape(embd_norm, shape=(0, 1, 1))
if cfg.network.REP_L2_NORM:
reps_norm = self.get_constant_symbol(1)
else:
assert (reps is not None), 'if representatives are not L2 normalized then reps must be provided'
reps_norm = mx.sym.sum_axis(mx.sym.square(reps), axis=0, keepdims=True)
dist = mx.sym.broadcast_add(mx.sym.broadcast_sub(embd_norm, mx.sym.broadcast_mul(self.get_constant_symbol(2), cos_sim)), reps_norm)
return dist
def get_symbol(self, cfg, is_train=True):
num_classes = cfg.dataset.NUM_CLASSES
num_reg_classes = (2 if cfg.CLASS_AGNOSTIC else num_classes)
data = mx.sym.Variable(name='data')
im_info = mx.sym.Variable(name='im_info')
(res2, res3, res4, res5) = self.get_resnet_backbone(data, with_dpyramid=True, with_dconv=True)
(fpn_p2, fpn_p3, fpn_p4, fpn_p5, fpn_p6) = self.get_fpn_feature(res2, res3, res4, res5)
(rpn_cls_score_p2, rpn_prob_p2, rpn_bbox_loss_p2, rpn_bbox_pred_p2) = self.get_rpn_subnet(fpn_p2, cfg.network.NUM_ANCHORS, 'p2')
(rpn_cls_score_p3, rpn_prob_p3, rpn_bbox_loss_p3, rpn_bbox_pred_p3) = self.get_rpn_subnet(fpn_p3, cfg.network.NUM_ANCHORS, 'p3')
(rpn_cls_score_p4, rpn_prob_p4, rpn_bbox_loss_p4, rpn_bbox_pred_p4) = self.get_rpn_subnet(fpn_p4, cfg.network.NUM_ANCHORS, 'p4')
(rpn_cls_score_p5, rpn_prob_p5, rpn_bbox_loss_p5, rpn_bbox_pred_p5) = self.get_rpn_subnet(fpn_p5, cfg.network.NUM_ANCHORS, 'p5')
(rpn_cls_score_p6, rpn_prob_p6, rpn_bbox_loss_p6, rpn_bbox_pred_p6) = self.get_rpn_subnet(fpn_p6, cfg.network.NUM_ANCHORS, 'p6')
rpn_cls_prob_dict = {'rpn_cls_prob_stride64': rpn_prob_p6, 'rpn_cls_prob_stride32': rpn_prob_p5, 'rpn_cls_prob_stride16': rpn_prob_p4, 'rpn_cls_prob_stride8': rpn_prob_p3, 'rpn_cls_prob_stride4': rpn_prob_p2}
rpn_bbox_pred_dict = {'rpn_bbox_pred_stride64': rpn_bbox_pred_p6, 'rpn_bbox_pred_stride32': rpn_bbox_pred_p5, 'rpn_bbox_pred_stride16': rpn_bbox_pred_p4, 'rpn_bbox_pred_stride8': rpn_bbox_pred_p3, 'rpn_bbox_pred_stride4': rpn_bbox_pred_p2}
arg_dict = dict((rpn_cls_prob_dict.items() + rpn_bbox_pred_dict.items()))
if is_train:
if (not cfg.network.base_net_lock):
rpn_label = mx.sym.Variable(name='label')
rpn_bbox_target = mx.sym.Variable(name='bbox_target')
rpn_bbox_weight = mx.sym.Variable(name='bbox_weight')
gt_boxes = mx.sym.Variable(name='gt_boxes')
if (not cfg.network.base_net_lock):
rpn_cls_score = mx.sym.Concat(rpn_cls_score_p2, rpn_cls_score_p3, rpn_cls_score_p4, rpn_cls_score_p5, rpn_cls_score_p6, dim=2)
rpn_bbox_loss = mx.sym.Concat(rpn_bbox_loss_p2, rpn_bbox_loss_p3, rpn_bbox_loss_p4, rpn_bbox_loss_p5, rpn_bbox_loss_p6, dim=2)
rpn_cls_output = mx.sym.SoftmaxOutput(data=rpn_cls_score, label=rpn_label, multi_output=True, normalization='valid', use_ignore=True, ignore_label=(- 1), name='rpn_cls_prob')
rpn_bbox_loss = (rpn_bbox_weight * mx.sym.smooth_l1(name='rpn_bbox_loss_l1', scalar=3.0, data=(rpn_bbox_loss - rpn_bbox_target)))
rpn_bbox_loss = mx.sym.MakeLoss(name='rpn_bbox_loss', data=rpn_bbox_loss, grad_scale=(1.0 / cfg.TRAIN.RPN_BATCH_SIZE))
aux_dict = {'op_type': 'pyramid_proposal', 'name': 'rois', 'im_info': im_info, 'feat_stride': tuple(cfg.network.RPN_FEAT_STRIDE), 'scales': tuple(cfg.network.ANCHOR_SCALES), 'ratios': tuple(cfg.network.ANCHOR_RATIOS), 'rpn_pre_nms_top_n': cfg.TRAIN.RPN_PRE_NMS_TOP_N, 'rpn_post_nms_top_n': cfg.TRAIN.RPN_POST_NMS_TOP_N, 'threshold': cfg.TRAIN.RPN_NMS_THRESH, 'rpn_min_size': cfg.TRAIN.RPN_MIN_SIZE}
rois = mx.sym.Custom(**dict((arg_dict.items() + aux_dict.items())))
gt_boxes_reshape = mx.sym.Reshape(data=gt_boxes, shape=((- 1), 5), name='gt_boxes_reshape')
(rois, label, bbox_target, bbox_weight) = mx.sym.Custom(rois=rois, gt_boxes=gt_boxes_reshape, op_type='proposal_target', num_classes=num_reg_classes, batch_images=cfg.TRAIN.BATCH_IMAGES, batch_rois=cfg.TRAIN.BATCH_ROIS, cfg=cPickle.dumps(cfg), fg_fraction=cfg.TRAIN.FG_FRACTION)
else:
aux_dict = {'op_type': 'pyramid_proposal', 'name': 'rois', 'im_info': im_info, 'feat_stride': tuple(cfg.network.RPN_FEAT_STRIDE), 'scales': tuple(cfg.network.ANCHOR_SCALES), 'ratios': tuple(cfg.network.ANCHOR_RATIOS), 'rpn_pre_nms_top_n': cfg.TEST.RPN_PRE_NMS_TOP_N, 'rpn_post_nms_top_n': cfg.TEST.RPN_POST_NMS_TOP_N, 'threshold': cfg.TEST.RPN_NMS_THRESH, 'rpn_min_size': cfg.TEST.RPN_MIN_SIZE}
rois = mx.sym.Custom(**dict((arg_dict.items() + aux_dict.items())))
offset_p2_weight = mx.sym.Variable(name='offset_p2_weight', dtype=np.float32, lr_mult=0.01)
offset_p3_weight = mx.sym.Variable(name='offset_p3_weight', dtype=np.float32, lr_mult=0.01)
offset_p4_weight = mx.sym.Variable(name='offset_p4_weight', dtype=np.float32, lr_mult=0.01)
offset_p5_weight = mx.sym.Variable(name='offset_p5_weight', dtype=np.float32, lr_mult=0.01)
offset_p2_bias = mx.sym.Variable(name='offset_p2_bias', dtype=np.float32, lr_mult=0.01)
offset_p3_bias = mx.sym.Variable(name='offset_p3_bias', dtype=np.float32, lr_mult=0.01)
offset_p4_bias = mx.sym.Variable(name='offset_p4_bias', dtype=np.float32, lr_mult=0.01)
offset_p5_bias = mx.sym.Variable(name='offset_p5_bias', dtype=np.float32, lr_mult=0.01)
roi_pool = mx.symbol.Custom(data_p2=fpn_p2, data_p3=fpn_p3, data_p4=fpn_p4, data_p5=fpn_p5, offset_weight_p2=offset_p2_weight, offset_bias_p2=offset_p2_bias, offset_weight_p3=offset_p3_weight, offset_bias_p3=offset_p3_bias, offset_weight_p4=offset_p4_weight, offset_bias_p4=offset_p4_bias, offset_weight_p5=offset_p5_weight, offset_bias_p5=offset_p5_bias, rois=rois, op_type='fpn_roi_pooling', name='fpn_roi_pooling', with_deformable=True)
fc_new_1 = mx.symbol.FullyConnected(name='fc_new_1', data=roi_pool, num_hidden=1024)
fc_new_1_relu = mx.sym.Activation(data=fc_new_1, act_type='relu', name='fc_new_1_relu')
fc_new_2 = mx.symbol.FullyConnected(name='fc_new_2', data=fc_new_1_relu, num_hidden=1024)
fc_new_2_relu = mx.sym.Activation(data=fc_new_2, act_type='relu', name='fc_new_2_relu')
if (is_train and cfg.network.base_net_lock):
fc_new_2_relu = mx.sym.BlockGrad(fc_new_2_relu)
rois = mx.sym.BlockGrad(rois)
label = mx.sym.BlockGrad(label)
bbox_target = mx.sym.BlockGrad(bbox_target)
bbox_weight = mx.sym.BlockGrad(bbox_weight)
lr_mult = cfg.TRAIN.REPS_LR_MULT
if cfg.network.SEPARABLE_REPS:
base = mx.sym.FullyConnected(data=self.get_constant_symbol(1), name='fc_representatives_base', num_hidden=(cfg.network.EMBEDDING_DIM * (num_classes - 1)), no_bias=True, lr_mult=lr_mult)
offset = mx.sym.FullyConnected(data=self.get_constant_symbol(1), name='fc_representatives_offset', num_hidden=(cfg.network.EMBEDDING_DIM * cfg.network.REPS_PER_CLASS), no_bias=True, lr_mult=lr_mult)
base = mx.sym.reshape(base, shape=(cfg.network.EMBEDDING_DIM, 1, (num_classes - 1)))
offset = mx.sym.reshape(offset, shape=(cfg.network.EMBEDDING_DIM, cfg.network.REPS_PER_CLASS, 1))
representatives = mx.sym.broadcast_add(base, offset, name='fc_representatives')
else:
representatives = mx.sym.FullyConnected(data=self.get_constant_symbol(1), name='fc_representatives', num_hidden=((cfg.network.EMBEDDING_DIM * cfg.network.REPS_PER_CLASS) * (num_classes - 1)), no_bias=True, lr_mult=lr_mult)
representatives = mx.sym.reshape(representatives, shape=(cfg.network.EMBEDDING_DIM, cfg.network.REPS_PER_CLASS, (num_classes - 1)))
if cfg.network.REP_L2_NORM:
representatives = mx.sym.transpose(mx.sym.L2Normalization(mx.sym.transpose(representatives, axes=(1, 0, 2)), mode='channel'), axes=(1, 0, 2))
extra_outputs = [mx.sym.BlockGrad(representatives)]
batch_embed = mx.symbol.FullyConnected(name='batch_embed', data=fc_new_2_relu, num_hidden=cfg.network.EMBEDDING_DIM)
if cfg.network.EMBED_L2_NORM:
batch_embed = mx.sym.L2Normalization(data=batch_embed, name='batch_embed_nrm', mode='instance')
cos_sim = mx.sym.dot(batch_embed, representatives, transpose_b=False)
all_cls_rep_dist = self.cos_sim_2_dist(cos_sim, cfg, embd=batch_embed, reps=representatives)
if (is_train and cfg.network.EMBED_LOSS_ENABLED):
if cfg.network.SMOOTH_MIN:
sum_exp_dist = mx.sym.sum_axis(mx.sym.exp(mx.sym.broadcast_mul(all_cls_rep_dist, self.get_constant_symbol(cfg.network.SMOOTH_CONST))), axis=1, keepdims=True)
all_cls_min_dist = mx.sym.broadcast_div(mx.sym.log(sum_exp_dist), self.get_constant_symbol(cfg.network.SMOOTH_CONST))
else:
all_cls_min_dist = mx.sym.min_axis(all_cls_rep_dist, axis=1, keepdims=True)
all_cls_min_dist = mx.sym.reshape(all_cls_min_dist, shape=(0, (num_classes - 1)))
mod_true_class = mx.sym.slice_axis(mx.sym.one_hot(label, depth=num_classes, on_value=1, off_value=0), axis=1, begin=1, end=None)
mod_false_class = mx.sym.slice_axis(mx.sym.one_hot(label, depth=num_classes, on_value=1000, off_value=0), axis=1, begin=1, end=None)
min_dist_true = mx.sym.sum_axis(mx.sym.broadcast_mul(all_cls_min_dist, mod_true_class), axis=1)
min_dist_false = mx.sym.min_axis(mx.sym.broadcast_add(all_cls_min_dist, mod_false_class), axis=1)
embed_loss_val = mx.sym.broadcast_sub(min_dist_true, min_dist_false)
embed_loss_val = mx.sym.broadcast_add(embed_loss_val, self.get_constant_symbol(cfg.network.EMBED_LOSS_MARGIN))
embed_loss_val = mx.sym.relu(embed_loss_val)
embed_loss_val = mx.sym.reshape(embed_loss_val, shape=(0, 1))
if (is_train and cfg.network.REPS_CLS_LOSS):
mask_block_ones = mx.sym.ones(shape=(cfg.network.REPS_PER_CLASS, cfg.network.REPS_PER_CLASS))
mask_block_zeros = mx.sym.zeros(shape=(cfg.network.REPS_PER_CLASS, cfg.network.REPS_PER_CLASS))
mask = None
for iC1 in range((num_classes - 1)):
mask_row = None
for iC2 in range((num_classes - 1)):
if (iC1 == iC2):
cblock = mask_block_ones
else:
cblock = mask_block_zeros
if (mask_row is None):
mask_row = cblock
else:
mask_row = mx.sym.concat(mask_row, cblock, dim=1)
if (mask is None):
mask = mask_row
else:
mask = mx.sym.concat(mask, mask_row, dim=0)
mask_NC = mx.sym.broadcast_mul(self.get_constant_symbol(1000), mask)
mask_C = mx.sym.broadcast_sub(self.get_constant_symbol(1000), mask_NC)
mask_C = mx.sym.BlockGrad(mask_C)
mask_NC = mx.sym.BlockGrad(mask_NC)
R = mx.sym.reshape(mx.sym.transpose(representatives, axes=(0, 2, 1)), shape=(0, (- 1)))
R2R_cos_sim = mx.sym.dot(R, R, transpose_a=True)
R2R = self.cos_sim_2_dist_generic(R2R_cos_sim, x=R, y=R, x_is_norm=cfg.network.REP_L2_NORM, y_is_norm=cfg.network.REP_L2_NORM)
C2C = mx.sym.broadcast_add(R2R, mask_C)
C2NC = mx.sym.broadcast_add(R2R, mask_NC)
min_dist_C = mx.sym.topk(C2C, axis=1, k=2, ret_typ='value', is_ascend=True)
min_dist_C = mx.sym.slice_axis(min_dist_C, axis=1, begin=1, end=2)
min_dist_NC = mx.sym.min_axis(C2NC, axis=1, keepdims=True)
reps_cls_loss_val = mx.sym.broadcast_sub(min_dist_C, min_dist_NC)
reps_cls_loss_val = mx.sym.broadcast_add(reps_cls_loss_val, self.get_constant_symbol(cfg.network.EMBED_LOSS_MARGIN))
reps_cls_loss_val = mx.sym.relu(reps_cls_loss_val)
probs = mx.sym.exp(mx.sym.broadcast_mul(all_cls_rep_dist, self.get_constant_symbol(((- 0.5) / float((cfg.network.SIGMA ** 2))))))
comb_cls_scores = mx.sym.max_axis(probs, axis=1, keepdims=False)
comb_cls_scores = mx.sym.broadcast_add(comb_cls_scores, self.get_constant_symbol(1e-07))
bg_scores = mx.sym.broadcast_sub(self.get_constant_symbol((1 + 1e-07)), mx.sym.max_axis(comb_cls_scores, axis=1, keepdims=True))
cls_score = mx.sym.concat(bg_scores, comb_cls_scores, dim=1, name='bg_concat')
cls_score = mx.sym.reshape(cls_score, shape=(0, (- 1)))
if cfg.network.SOFTMAX_ENABLED:
cls_score = mx.sym.broadcast_mul(self.get_constant_symbol(cfg.network.SOFTMAX_MUL), cls_score)
if cfg.network.ADDITIONAL_LINEAR_CLS_LOSS:
cls_score_lin = mx.symbol.FullyConnected(name='cls_score_lin', data=fc_new_2_relu, num_hidden=num_classes)
bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=fc_new_2_relu, num_hidden=(num_reg_classes * 4))
if is_train:
if cfg.TRAIN.ENABLE_OHEM:
rm_last = int(cfg.TRAIN.RM_LAST)
(labels_ohem, bbox_weights_ohem) = mx.sym.Custom(op_type='BoxAnnotatorOHEM', num_classes=num_classes, num_reg_classes=num_reg_classes, roi_per_img=cfg.TRAIN.BATCH_ROIS_OHEM, cls_score=cls_score, bbox_pred=bbox_pred, labels=label, bbox_targets=bbox_target, bbox_weights=bbox_weight, rm_last=rm_last)
if cfg.network.SOFTMAX_ENABLED:
cls_prob = mx.sym.SoftmaxOutput(name='cls_prob', data=cls_score, label=labels_ohem, normalization='valid', use_ignore=True, ignore_label=(- 1))
else:
zz = mx.sym.zeros_like(label)
cls_prob = mx.sym.BlockGrad(cls_score)
invalid = mx.sym.broadcast_equal(labels_ohem, self.get_constant_symbol((- 1)))
minoh_labels = mx.sym.one_hot(mx.sym.broadcast_add(mx.sym.cast(invalid, dtype='float32'), labels_ohem), depth=num_classes, on_value=(- 1), off_value=0)
ce_loss = mx.sym.where(invalid, x=zz, y=mx.sym.sum(mx.sym.broadcast_mul(minoh_labels, mx.sym.log(mx.sym.broadcast_add(cls_score, self.get_constant_symbol(1e-07)))), axis=1))
ce_loss = mx.sym.MakeLoss(ce_loss, normalization='valid')
bbox_loss_ = (bbox_weights_ohem * mx.sym.smooth_l1(name='bbox_loss_', scalar=1.0, data=(bbox_pred - bbox_target)))
bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=(1.0 / cfg.TRAIN.BATCH_ROIS_OHEM))
rcnn_label = labels_ohem
if cfg.network.ADDITIONAL_LINEAR_CLS_LOSS:
cls_prob_lin = mx.sym.SoftmaxOutput(name='cls_prob_lin', data=cls_score_lin, label=labels_ohem, normalization='valid', use_ignore=True, ignore_label=(- 1))
if cfg.network.EMBED_LOSS_ENABLED:
embed_loss_ = (mx.sym.slice_axis(bbox_weights_ohem, axis=1, begin=(- 1), end=None) * embed_loss_val)
embed_loss = mx.sym.MakeLoss(name='embed_loss', data=embed_loss_, grad_scale=(1.0 / cfg.TRAIN.BATCH_ROIS_OHEM))
else:
cls_prob = mx.sym.SoftmaxOutput(name='cls_prob', data=cls_score, label=label, normalization='valid')
bbox_loss_ = (bbox_weight * mx.sym.smooth_l1(name='bbox_loss_', scalar=1.0, data=(bbox_pred - bbox_target)))
bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=(1.0 / cfg.TRAIN.BATCH_ROIS))
rcnn_label = label
if cfg.network.ADDITIONAL_LINEAR_CLS_LOSS:
cls_prob_lin = mx.sym.SoftmaxOutput(name='cls_prob_lin', data=cls_score_lin, label=label, normalization='valid')
if cfg.network.EMBED_LOSS_ENABLED:
embed_loss_ = (mx.sym.slice_axis(bbox_weight, axis=1, begin=0, end=1) * embed_loss_val)
embed_loss = mx.sym.MakeLoss(name='embed_loss', data=embed_loss_, grad_scale=(1.0 / cfg.TRAIN.BATCH_ROIS))
if cfg.network.EMBED_LOSS_ENABLED:
extra_outputs += [embed_loss]
if cfg.network.REPS_CLS_LOSS:
extra_outputs += [mx.sym.MakeLoss(name='reps_cls_loss', data=reps_cls_loss_val, grad_scale=(1.0 / (cfg.network.REPS_PER_CLASS * (num_classes - 1))))]
if cfg.network.ADDITIONAL_LINEAR_CLS_LOSS:
extra_outputs += [cls_prob_lin]
if (not cfg.network.SOFTMAX_ENABLED):
extra_outputs += [ce_loss]
extra_outputs += [mx.sym.BlockGrad(rois), mx.sym.identity(mx.sym.BlockGrad(batch_embed), name='psp_final_embed')]
rcnn_label = mx.sym.Reshape(data=rcnn_label, shape=(cfg.TRAIN.BATCH_IMAGES, (- 1)), name='label_reshape')
cls_prob = mx.sym.Reshape(data=cls_prob, shape=(cfg.TRAIN.BATCH_IMAGES, (- 1), num_classes), name='cls_prob_reshape')
bbox_loss = mx.sym.Reshape(data=bbox_loss, shape=(cfg.TRAIN.BATCH_IMAGES, (- 1), (4 * num_reg_classes)), name='bbox_loss_reshape')
if cfg.network.base_net_lock:
group = mx.sym.Group(([cls_prob, bbox_loss, mx.sym.BlockGrad(rcnn_label)] + extra_outputs))
else:
group = mx.sym.Group(([rpn_cls_output, rpn_bbox_loss, cls_prob, bbox_loss, mx.sym.BlockGrad(rcnn_label)] + extra_outputs))
else:
cls_prob = mx.sym.SoftmaxActivation(name='cls_prob', data=cls_score)
cls_prob = mx.sym.Reshape(data=cls_prob, shape=(cfg.TEST.BATCH_IMAGES, (- 1), num_classes), name='cls_prob_reshape')
bbox_pred = mx.sym.Reshape(data=bbox_pred, shape=(cfg.TEST.BATCH_IMAGES, (- 1), (4 * num_reg_classes)), name='bbox_pred_reshape')
cls_score_orig = cls_score
if cfg.network.SOFTMAX_ENABLED:
cls_score_orig = mx.sym.broadcast_div(cls_score_orig, self.get_constant_symbol(cfg.network.SOFTMAX_MUL))
cls_score_orig = mx.sym.Reshape(data=cls_score_orig, shape=(cfg.TEST.BATCH_IMAGES, (- 1), num_classes))
group = mx.sym.Group([rois, cls_prob, bbox_pred, mx.sym.identity(batch_embed, name='psp_final_embed'), mx.sym.identity(cls_score_orig, name='cls_score')])
self.sym = group
return group
def init_weight_rcnn(self, cfg, arg_params, aux_params):
arg_params['fc_new_1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_1_weight'])
arg_params['fc_new_1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_1_bias'])
arg_params['fc_new_2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_2_weight'])
arg_params['fc_new_2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_2_bias'])
arg_params['bbox_pred_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['bbox_pred_weight'])
arg_params['bbox_pred_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['bbox_pred_bias'])
arg_params['batch_embed_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['batch_embed_weight'])
arg_params['batch_embed_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['batch_embed_bias'])
name = 'fc_representatives'
if cfg.network.SEPARABLE_REPS:
arg_params[(name + '_base_weight')] = mx.random.normal(0, 0.1, shape=self.arg_shape_dict[(name + '_base_weight')])
arg_params[(name + '_offset_weight')] = mx.random.normal(0, 0.1, shape=self.arg_shape_dict[(name + '_offset_weight')])
elif cfg.network.SEPARABLE_REPS_INIT:
C = mx.random.normal(0, 0.1, shape=(cfg.network.EMBEDDING_DIM, 1, (cfg.dataset.NUM_CLASSES - 1)))
R = mx.random.normal(0, 0.05, shape=(cfg.network.EMBEDDING_DIM, cfg.network.REPS_PER_CLASS, 1))
CR = (C + R)
arg_params[(name + '_weight')] = mx.nd.reshape(CR, shape=((- 1), 1))
else:
arg_params[(name + '_weight')] = mx.random.normal(0, 0.1, shape=self.arg_shape_dict[(name + '_weight')])
if cfg.network.ADDITIONAL_LINEAR_CLS_LOSS:
arg_params['cls_score_lin_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['cls_score_lin_weight'])
arg_params['cls_score_lin_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['cls_score_lin_bias'])
def init_deformable_convnet(self, cfg, arg_params, aux_params):
arg_params['res5a_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res5a_branch2b_offset_weight'])
arg_params['res5a_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res5a_branch2b_offset_bias'])
arg_params['res5b_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res5b_branch2b_offset_weight'])
arg_params['res5b_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res5b_branch2b_offset_bias'])
arg_params['res5c_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res5c_branch2b_offset_weight'])
arg_params['res5c_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res5c_branch2b_offset_bias'])
arg_params['res3b3_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res3b3_branch2b_offset_weight'])
arg_params['res3b3_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res3b3_branch2b_offset_bias'])
arg_params['res4b22_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res4b22_branch2b_offset_weight'])
arg_params['res4b22_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res4b22_branch2b_offset_bias'])
def init_weight_fpn(self, cfg, arg_params, aux_params):
arg_params['fpn_p6_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p6_weight'])
arg_params['fpn_p6_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p6_bias'])
arg_params['fpn_p5_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p5_weight'])
arg_params['fpn_p5_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p5_bias'])
arg_params['fpn_p4_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p4_weight'])
arg_params['fpn_p4_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p4_bias'])
arg_params['fpn_p3_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p3_weight'])
arg_params['fpn_p3_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p3_bias'])
arg_params['fpn_p2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p2_weight'])
arg_params['fpn_p2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p2_bias'])
arg_params['fpn_p5_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p5_1x1_weight'])
arg_params['fpn_p5_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p5_1x1_bias'])
arg_params['fpn_p4_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p4_1x1_weight'])
arg_params['fpn_p4_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p4_1x1_bias'])
arg_params['fpn_p3_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p3_1x1_weight'])
arg_params['fpn_p3_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p3_1x1_bias'])
arg_params['fpn_p2_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p2_1x1_weight'])
arg_params['fpn_p2_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p2_1x1_bias'])
def init_weight(self, cfg, arg_params, aux_params):
(arg_params2, aux_params2) = ({}, {})
for name in self.shared_param_list:
if ('offset' in name):
arg_params2[(name + '_weight')] = mx.nd.zeros(shape=self.arg_shape_dict[(name + '_weight')])
else:
arg_params2[(name + '_weight')] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict[(name + '_weight')])
arg_params2[(name + '_bias')] = mx.nd.zeros(shape=self.arg_shape_dict[(name + '_bias')])
self.init_deformable_convnet(cfg, arg_params2, aux_params2)
self.init_weight_rcnn(cfg, arg_params2, aux_params2)
self.init_weight_fpn(cfg, arg_params2, aux_params2)
for k in arg_params2:
if cfg.network.pretrained_weights_are_priority:
if ((k not in arg_params) or (arg_params[k].shape != arg_params2[k].shape)):
arg_params[k] = arg_params2[k]
else:
arg_params[k] = arg_params2[k]
for k in aux_params2:
if cfg.network.pretrained_weights_are_priority:
if (k not in aux_params):
aux_params[k] = aux_params2[k]
else:
aux_params[k] = aux_params2[k]
|
class resnet_v1_101_fpn_dcn_rcnn_rep_noemb(Symbol):
def __init__(self):
'\n Use __init__ to define parameter network needs\n '
self.shared_param_list = ['offset_p2', 'offset_p3', 'offset_p4', 'offset_p5', 'rpn_conv', 'rpn_cls_score', 'rpn_bbox_pred']
self.shared_param_dict = {}
for name in self.shared_param_list:
self.shared_param_dict[(name + '_weight')] = mx.sym.Variable((name + '_weight'))
self.shared_param_dict[(name + '_bias')] = mx.sym.Variable((name + '_bias'))
self.constants_dict = {}
def get_resnet_backbone(self, data, with_dilated=False, with_dconv=False, with_dpyramid=False, eps=1e-05):
conv1 = mx.symbol.Convolution(name='conv1', data=data, num_filter=64, pad=(3, 3), kernel=(7, 7), stride=(2, 2), no_bias=True)
bn_conv1 = mx.symbol.BatchNorm(name='bn_conv1', data=conv1, use_global_stats=True, fix_gamma=False, eps=eps)
scale_conv1 = bn_conv1
conv1_relu = mx.symbol.Activation(name='conv1_relu', data=scale_conv1, act_type='relu')
pool1 = mx.symbol.Pooling(name='pool1', data=conv1_relu, pooling_convention='full', pad=(0, 0), kernel=(3, 3), stride=(2, 2), pool_type='max')
res2a_branch1 = mx.symbol.Convolution(name='res2a_branch1', data=pool1, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2a_branch1 = mx.symbol.BatchNorm(name='bn2a_branch1', data=res2a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch1 = bn2a_branch1
res2a_branch2a = mx.symbol.Convolution(name='res2a_branch2a', data=pool1, num_filter=64, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2a_branch2a = mx.symbol.BatchNorm(name='bn2a_branch2a', data=res2a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch2a = bn2a_branch2a
res2a_branch2a_relu = mx.symbol.Activation(name='res2a_branch2a_relu', data=scale2a_branch2a, act_type='relu')
res2a_branch2b = mx.symbol.Convolution(name='res2a_branch2b', data=res2a_branch2a_relu, num_filter=64, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn2a_branch2b = mx.symbol.BatchNorm(name='bn2a_branch2b', data=res2a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch2b = bn2a_branch2b
res2a_branch2b_relu = mx.symbol.Activation(name='res2a_branch2b_relu', data=scale2a_branch2b, act_type='relu')
res2a_branch2c = mx.symbol.Convolution(name='res2a_branch2c', data=res2a_branch2b_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2a_branch2c = mx.symbol.BatchNorm(name='bn2a_branch2c', data=res2a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch2c = bn2a_branch2c
res2a = mx.symbol.broadcast_add(*[scale2a_branch1, scale2a_branch2c], name='res2a')
res2a_relu = mx.symbol.Activation(name='res2a_relu', data=res2a, act_type='relu')
res2b_branch2a = mx.symbol.Convolution(name='res2b_branch2a', data=res2a_relu, num_filter=64, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2b_branch2a = mx.symbol.BatchNorm(name='bn2b_branch2a', data=res2b_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale2b_branch2a = bn2b_branch2a
res2b_branch2a_relu = mx.symbol.Activation(name='res2b_branch2a_relu', data=scale2b_branch2a, act_type='relu')
res2b_branch2b = mx.symbol.Convolution(name='res2b_branch2b', data=res2b_branch2a_relu, num_filter=64, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn2b_branch2b = mx.symbol.BatchNorm(name='bn2b_branch2b', data=res2b_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale2b_branch2b = bn2b_branch2b
res2b_branch2b_relu = mx.symbol.Activation(name='res2b_branch2b_relu', data=scale2b_branch2b, act_type='relu')
res2b_branch2c = mx.symbol.Convolution(name='res2b_branch2c', data=res2b_branch2b_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2b_branch2c = mx.symbol.BatchNorm(name='bn2b_branch2c', data=res2b_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale2b_branch2c = bn2b_branch2c
res2b = mx.symbol.broadcast_add(*[res2a_relu, scale2b_branch2c], name='res2b')
res2b_relu = mx.symbol.Activation(name='res2b_relu', data=res2b, act_type='relu')
res2c_branch2a = mx.symbol.Convolution(name='res2c_branch2a', data=res2b_relu, num_filter=64, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2c_branch2a = mx.symbol.BatchNorm(name='bn2c_branch2a', data=res2c_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale2c_branch2a = bn2c_branch2a
res2c_branch2a_relu = mx.symbol.Activation(name='res2c_branch2a_relu', data=scale2c_branch2a, act_type='relu')
res2c_branch2b = mx.symbol.Convolution(name='res2c_branch2b', data=res2c_branch2a_relu, num_filter=64, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn2c_branch2b = mx.symbol.BatchNorm(name='bn2c_branch2b', data=res2c_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale2c_branch2b = bn2c_branch2b
res2c_branch2b_relu = mx.symbol.Activation(name='res2c_branch2b_relu', data=scale2c_branch2b, act_type='relu')
res2c_branch2c = mx.symbol.Convolution(name='res2c_branch2c', data=res2c_branch2b_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2c_branch2c = mx.symbol.BatchNorm(name='bn2c_branch2c', data=res2c_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale2c_branch2c = bn2c_branch2c
res2c = mx.symbol.broadcast_add(*[res2b_relu, scale2c_branch2c], name='res2c')
res2c_relu = mx.symbol.Activation(name='res2c_relu', data=res2c, act_type='relu')
res3a_branch1 = mx.symbol.Convolution(name='res3a_branch1', data=res2c_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn3a_branch1 = mx.symbol.BatchNorm(name='bn3a_branch1', data=res3a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch1 = bn3a_branch1
res3a_branch2a = mx.symbol.Convolution(name='res3a_branch2a', data=res2c_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn3a_branch2a = mx.symbol.BatchNorm(name='bn3a_branch2a', data=res3a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch2a = bn3a_branch2a
res3a_branch2a_relu = mx.symbol.Activation(name='res3a_branch2a_relu', data=scale3a_branch2a, act_type='relu')
res3a_branch2b = mx.symbol.Convolution(name='res3a_branch2b', data=res3a_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3a_branch2b = mx.symbol.BatchNorm(name='bn3a_branch2b', data=res3a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch2b = bn3a_branch2b
res3a_branch2b_relu = mx.symbol.Activation(name='res3a_branch2b_relu', data=scale3a_branch2b, act_type='relu')
res3a_branch2c = mx.symbol.Convolution(name='res3a_branch2c', data=res3a_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3a_branch2c = mx.symbol.BatchNorm(name='bn3a_branch2c', data=res3a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch2c = bn3a_branch2c
res3a = mx.symbol.broadcast_add(*[scale3a_branch1, scale3a_branch2c], name='res3a')
res3a_relu = mx.symbol.Activation(name='res3a_relu', data=res3a, act_type='relu')
res3b1_branch2a = mx.symbol.Convolution(name='res3b1_branch2a', data=res3a_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b1_branch2a = mx.symbol.BatchNorm(name='bn3b1_branch2a', data=res3b1_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b1_branch2a = bn3b1_branch2a
res3b1_branch2a_relu = mx.symbol.Activation(name='res3b1_branch2a_relu', data=scale3b1_branch2a, act_type='relu')
res3b1_branch2b = mx.symbol.Convolution(name='res3b1_branch2b', data=res3b1_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3b1_branch2b = mx.symbol.BatchNorm(name='bn3b1_branch2b', data=res3b1_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b1_branch2b = bn3b1_branch2b
res3b1_branch2b_relu = mx.symbol.Activation(name='res3b1_branch2b_relu', data=scale3b1_branch2b, act_type='relu')
res3b1_branch2c = mx.symbol.Convolution(name='res3b1_branch2c', data=res3b1_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b1_branch2c = mx.symbol.BatchNorm(name='bn3b1_branch2c', data=res3b1_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b1_branch2c = bn3b1_branch2c
res3b1 = mx.symbol.broadcast_add(*[res3a_relu, scale3b1_branch2c], name='res3b1')
res3b1_relu = mx.symbol.Activation(name='res3b1_relu', data=res3b1, act_type='relu')
res3b2_branch2a = mx.symbol.Convolution(name='res3b2_branch2a', data=res3b1_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b2_branch2a = mx.symbol.BatchNorm(name='bn3b2_branch2a', data=res3b2_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b2_branch2a = bn3b2_branch2a
res3b2_branch2a_relu = mx.symbol.Activation(name='res3b2_branch2a_relu', data=scale3b2_branch2a, act_type='relu')
res3b2_branch2b = mx.symbol.Convolution(name='res3b2_branch2b', data=res3b2_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3b2_branch2b = mx.symbol.BatchNorm(name='bn3b2_branch2b', data=res3b2_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b2_branch2b = bn3b2_branch2b
res3b2_branch2b_relu = mx.symbol.Activation(name='res3b2_branch2b_relu', data=scale3b2_branch2b, act_type='relu')
res3b2_branch2c = mx.symbol.Convolution(name='res3b2_branch2c', data=res3b2_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b2_branch2c = mx.symbol.BatchNorm(name='bn3b2_branch2c', data=res3b2_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b2_branch2c = bn3b2_branch2c
res3b2 = mx.symbol.broadcast_add(*[res3b1_relu, scale3b2_branch2c], name='res3b2')
res3b2_relu = mx.symbol.Activation(name='res3b2_relu', data=res3b2, act_type='relu')
res3b3_branch2a = mx.symbol.Convolution(name='res3b3_branch2a', data=res3b2_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b3_branch2a = mx.symbol.BatchNorm(name='bn3b3_branch2a', data=res3b3_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b3_branch2a = bn3b3_branch2a
res3b3_branch2a_relu = mx.symbol.Activation(name='res3b3_branch2a_relu', data=scale3b3_branch2a, act_type='relu')
if with_dpyramid:
res3b3_branch2b_offset = mx.symbol.Convolution(name='res3b3_branch2b_offset', data=res3b3_branch2a_relu, num_filter=72, pad=(1, 1), kernel=(3, 3), stride=(1, 1))
res3b3_branch2b = mx.contrib.symbol.DeformableConvolution(name='res3b3_branch2b', data=res3b3_branch2a_relu, offset=res3b3_branch2b_offset, num_filter=128, pad=(1, 1), kernel=(3, 3), num_deformable_group=4, stride=(1, 1), no_bias=True)
else:
res3b3_branch2b = mx.symbol.Convolution(name='res3b3_branch2b', data=res3b3_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3b3_branch2b = mx.symbol.BatchNorm(name='bn3b3_branch2b', data=res3b3_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b3_branch2b = bn3b3_branch2b
res3b3_branch2b_relu = mx.symbol.Activation(name='res3b3_branch2b_relu', data=scale3b3_branch2b, act_type='relu')
res3b3_branch2c = mx.symbol.Convolution(name='res3b3_branch2c', data=res3b3_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b3_branch2c = mx.symbol.BatchNorm(name='bn3b3_branch2c', data=res3b3_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b3_branch2c = bn3b3_branch2c
res3b3 = mx.symbol.broadcast_add(*[res3b2_relu, scale3b3_branch2c], name='res3b3')
res3b3_relu = mx.symbol.Activation(name='res3b3_relu', data=res3b3, act_type='relu')
res4a_branch1 = mx.symbol.Convolution(name='res4a_branch1', data=res3b3_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn4a_branch1 = mx.symbol.BatchNorm(name='bn4a_branch1', data=res4a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch1 = bn4a_branch1
res4a_branch2a = mx.symbol.Convolution(name='res4a_branch2a', data=res3b3_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn4a_branch2a = mx.symbol.BatchNorm(name='bn4a_branch2a', data=res4a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch2a = bn4a_branch2a
res4a_branch2a_relu = mx.symbol.Activation(name='res4a_branch2a_relu', data=scale4a_branch2a, act_type='relu')
res4a_branch2b = mx.symbol.Convolution(name='res4a_branch2b', data=res4a_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4a_branch2b = mx.symbol.BatchNorm(name='bn4a_branch2b', data=res4a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch2b = bn4a_branch2b
res4a_branch2b_relu = mx.symbol.Activation(name='res4a_branch2b_relu', data=scale4a_branch2b, act_type='relu')
res4a_branch2c = mx.symbol.Convolution(name='res4a_branch2c', data=res4a_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4a_branch2c = mx.symbol.BatchNorm(name='bn4a_branch2c', data=res4a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch2c = bn4a_branch2c
res4a = mx.symbol.broadcast_add(*[scale4a_branch1, scale4a_branch2c], name='res4a')
res4a_relu = mx.symbol.Activation(name='res4a_relu', data=res4a, act_type='relu')
res4b1_branch2a = mx.symbol.Convolution(name='res4b1_branch2a', data=res4a_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b1_branch2a = mx.symbol.BatchNorm(name='bn4b1_branch2a', data=res4b1_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b1_branch2a = bn4b1_branch2a
res4b1_branch2a_relu = mx.symbol.Activation(name='res4b1_branch2a_relu', data=scale4b1_branch2a, act_type='relu')
res4b1_branch2b = mx.symbol.Convolution(name='res4b1_branch2b', data=res4b1_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b1_branch2b = mx.symbol.BatchNorm(name='bn4b1_branch2b', data=res4b1_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b1_branch2b = bn4b1_branch2b
res4b1_branch2b_relu = mx.symbol.Activation(name='res4b1_branch2b_relu', data=scale4b1_branch2b, act_type='relu')
res4b1_branch2c = mx.symbol.Convolution(name='res4b1_branch2c', data=res4b1_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b1_branch2c = mx.symbol.BatchNorm(name='bn4b1_branch2c', data=res4b1_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b1_branch2c = bn4b1_branch2c
res4b1 = mx.symbol.broadcast_add(*[res4a_relu, scale4b1_branch2c], name='res4b1')
res4b1_relu = mx.symbol.Activation(name='res4b1_relu', data=res4b1, act_type='relu')
res4b2_branch2a = mx.symbol.Convolution(name='res4b2_branch2a', data=res4b1_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b2_branch2a = mx.symbol.BatchNorm(name='bn4b2_branch2a', data=res4b2_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b2_branch2a = bn4b2_branch2a
res4b2_branch2a_relu = mx.symbol.Activation(name='res4b2_branch2a_relu', data=scale4b2_branch2a, act_type='relu')
res4b2_branch2b = mx.symbol.Convolution(name='res4b2_branch2b', data=res4b2_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b2_branch2b = mx.symbol.BatchNorm(name='bn4b2_branch2b', data=res4b2_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b2_branch2b = bn4b2_branch2b
res4b2_branch2b_relu = mx.symbol.Activation(name='res4b2_branch2b_relu', data=scale4b2_branch2b, act_type='relu')
res4b2_branch2c = mx.symbol.Convolution(name='res4b2_branch2c', data=res4b2_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b2_branch2c = mx.symbol.BatchNorm(name='bn4b2_branch2c', data=res4b2_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b2_branch2c = bn4b2_branch2c
res4b2 = mx.symbol.broadcast_add(*[res4b1_relu, scale4b2_branch2c], name='res4b2')
res4b2_relu = mx.symbol.Activation(name='res4b2_relu', data=res4b2, act_type='relu')
res4b3_branch2a = mx.symbol.Convolution(name='res4b3_branch2a', data=res4b2_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b3_branch2a = mx.symbol.BatchNorm(name='bn4b3_branch2a', data=res4b3_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b3_branch2a = bn4b3_branch2a
res4b3_branch2a_relu = mx.symbol.Activation(name='res4b3_branch2a_relu', data=scale4b3_branch2a, act_type='relu')
res4b3_branch2b = mx.symbol.Convolution(name='res4b3_branch2b', data=res4b3_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b3_branch2b = mx.symbol.BatchNorm(name='bn4b3_branch2b', data=res4b3_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b3_branch2b = bn4b3_branch2b
res4b3_branch2b_relu = mx.symbol.Activation(name='res4b3_branch2b_relu', data=scale4b3_branch2b, act_type='relu')
res4b3_branch2c = mx.symbol.Convolution(name='res4b3_branch2c', data=res4b3_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b3_branch2c = mx.symbol.BatchNorm(name='bn4b3_branch2c', data=res4b3_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b3_branch2c = bn4b3_branch2c
res4b3 = mx.symbol.broadcast_add(*[res4b2_relu, scale4b3_branch2c], name='res4b3')
res4b3_relu = mx.symbol.Activation(name='res4b3_relu', data=res4b3, act_type='relu')
res4b4_branch2a = mx.symbol.Convolution(name='res4b4_branch2a', data=res4b3_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b4_branch2a = mx.symbol.BatchNorm(name='bn4b4_branch2a', data=res4b4_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b4_branch2a = bn4b4_branch2a
res4b4_branch2a_relu = mx.symbol.Activation(name='res4b4_branch2a_relu', data=scale4b4_branch2a, act_type='relu')
res4b4_branch2b = mx.symbol.Convolution(name='res4b4_branch2b', data=res4b4_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b4_branch2b = mx.symbol.BatchNorm(name='bn4b4_branch2b', data=res4b4_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b4_branch2b = bn4b4_branch2b
res4b4_branch2b_relu = mx.symbol.Activation(name='res4b4_branch2b_relu', data=scale4b4_branch2b, act_type='relu')
res4b4_branch2c = mx.symbol.Convolution(name='res4b4_branch2c', data=res4b4_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b4_branch2c = mx.symbol.BatchNorm(name='bn4b4_branch2c', data=res4b4_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b4_branch2c = bn4b4_branch2c
res4b4 = mx.symbol.broadcast_add(*[res4b3_relu, scale4b4_branch2c], name='res4b4')
res4b4_relu = mx.symbol.Activation(name='res4b4_relu', data=res4b4, act_type='relu')
res4b5_branch2a = mx.symbol.Convolution(name='res4b5_branch2a', data=res4b4_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b5_branch2a = mx.symbol.BatchNorm(name='bn4b5_branch2a', data=res4b5_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b5_branch2a = bn4b5_branch2a
res4b5_branch2a_relu = mx.symbol.Activation(name='res4b5_branch2a_relu', data=scale4b5_branch2a, act_type='relu')
res4b5_branch2b = mx.symbol.Convolution(name='res4b5_branch2b', data=res4b5_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b5_branch2b = mx.symbol.BatchNorm(name='bn4b5_branch2b', data=res4b5_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b5_branch2b = bn4b5_branch2b
res4b5_branch2b_relu = mx.symbol.Activation(name='res4b5_branch2b_relu', data=scale4b5_branch2b, act_type='relu')
res4b5_branch2c = mx.symbol.Convolution(name='res4b5_branch2c', data=res4b5_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b5_branch2c = mx.symbol.BatchNorm(name='bn4b5_branch2c', data=res4b5_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b5_branch2c = bn4b5_branch2c
res4b5 = mx.symbol.broadcast_add(*[res4b4_relu, scale4b5_branch2c], name='res4b5')
res4b5_relu = mx.symbol.Activation(name='res4b5_relu', data=res4b5, act_type='relu')
res4b6_branch2a = mx.symbol.Convolution(name='res4b6_branch2a', data=res4b5_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b6_branch2a = mx.symbol.BatchNorm(name='bn4b6_branch2a', data=res4b6_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b6_branch2a = bn4b6_branch2a
res4b6_branch2a_relu = mx.symbol.Activation(name='res4b6_branch2a_relu', data=scale4b6_branch2a, act_type='relu')
res4b6_branch2b = mx.symbol.Convolution(name='res4b6_branch2b', data=res4b6_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b6_branch2b = mx.symbol.BatchNorm(name='bn4b6_branch2b', data=res4b6_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b6_branch2b = bn4b6_branch2b
res4b6_branch2b_relu = mx.symbol.Activation(name='res4b6_branch2b_relu', data=scale4b6_branch2b, act_type='relu')
res4b6_branch2c = mx.symbol.Convolution(name='res4b6_branch2c', data=res4b6_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b6_branch2c = mx.symbol.BatchNorm(name='bn4b6_branch2c', data=res4b6_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b6_branch2c = bn4b6_branch2c
res4b6 = mx.symbol.broadcast_add(*[res4b5_relu, scale4b6_branch2c], name='res4b6')
res4b6_relu = mx.symbol.Activation(name='res4b6_relu', data=res4b6, act_type='relu')
res4b7_branch2a = mx.symbol.Convolution(name='res4b7_branch2a', data=res4b6_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b7_branch2a = mx.symbol.BatchNorm(name='bn4b7_branch2a', data=res4b7_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b7_branch2a = bn4b7_branch2a
res4b7_branch2a_relu = mx.symbol.Activation(name='res4b7_branch2a_relu', data=scale4b7_branch2a, act_type='relu')
res4b7_branch2b = mx.symbol.Convolution(name='res4b7_branch2b', data=res4b7_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b7_branch2b = mx.symbol.BatchNorm(name='bn4b7_branch2b', data=res4b7_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b7_branch2b = bn4b7_branch2b
res4b7_branch2b_relu = mx.symbol.Activation(name='res4b7_branch2b_relu', data=scale4b7_branch2b, act_type='relu')
res4b7_branch2c = mx.symbol.Convolution(name='res4b7_branch2c', data=res4b7_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b7_branch2c = mx.symbol.BatchNorm(name='bn4b7_branch2c', data=res4b7_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b7_branch2c = bn4b7_branch2c
res4b7 = mx.symbol.broadcast_add(*[res4b6_relu, scale4b7_branch2c], name='res4b7')
res4b7_relu = mx.symbol.Activation(name='res4b7_relu', data=res4b7, act_type='relu')
res4b8_branch2a = mx.symbol.Convolution(name='res4b8_branch2a', data=res4b7_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b8_branch2a = mx.symbol.BatchNorm(name='bn4b8_branch2a', data=res4b8_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b8_branch2a = bn4b8_branch2a
res4b8_branch2a_relu = mx.symbol.Activation(name='res4b8_branch2a_relu', data=scale4b8_branch2a, act_type='relu')
res4b8_branch2b = mx.symbol.Convolution(name='res4b8_branch2b', data=res4b8_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b8_branch2b = mx.symbol.BatchNorm(name='bn4b8_branch2b', data=res4b8_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b8_branch2b = bn4b8_branch2b
res4b8_branch2b_relu = mx.symbol.Activation(name='res4b8_branch2b_relu', data=scale4b8_branch2b, act_type='relu')
res4b8_branch2c = mx.symbol.Convolution(name='res4b8_branch2c', data=res4b8_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b8_branch2c = mx.symbol.BatchNorm(name='bn4b8_branch2c', data=res4b8_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b8_branch2c = bn4b8_branch2c
res4b8 = mx.symbol.broadcast_add(*[res4b7_relu, scale4b8_branch2c], name='res4b8')
res4b8_relu = mx.symbol.Activation(name='res4b8_relu', data=res4b8, act_type='relu')
res4b9_branch2a = mx.symbol.Convolution(name='res4b9_branch2a', data=res4b8_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b9_branch2a = mx.symbol.BatchNorm(name='bn4b9_branch2a', data=res4b9_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b9_branch2a = bn4b9_branch2a
res4b9_branch2a_relu = mx.symbol.Activation(name='res4b9_branch2a_relu', data=scale4b9_branch2a, act_type='relu')
res4b9_branch2b = mx.symbol.Convolution(name='res4b9_branch2b', data=res4b9_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b9_branch2b = mx.symbol.BatchNorm(name='bn4b9_branch2b', data=res4b9_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b9_branch2b = bn4b9_branch2b
res4b9_branch2b_relu = mx.symbol.Activation(name='res4b9_branch2b_relu', data=scale4b9_branch2b, act_type='relu')
res4b9_branch2c = mx.symbol.Convolution(name='res4b9_branch2c', data=res4b9_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b9_branch2c = mx.symbol.BatchNorm(name='bn4b9_branch2c', data=res4b9_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b9_branch2c = bn4b9_branch2c
res4b9 = mx.symbol.broadcast_add(*[res4b8_relu, scale4b9_branch2c], name='res4b9')
res4b9_relu = mx.symbol.Activation(name='res4b9_relu', data=res4b9, act_type='relu')
res4b10_branch2a = mx.symbol.Convolution(name='res4b10_branch2a', data=res4b9_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b10_branch2a = mx.symbol.BatchNorm(name='bn4b10_branch2a', data=res4b10_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b10_branch2a = bn4b10_branch2a
res4b10_branch2a_relu = mx.symbol.Activation(name='res4b10_branch2a_relu', data=scale4b10_branch2a, act_type='relu')
res4b10_branch2b = mx.symbol.Convolution(name='res4b10_branch2b', data=res4b10_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b10_branch2b = mx.symbol.BatchNorm(name='bn4b10_branch2b', data=res4b10_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b10_branch2b = bn4b10_branch2b
res4b10_branch2b_relu = mx.symbol.Activation(name='res4b10_branch2b_relu', data=scale4b10_branch2b, act_type='relu')
res4b10_branch2c = mx.symbol.Convolution(name='res4b10_branch2c', data=res4b10_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b10_branch2c = mx.symbol.BatchNorm(name='bn4b10_branch2c', data=res4b10_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b10_branch2c = bn4b10_branch2c
res4b10 = mx.symbol.broadcast_add(*[res4b9_relu, scale4b10_branch2c], name='res4b10')
res4b10_relu = mx.symbol.Activation(name='res4b10_relu', data=res4b10, act_type='relu')
res4b11_branch2a = mx.symbol.Convolution(name='res4b11_branch2a', data=res4b10_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b11_branch2a = mx.symbol.BatchNorm(name='bn4b11_branch2a', data=res4b11_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b11_branch2a = bn4b11_branch2a
res4b11_branch2a_relu = mx.symbol.Activation(name='res4b11_branch2a_relu', data=scale4b11_branch2a, act_type='relu')
res4b11_branch2b = mx.symbol.Convolution(name='res4b11_branch2b', data=res4b11_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b11_branch2b = mx.symbol.BatchNorm(name='bn4b11_branch2b', data=res4b11_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b11_branch2b = bn4b11_branch2b
res4b11_branch2b_relu = mx.symbol.Activation(name='res4b11_branch2b_relu', data=scale4b11_branch2b, act_type='relu')
res4b11_branch2c = mx.symbol.Convolution(name='res4b11_branch2c', data=res4b11_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b11_branch2c = mx.symbol.BatchNorm(name='bn4b11_branch2c', data=res4b11_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b11_branch2c = bn4b11_branch2c
res4b11 = mx.symbol.broadcast_add(*[res4b10_relu, scale4b11_branch2c], name='res4b11')
res4b11_relu = mx.symbol.Activation(name='res4b11_relu', data=res4b11, act_type='relu')
res4b12_branch2a = mx.symbol.Convolution(name='res4b12_branch2a', data=res4b11_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b12_branch2a = mx.symbol.BatchNorm(name='bn4b12_branch2a', data=res4b12_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b12_branch2a = bn4b12_branch2a
res4b12_branch2a_relu = mx.symbol.Activation(name='res4b12_branch2a_relu', data=scale4b12_branch2a, act_type='relu')
res4b12_branch2b = mx.symbol.Convolution(name='res4b12_branch2b', data=res4b12_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b12_branch2b = mx.symbol.BatchNorm(name='bn4b12_branch2b', data=res4b12_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b12_branch2b = bn4b12_branch2b
res4b12_branch2b_relu = mx.symbol.Activation(name='res4b12_branch2b_relu', data=scale4b12_branch2b, act_type='relu')
res4b12_branch2c = mx.symbol.Convolution(name='res4b12_branch2c', data=res4b12_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b12_branch2c = mx.symbol.BatchNorm(name='bn4b12_branch2c', data=res4b12_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b12_branch2c = bn4b12_branch2c
res4b12 = mx.symbol.broadcast_add(*[res4b11_relu, scale4b12_branch2c], name='res4b12')
res4b12_relu = mx.symbol.Activation(name='res4b12_relu', data=res4b12, act_type='relu')
res4b13_branch2a = mx.symbol.Convolution(name='res4b13_branch2a', data=res4b12_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b13_branch2a = mx.symbol.BatchNorm(name='bn4b13_branch2a', data=res4b13_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b13_branch2a = bn4b13_branch2a
res4b13_branch2a_relu = mx.symbol.Activation(name='res4b13_branch2a_relu', data=scale4b13_branch2a, act_type='relu')
res4b13_branch2b = mx.symbol.Convolution(name='res4b13_branch2b', data=res4b13_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b13_branch2b = mx.symbol.BatchNorm(name='bn4b13_branch2b', data=res4b13_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b13_branch2b = bn4b13_branch2b
res4b13_branch2b_relu = mx.symbol.Activation(name='res4b13_branch2b_relu', data=scale4b13_branch2b, act_type='relu')
res4b13_branch2c = mx.symbol.Convolution(name='res4b13_branch2c', data=res4b13_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b13_branch2c = mx.symbol.BatchNorm(name='bn4b13_branch2c', data=res4b13_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b13_branch2c = bn4b13_branch2c
res4b13 = mx.symbol.broadcast_add(*[res4b12_relu, scale4b13_branch2c], name='res4b13')
res4b13_relu = mx.symbol.Activation(name='res4b13_relu', data=res4b13, act_type='relu')
res4b14_branch2a = mx.symbol.Convolution(name='res4b14_branch2a', data=res4b13_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b14_branch2a = mx.symbol.BatchNorm(name='bn4b14_branch2a', data=res4b14_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b14_branch2a = bn4b14_branch2a
res4b14_branch2a_relu = mx.symbol.Activation(name='res4b14_branch2a_relu', data=scale4b14_branch2a, act_type='relu')
res4b14_branch2b = mx.symbol.Convolution(name='res4b14_branch2b', data=res4b14_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b14_branch2b = mx.symbol.BatchNorm(name='bn4b14_branch2b', data=res4b14_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b14_branch2b = bn4b14_branch2b
res4b14_branch2b_relu = mx.symbol.Activation(name='res4b14_branch2b_relu', data=scale4b14_branch2b, act_type='relu')
res4b14_branch2c = mx.symbol.Convolution(name='res4b14_branch2c', data=res4b14_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b14_branch2c = mx.symbol.BatchNorm(name='bn4b14_branch2c', data=res4b14_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b14_branch2c = bn4b14_branch2c
res4b14 = mx.symbol.broadcast_add(*[res4b13_relu, scale4b14_branch2c], name='res4b14')
res4b14_relu = mx.symbol.Activation(name='res4b14_relu', data=res4b14, act_type='relu')
res4b15_branch2a = mx.symbol.Convolution(name='res4b15_branch2a', data=res4b14_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b15_branch2a = mx.symbol.BatchNorm(name='bn4b15_branch2a', data=res4b15_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b15_branch2a = bn4b15_branch2a
res4b15_branch2a_relu = mx.symbol.Activation(name='res4b15_branch2a_relu', data=scale4b15_branch2a, act_type='relu')
res4b15_branch2b = mx.symbol.Convolution(name='res4b15_branch2b', data=res4b15_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b15_branch2b = mx.symbol.BatchNorm(name='bn4b15_branch2b', data=res4b15_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b15_branch2b = bn4b15_branch2b
res4b15_branch2b_relu = mx.symbol.Activation(name='res4b15_branch2b_relu', data=scale4b15_branch2b, act_type='relu')
res4b15_branch2c = mx.symbol.Convolution(name='res4b15_branch2c', data=res4b15_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b15_branch2c = mx.symbol.BatchNorm(name='bn4b15_branch2c', data=res4b15_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b15_branch2c = bn4b15_branch2c
res4b15 = mx.symbol.broadcast_add(*[res4b14_relu, scale4b15_branch2c], name='res4b15')
res4b15_relu = mx.symbol.Activation(name='res4b15_relu', data=res4b15, act_type='relu')
res4b16_branch2a = mx.symbol.Convolution(name='res4b16_branch2a', data=res4b15_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b16_branch2a = mx.symbol.BatchNorm(name='bn4b16_branch2a', data=res4b16_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b16_branch2a = bn4b16_branch2a
res4b16_branch2a_relu = mx.symbol.Activation(name='res4b16_branch2a_relu', data=scale4b16_branch2a, act_type='relu')
res4b16_branch2b = mx.symbol.Convolution(name='res4b16_branch2b', data=res4b16_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b16_branch2b = mx.symbol.BatchNorm(name='bn4b16_branch2b', data=res4b16_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b16_branch2b = bn4b16_branch2b
res4b16_branch2b_relu = mx.symbol.Activation(name='res4b16_branch2b_relu', data=scale4b16_branch2b, act_type='relu')
res4b16_branch2c = mx.symbol.Convolution(name='res4b16_branch2c', data=res4b16_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b16_branch2c = mx.symbol.BatchNorm(name='bn4b16_branch2c', data=res4b16_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b16_branch2c = bn4b16_branch2c
res4b16 = mx.symbol.broadcast_add(*[res4b15_relu, scale4b16_branch2c], name='res4b16')
res4b16_relu = mx.symbol.Activation(name='res4b16_relu', data=res4b16, act_type='relu')
res4b17_branch2a = mx.symbol.Convolution(name='res4b17_branch2a', data=res4b16_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b17_branch2a = mx.symbol.BatchNorm(name='bn4b17_branch2a', data=res4b17_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b17_branch2a = bn4b17_branch2a
res4b17_branch2a_relu = mx.symbol.Activation(name='res4b17_branch2a_relu', data=scale4b17_branch2a, act_type='relu')
res4b17_branch2b = mx.symbol.Convolution(name='res4b17_branch2b', data=res4b17_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b17_branch2b = mx.symbol.BatchNorm(name='bn4b17_branch2b', data=res4b17_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b17_branch2b = bn4b17_branch2b
res4b17_branch2b_relu = mx.symbol.Activation(name='res4b17_branch2b_relu', data=scale4b17_branch2b, act_type='relu')
res4b17_branch2c = mx.symbol.Convolution(name='res4b17_branch2c', data=res4b17_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b17_branch2c = mx.symbol.BatchNorm(name='bn4b17_branch2c', data=res4b17_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b17_branch2c = bn4b17_branch2c
res4b17 = mx.symbol.broadcast_add(*[res4b16_relu, scale4b17_branch2c], name='res4b17')
res4b17_relu = mx.symbol.Activation(name='res4b17_relu', data=res4b17, act_type='relu')
res4b18_branch2a = mx.symbol.Convolution(name='res4b18_branch2a', data=res4b17_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b18_branch2a = mx.symbol.BatchNorm(name='bn4b18_branch2a', data=res4b18_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b18_branch2a = bn4b18_branch2a
res4b18_branch2a_relu = mx.symbol.Activation(name='res4b18_branch2a_relu', data=scale4b18_branch2a, act_type='relu')
res4b18_branch2b = mx.symbol.Convolution(name='res4b18_branch2b', data=res4b18_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b18_branch2b = mx.symbol.BatchNorm(name='bn4b18_branch2b', data=res4b18_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b18_branch2b = bn4b18_branch2b
res4b18_branch2b_relu = mx.symbol.Activation(name='res4b18_branch2b_relu', data=scale4b18_branch2b, act_type='relu')
res4b18_branch2c = mx.symbol.Convolution(name='res4b18_branch2c', data=res4b18_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b18_branch2c = mx.symbol.BatchNorm(name='bn4b18_branch2c', data=res4b18_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b18_branch2c = bn4b18_branch2c
res4b18 = mx.symbol.broadcast_add(*[res4b17_relu, scale4b18_branch2c], name='res4b18')
res4b18_relu = mx.symbol.Activation(name='res4b18_relu', data=res4b18, act_type='relu')
res4b19_branch2a = mx.symbol.Convolution(name='res4b19_branch2a', data=res4b18_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b19_branch2a = mx.symbol.BatchNorm(name='bn4b19_branch2a', data=res4b19_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b19_branch2a = bn4b19_branch2a
res4b19_branch2a_relu = mx.symbol.Activation(name='res4b19_branch2a_relu', data=scale4b19_branch2a, act_type='relu')
res4b19_branch2b = mx.symbol.Convolution(name='res4b19_branch2b', data=res4b19_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b19_branch2b = mx.symbol.BatchNorm(name='bn4b19_branch2b', data=res4b19_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b19_branch2b = bn4b19_branch2b
res4b19_branch2b_relu = mx.symbol.Activation(name='res4b19_branch2b_relu', data=scale4b19_branch2b, act_type='relu')
res4b19_branch2c = mx.symbol.Convolution(name='res4b19_branch2c', data=res4b19_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b19_branch2c = mx.symbol.BatchNorm(name='bn4b19_branch2c', data=res4b19_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b19_branch2c = bn4b19_branch2c
res4b19 = mx.symbol.broadcast_add(*[res4b18_relu, scale4b19_branch2c], name='res4b19')
res4b19_relu = mx.symbol.Activation(name='res4b19_relu', data=res4b19, act_type='relu')
res4b20_branch2a = mx.symbol.Convolution(name='res4b20_branch2a', data=res4b19_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b20_branch2a = mx.symbol.BatchNorm(name='bn4b20_branch2a', data=res4b20_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b20_branch2a = bn4b20_branch2a
res4b20_branch2a_relu = mx.symbol.Activation(name='res4b20_branch2a_relu', data=scale4b20_branch2a, act_type='relu')
res4b20_branch2b = mx.symbol.Convolution(name='res4b20_branch2b', data=res4b20_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b20_branch2b = mx.symbol.BatchNorm(name='bn4b20_branch2b', data=res4b20_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b20_branch2b = bn4b20_branch2b
res4b20_branch2b_relu = mx.symbol.Activation(name='res4b20_branch2b_relu', data=scale4b20_branch2b, act_type='relu')
res4b20_branch2c = mx.symbol.Convolution(name='res4b20_branch2c', data=res4b20_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b20_branch2c = mx.symbol.BatchNorm(name='bn4b20_branch2c', data=res4b20_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b20_branch2c = bn4b20_branch2c
res4b20 = mx.symbol.broadcast_add(*[res4b19_relu, scale4b20_branch2c], name='res4b20')
res4b20_relu = mx.symbol.Activation(name='res4b20_relu', data=res4b20, act_type='relu')
res4b21_branch2a = mx.symbol.Convolution(name='res4b21_branch2a', data=res4b20_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b21_branch2a = mx.symbol.BatchNorm(name='bn4b21_branch2a', data=res4b21_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b21_branch2a = bn4b21_branch2a
res4b21_branch2a_relu = mx.symbol.Activation(name='res4b21_branch2a_relu', data=scale4b21_branch2a, act_type='relu')
res4b21_branch2b = mx.symbol.Convolution(name='res4b21_branch2b', data=res4b21_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b21_branch2b = mx.symbol.BatchNorm(name='bn4b21_branch2b', data=res4b21_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b21_branch2b = bn4b21_branch2b
res4b21_branch2b_relu = mx.symbol.Activation(name='res4b21_branch2b_relu', data=scale4b21_branch2b, act_type='relu')
res4b21_branch2c = mx.symbol.Convolution(name='res4b21_branch2c', data=res4b21_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b21_branch2c = mx.symbol.BatchNorm(name='bn4b21_branch2c', data=res4b21_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b21_branch2c = bn4b21_branch2c
res4b21 = mx.symbol.broadcast_add(*[res4b20_relu, scale4b21_branch2c], name='res4b21')
res4b21_relu = mx.symbol.Activation(name='res4b21_relu', data=res4b21, act_type='relu')
res4b22_branch2a = mx.symbol.Convolution(name='res4b22_branch2a', data=res4b21_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b22_branch2a = mx.symbol.BatchNorm(name='bn4b22_branch2a', data=res4b22_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b22_branch2a = bn4b22_branch2a
res4b22_branch2a_relu = mx.symbol.Activation(name='res4b22_branch2a_relu', data=scale4b22_branch2a, act_type='relu')
if with_dpyramid:
res4b22_branch2b_offset = mx.symbol.Convolution(name='res4b22_branch2b_offset', data=res4b22_branch2a_relu, num_filter=72, pad=(1, 1), kernel=(3, 3), stride=(1, 1))
res4b22_branch2b = mx.contrib.symbol.DeformableConvolution(name='res4b22_branch2b', data=res4b22_branch2a_relu, offset=res4b22_branch2b_offset, num_filter=256, pad=(1, 1), kernel=(3, 3), num_deformable_group=4, stride=(1, 1), no_bias=True)
else:
res4b22_branch2b = mx.symbol.Convolution(name='res4b22_branch2b', data=res4b22_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b22_branch2b = mx.symbol.BatchNorm(name='bn4b22_branch2b', data=res4b22_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b22_branch2b = bn4b22_branch2b
res4b22_branch2b_relu = mx.symbol.Activation(name='res4b22_branch2b_relu', data=scale4b22_branch2b, act_type='relu')
res4b22_branch2c = mx.symbol.Convolution(name='res4b22_branch2c', data=res4b22_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b22_branch2c = mx.symbol.BatchNorm(name='bn4b22_branch2c', data=res4b22_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b22_branch2c = bn4b22_branch2c
res4b22 = mx.symbol.broadcast_add(*[res4b21_relu, scale4b22_branch2c], name='res4b22')
res4b22_relu = mx.symbol.Activation(name='res4b22_relu', data=res4b22, act_type='relu')
if with_dilated:
res5_stride = (1, 1)
res5_dilate = (2, 2)
else:
res5_stride = (2, 2)
res5_dilate = (1, 1)
res5a_branch2a = mx.symbol.Convolution(name='res5a_branch2a', data=res4b22_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=res5_stride, no_bias=True)
bn5a_branch2a = mx.symbol.BatchNorm(name='bn5a_branch2a', data=res5a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch2a = bn5a_branch2a
res5a_branch2a_relu = mx.symbol.Activation(name='res5a_branch2a_relu', data=scale5a_branch2a, act_type='relu')
if with_dconv:
res5a_branch2b_offset = mx.symbol.Convolution(name='res5a_branch2b_offset', data=res5a_branch2a_relu, num_filter=72, pad=res5_dilate, kernel=(3, 3), dilate=res5_dilate)
res5a_branch2b = mx.contrib.symbol.DeformableConvolution(name='res5a_branch2b', data=res5a_branch2a_relu, offset=res5a_branch2b_offset, num_filter=512, pad=res5_dilate, kernel=(3, 3), num_deformable_group=4, stride=(1, 1), dilate=res5_dilate, no_bias=True)
else:
res5a_branch2b = mx.symbol.Convolution(name='res5a_branch2b', data=res5a_branch2a_relu, num_filter=512, pad=res5_dilate, kernel=(3, 3), stride=(1, 1), dilate=res5_dilate, no_bias=True)
bn5a_branch2b = mx.symbol.BatchNorm(name='bn5a_branch2b', data=res5a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch2b = bn5a_branch2b
res5a_branch2b_relu = mx.symbol.Activation(name='res5a_branch2b_relu', data=scale5a_branch2b, act_type='relu')
res5a_branch2c = mx.symbol.Convolution(name='res5a_branch2c', data=res5a_branch2b_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5a_branch2c = mx.symbol.BatchNorm(name='bn5a_branch2c', data=res5a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch2c = bn5a_branch2c
res5a_branch1 = mx.symbol.Convolution(name='res5a_branch1', data=res4b22_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=res5_stride, no_bias=True)
bn5a_branch1 = mx.symbol.BatchNorm(name='bn5a_branch1', data=res5a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch1 = bn5a_branch1
res5a = mx.symbol.broadcast_add(*[scale5a_branch1, scale5a_branch2c], name='res5a')
res5a_relu = mx.symbol.Activation(name='res5a_relu', data=res5a, act_type='relu')
res5b_branch2a = mx.symbol.Convolution(name='res5b_branch2a', data=res5a_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5b_branch2a = mx.symbol.BatchNorm(name='bn5b_branch2a', data=res5b_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale5b_branch2a = bn5b_branch2a
res5b_branch2a_relu = mx.symbol.Activation(name='res5b_branch2a_relu', data=scale5b_branch2a, act_type='relu')
if with_dconv:
res5b_branch2b_offset = mx.symbol.Convolution(name='res5b_branch2b_offset', data=res5b_branch2a_relu, num_filter=72, pad=res5_dilate, kernel=(3, 3), dilate=res5_dilate)
res5b_branch2b = mx.contrib.symbol.DeformableConvolution(name='res5b_branch2b', data=res5b_branch2a_relu, offset=res5b_branch2b_offset, num_filter=512, pad=res5_dilate, kernel=(3, 3), num_deformable_group=4, dilate=res5_dilate, no_bias=True)
else:
res5b_branch2b = mx.symbol.Convolution(name='res5b_branch2b', data=res5b_branch2a_relu, num_filter=512, pad=res5_dilate, kernel=(3, 3), stride=(1, 1), dilate=res5_dilate, no_bias=True)
bn5b_branch2b = mx.symbol.BatchNorm(name='bn5b_branch2b', data=res5b_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale5b_branch2b = bn5b_branch2b
res5b_branch2b_relu = mx.symbol.Activation(name='res5b_branch2b_relu', data=scale5b_branch2b, act_type='relu')
res5b_branch2c = mx.symbol.Convolution(name='res5b_branch2c', data=res5b_branch2b_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5b_branch2c = mx.symbol.BatchNorm(name='bn5b_branch2c', data=res5b_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale5b_branch2c = bn5b_branch2c
res5b = mx.symbol.broadcast_add(*[res5a_relu, scale5b_branch2c], name='res5b')
res5b_relu = mx.symbol.Activation(name='res5b_relu', data=res5b, act_type='relu')
res5c_branch2a = mx.symbol.Convolution(name='res5c_branch2a', data=res5b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5c_branch2a = mx.symbol.BatchNorm(name='bn5c_branch2a', data=res5c_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale5c_branch2a = bn5c_branch2a
res5c_branch2a_relu = mx.symbol.Activation(name='res5c_branch2a_relu', data=scale5c_branch2a, act_type='relu')
if with_dconv:
res5c_branch2b_offset = mx.symbol.Convolution(name='res5c_branch2b_offset', data=res5c_branch2a_relu, num_filter=72, pad=res5_dilate, kernel=(3, 3), dilate=res5_dilate)
res5c_branch2b = mx.contrib.symbol.DeformableConvolution(name='res5c_branch2b', data=res5c_branch2a_relu, offset=res5c_branch2b_offset, num_filter=512, pad=res5_dilate, kernel=(3, 3), num_deformable_group=4, dilate=res5_dilate, no_bias=True)
else:
res5c_branch2b = mx.symbol.Convolution(name='res5c_branch2b', data=res5c_branch2a_relu, num_filter=512, pad=res5_dilate, kernel=(3, 3), stride=(1, 1), dilate=res5_dilate, no_bias=True)
bn5c_branch2b = mx.symbol.BatchNorm(name='bn5c_branch2b', data=res5c_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale5c_branch2b = bn5c_branch2b
res5c_branch2b_relu = mx.symbol.Activation(name='res5c_branch2b_relu', data=scale5c_branch2b, act_type='relu')
res5c_branch2c = mx.symbol.Convolution(name='res5c_branch2c', data=res5c_branch2b_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5c_branch2c = mx.symbol.BatchNorm(name='bn5c_branch2c', data=res5c_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale5c_branch2c = bn5c_branch2c
res5c = mx.symbol.broadcast_add(*[res5b_relu, scale5c_branch2c], name='res5c')
res5c_relu = mx.symbol.Activation(name='res5c_relu', data=res5c, act_type='relu')
return (res2c_relu, res3b3_relu, res4b22_relu, res5c_relu)
def get_fpn_feature(self, c2, c3, c4, c5, feature_dim=256):
fpn_p5_1x1 = mx.symbol.Convolution(data=c5, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p5_1x1')
fpn_p4_1x1 = mx.symbol.Convolution(data=c4, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p4_1x1')
fpn_p3_1x1 = mx.symbol.Convolution(data=c3, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p3_1x1')
fpn_p2_1x1 = mx.symbol.Convolution(data=c2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p2_1x1')
fpn_p5_upsample = mx.symbol.UpSampling(fpn_p5_1x1, scale=2, sample_type='nearest', name='fpn_p5_upsample')
fpn_p4_plus = mx.sym.ElementWiseSum(*[fpn_p5_upsample, fpn_p4_1x1], name='fpn_p4_sum')
fpn_p4_upsample = mx.symbol.UpSampling(fpn_p4_plus, scale=2, sample_type='nearest', name='fpn_p4_upsample')
fpn_p3_plus = mx.sym.ElementWiseSum(*[fpn_p4_upsample, fpn_p3_1x1], name='fpn_p3_sum')
fpn_p3_upsample = mx.symbol.UpSampling(fpn_p3_plus, scale=2, sample_type='nearest', name='fpn_p3_upsample')
fpn_p2_plus = mx.sym.ElementWiseSum(*[fpn_p3_upsample, fpn_p2_1x1], name='fpn_p2_sum')
fpn_p6 = mx.sym.Convolution(data=c5, kernel=(3, 3), pad=(1, 1), stride=(2, 2), num_filter=feature_dim, name='fpn_p6')
fpn_p5 = mx.symbol.Convolution(data=fpn_p5_1x1, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p5')
fpn_p4 = mx.symbol.Convolution(data=fpn_p4_plus, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p4')
fpn_p3 = mx.symbol.Convolution(data=fpn_p3_plus, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p3')
fpn_p2 = mx.symbol.Convolution(data=fpn_p2_plus, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p2')
return (fpn_p2, fpn_p3, fpn_p4, fpn_p5, fpn_p6)
def get_rpn_subnet(self, data, num_anchors, suffix):
rpn_conv = mx.sym.Convolution(data=data, kernel=(3, 3), pad=(1, 1), num_filter=512, name=('rpn_conv_' + suffix), weight=self.shared_param_dict['rpn_conv_weight'], bias=self.shared_param_dict['rpn_conv_bias'])
rpn_relu = mx.sym.Activation(data=rpn_conv, act_type='relu', name=('rpn_relu_' + suffix))
rpn_cls_score = mx.sym.Convolution(data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=(2 * num_anchors), name=('rpn_cls_score_' + suffix), weight=self.shared_param_dict['rpn_cls_score_weight'], bias=self.shared_param_dict['rpn_cls_score_bias'])
rpn_bbox_pred = mx.sym.Convolution(data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=(4 * num_anchors), name=('rpn_bbox_pred_' + suffix), weight=self.shared_param_dict['rpn_bbox_pred_weight'], bias=self.shared_param_dict['rpn_bbox_pred_bias'])
rpn_cls_score_t1 = mx.sym.Reshape(data=rpn_cls_score, shape=(0, 2, (- 1), 0), name=('rpn_cls_score_t1_' + suffix))
rpn_cls_score_t2 = mx.sym.Reshape(data=rpn_cls_score_t1, shape=(0, 2, (- 1)), name=('rpn_cls_score_t2_' + suffix))
rpn_cls_prob = mx.sym.SoftmaxActivation(data=rpn_cls_score_t1, mode='channel', name=('rpn_cls_prob_' + suffix))
rpn_cls_prob_t = mx.sym.Reshape(data=rpn_cls_prob, shape=(0, (2 * num_anchors), (- 1), 0), name=('rpn_cls_prob_t_' + suffix))
rpn_bbox_pred_t = mx.sym.Reshape(data=rpn_bbox_pred, shape=(0, 0, (- 1)), name=('rpn_bbox_pred_t_' + suffix))
return (rpn_cls_score_t2, rpn_cls_prob_t, rpn_bbox_pred_t, rpn_bbox_pred)
def get_deformable_roipooling(self, name, data, rois, output_dim, spatial_scale, param_name, group_size=1, pooled_size=7, sample_per_part=4, part_size=7):
offset = mx.contrib.sym.DeformablePSROIPooling(name=(('offset_' + name) + '_t'), data=data, rois=rois, group_size=group_size, pooled_size=pooled_size, sample_per_part=sample_per_part, no_trans=True, part_size=part_size, output_dim=output_dim, spatial_scale=spatial_scale)
offset = mx.sym.FullyConnected(name=('offset_' + name), data=offset, num_hidden=((part_size * part_size) * 2), lr_mult=0.01, weight=self.shared_param_dict[(('offset_' + param_name) + '_weight')], bias=self.shared_param_dict[(('offset_' + param_name) + '_bias')])
offset_reshape = mx.sym.Reshape(data=offset, shape=((- 1), 2, part_size, part_size), name=('offset_reshape_' + name))
output = mx.contrib.sym.DeformablePSROIPooling(name=('deformable_roi_pool_' + name), data=data, rois=rois, trans=offset_reshape, group_size=group_size, pooled_size=pooled_size, sample_per_part=sample_per_part, no_trans=False, part_size=part_size, output_dim=output_dim, spatial_scale=spatial_scale, trans_std=0.1)
return output
def get_constant_symbol(self, const_val):
if (const_val in self.constants_dict):
return self.constants_dict[const_val]
name = 'const_eq_{0}'.format(const_val)
c = mx.sym.Variable(name, shape=(1,), init=MyConstant(value=[const_val]))
c = mx.sym.BlockGrad(c)
self.constants_dict[const_val] = c
return c
def cos_sim_2_dist_generic(self, cos_sim, x=None, y=None, x_is_norm=True, y_is_norm=True):
if x_is_norm:
x_norm = self.get_constant_symbol(1)
else:
assert (x is not None), 'if x is not L2 normalized then x must be provided'
x_norm = mx.sym.sum_axis(mx.sym.square(x), axis=0, keepdims=True)
x_norm = mx.sym.transpose(x_norm, axes=(1, 0))
if y_is_norm:
y_norm = self.get_constant_symbol(1)
else:
assert (y is not None), 'if y is not L2 normalized then y must be provided'
y_norm = mx.sym.sum_axis(mx.sym.square(y), axis=0, keepdims=True)
dist = mx.sym.broadcast_add(mx.sym.broadcast_sub(x_norm, mx.sym.broadcast_mul(self.get_constant_symbol(2), cos_sim)), y_norm)
return dist
def cos_sim_2_dist(self, cos_sim, cfg=None, embd=None, reps=None):
if cfg.network.EMBED_L2_NORM:
embd_norm = self.get_constant_symbol(1)
else:
assert (embd is not None), 'if embedding is not L2 normalized then embd must be provided'
embd_norm = mx.sym.sum_axis(mx.sym.square(embd), axis=1, keepdims=True)
embd_norm = mx.sym.reshape(embd_norm, shape=(0, 1, 1))
if cfg.network.REP_L2_NORM:
reps_norm = self.get_constant_symbol(1)
else:
assert (reps is not None), 'if representatives are not L2 normalized then reps must be provided'
reps_norm = mx.sym.sum_axis(mx.sym.square(reps), axis=0, keepdims=True)
dist = mx.sym.broadcast_add(mx.sym.broadcast_sub(embd_norm, mx.sym.broadcast_mul(self.get_constant_symbol(2), cos_sim)), reps_norm)
return dist
def get_symbol(self, cfg, is_train=True):
num_classes = cfg.dataset.NUM_CLASSES
num_reg_classes = (2 if cfg.CLASS_AGNOSTIC else num_classes)
data = mx.sym.Variable(name='data')
im_info = mx.sym.Variable(name='im_info')
(res2, res3, res4, res5) = self.get_resnet_backbone(data, with_dpyramid=True, with_dconv=True)
(fpn_p2, fpn_p3, fpn_p4, fpn_p5, fpn_p6) = self.get_fpn_feature(res2, res3, res4, res5)
(rpn_cls_score_p2, rpn_prob_p2, rpn_bbox_loss_p2, rpn_bbox_pred_p2) = self.get_rpn_subnet(fpn_p2, cfg.network.NUM_ANCHORS, 'p2')
(rpn_cls_score_p3, rpn_prob_p3, rpn_bbox_loss_p3, rpn_bbox_pred_p3) = self.get_rpn_subnet(fpn_p3, cfg.network.NUM_ANCHORS, 'p3')
(rpn_cls_score_p4, rpn_prob_p4, rpn_bbox_loss_p4, rpn_bbox_pred_p4) = self.get_rpn_subnet(fpn_p4, cfg.network.NUM_ANCHORS, 'p4')
(rpn_cls_score_p5, rpn_prob_p5, rpn_bbox_loss_p5, rpn_bbox_pred_p5) = self.get_rpn_subnet(fpn_p5, cfg.network.NUM_ANCHORS, 'p5')
(rpn_cls_score_p6, rpn_prob_p6, rpn_bbox_loss_p6, rpn_bbox_pred_p6) = self.get_rpn_subnet(fpn_p6, cfg.network.NUM_ANCHORS, 'p6')
rpn_cls_prob_dict = {'rpn_cls_prob_stride64': rpn_prob_p6, 'rpn_cls_prob_stride32': rpn_prob_p5, 'rpn_cls_prob_stride16': rpn_prob_p4, 'rpn_cls_prob_stride8': rpn_prob_p3, 'rpn_cls_prob_stride4': rpn_prob_p2}
rpn_bbox_pred_dict = {'rpn_bbox_pred_stride64': rpn_bbox_pred_p6, 'rpn_bbox_pred_stride32': rpn_bbox_pred_p5, 'rpn_bbox_pred_stride16': rpn_bbox_pred_p4, 'rpn_bbox_pred_stride8': rpn_bbox_pred_p3, 'rpn_bbox_pred_stride4': rpn_bbox_pred_p2}
arg_dict = dict((rpn_cls_prob_dict.items() + rpn_bbox_pred_dict.items()))
if is_train:
if (not cfg.network.base_net_lock):
rpn_label = mx.sym.Variable(name='label')
rpn_bbox_target = mx.sym.Variable(name='bbox_target')
rpn_bbox_weight = mx.sym.Variable(name='bbox_weight')
gt_boxes = mx.sym.Variable(name='gt_boxes')
if (not cfg.network.base_net_lock):
rpn_cls_score = mx.sym.Concat(rpn_cls_score_p2, rpn_cls_score_p3, rpn_cls_score_p4, rpn_cls_score_p5, rpn_cls_score_p6, dim=2)
rpn_bbox_loss = mx.sym.Concat(rpn_bbox_loss_p2, rpn_bbox_loss_p3, rpn_bbox_loss_p4, rpn_bbox_loss_p5, rpn_bbox_loss_p6, dim=2)
rpn_cls_output = mx.sym.SoftmaxOutput(data=rpn_cls_score, label=rpn_label, multi_output=True, normalization='valid', use_ignore=True, ignore_label=(- 1), name='rpn_cls_prob')
rpn_bbox_loss = (rpn_bbox_weight * mx.sym.smooth_l1(name='rpn_bbox_loss_l1', scalar=3.0, data=(rpn_bbox_loss - rpn_bbox_target)))
rpn_bbox_loss = mx.sym.MakeLoss(name='rpn_bbox_loss', data=rpn_bbox_loss, grad_scale=(1.0 / cfg.TRAIN.RPN_BATCH_SIZE))
aux_dict = {'op_type': 'pyramid_proposal', 'name': 'rois', 'im_info': im_info, 'feat_stride': tuple(cfg.network.RPN_FEAT_STRIDE), 'scales': tuple(cfg.network.ANCHOR_SCALES), 'ratios': tuple(cfg.network.ANCHOR_RATIOS), 'rpn_pre_nms_top_n': cfg.TRAIN.RPN_PRE_NMS_TOP_N, 'rpn_post_nms_top_n': cfg.TRAIN.RPN_POST_NMS_TOP_N, 'threshold': cfg.TRAIN.RPN_NMS_THRESH, 'rpn_min_size': cfg.TRAIN.RPN_MIN_SIZE}
rois = mx.sym.Custom(**dict((arg_dict.items() + aux_dict.items())))
gt_boxes_reshape = mx.sym.Reshape(data=gt_boxes, shape=((- 1), 5), name='gt_boxes_reshape')
(rois, label, bbox_target, bbox_weight) = mx.sym.Custom(rois=rois, gt_boxes=gt_boxes_reshape, op_type='proposal_target', num_classes=num_reg_classes, batch_images=cfg.TRAIN.BATCH_IMAGES, batch_rois=cfg.TRAIN.BATCH_ROIS, cfg=cPickle.dumps(cfg), fg_fraction=cfg.TRAIN.FG_FRACTION)
else:
aux_dict = {'op_type': 'pyramid_proposal', 'name': 'rois', 'im_info': im_info, 'feat_stride': tuple(cfg.network.RPN_FEAT_STRIDE), 'scales': tuple(cfg.network.ANCHOR_SCALES), 'ratios': tuple(cfg.network.ANCHOR_RATIOS), 'rpn_pre_nms_top_n': cfg.TEST.RPN_PRE_NMS_TOP_N, 'rpn_post_nms_top_n': cfg.TEST.RPN_POST_NMS_TOP_N, 'threshold': cfg.TEST.RPN_NMS_THRESH, 'rpn_min_size': cfg.TEST.RPN_MIN_SIZE}
rois = mx.sym.Custom(**dict((arg_dict.items() + aux_dict.items())))
offset_p2_weight = mx.sym.Variable(name='offset_p2_weight', dtype=np.float32, lr_mult=0.01)
offset_p3_weight = mx.sym.Variable(name='offset_p3_weight', dtype=np.float32, lr_mult=0.01)
offset_p4_weight = mx.sym.Variable(name='offset_p4_weight', dtype=np.float32, lr_mult=0.01)
offset_p5_weight = mx.sym.Variable(name='offset_p5_weight', dtype=np.float32, lr_mult=0.01)
offset_p2_bias = mx.sym.Variable(name='offset_p2_bias', dtype=np.float32, lr_mult=0.01)
offset_p3_bias = mx.sym.Variable(name='offset_p3_bias', dtype=np.float32, lr_mult=0.01)
offset_p4_bias = mx.sym.Variable(name='offset_p4_bias', dtype=np.float32, lr_mult=0.01)
offset_p5_bias = mx.sym.Variable(name='offset_p5_bias', dtype=np.float32, lr_mult=0.01)
roi_pool = mx.symbol.Custom(data_p2=fpn_p2, data_p3=fpn_p3, data_p4=fpn_p4, data_p5=fpn_p5, offset_weight_p2=offset_p2_weight, offset_bias_p2=offset_p2_bias, offset_weight_p3=offset_p3_weight, offset_bias_p3=offset_p3_bias, offset_weight_p4=offset_p4_weight, offset_bias_p4=offset_p4_bias, offset_weight_p5=offset_p5_weight, offset_bias_p5=offset_p5_bias, rois=rois, op_type='fpn_roi_pooling', name='fpn_roi_pooling', with_deformable=True)
fc_new_1 = mx.symbol.FullyConnected(name='fc_new_1', data=roi_pool, num_hidden=1024)
fc_new_1_relu = mx.sym.Activation(data=fc_new_1, act_type='relu', name='fc_new_1_relu')
fc_new_2 = mx.symbol.FullyConnected(name='fc_new_2', data=fc_new_1_relu, num_hidden=1024)
fc_new_2_relu = mx.sym.Activation(data=fc_new_2, act_type='relu', name='fc_new_2_relu')
if (is_train and cfg.network.base_net_lock):
fc_new_2_relu = mx.sym.BlockGrad(fc_new_2_relu)
rois = mx.sym.BlockGrad(rois)
label = mx.sym.BlockGrad(label)
bbox_target = mx.sym.BlockGrad(bbox_target)
bbox_weight = mx.sym.BlockGrad(bbox_weight)
lr_mult = cfg.TRAIN.REPS_LR_MULT
if cfg.network.SEPARABLE_REPS:
base = mx.sym.FullyConnected(data=self.get_constant_symbol(1), name='fc_representatives_base', num_hidden=(cfg.network.EMBEDDING_DIM * (num_classes - 1)), no_bias=True, lr_mult=lr_mult)
offset = mx.sym.FullyConnected(data=self.get_constant_symbol(1), name='fc_representatives_offset', num_hidden=(cfg.network.EMBEDDING_DIM * cfg.network.REPS_PER_CLASS), no_bias=True, lr_mult=lr_mult)
base = mx.sym.reshape(base, shape=(cfg.network.EMBEDDING_DIM, 1, (num_classes - 1)))
offset = mx.sym.reshape(offset, shape=(cfg.network.EMBEDDING_DIM, cfg.network.REPS_PER_CLASS, 1))
representatives = mx.sym.broadcast_add(base, offset, name='fc_representatives')
else:
representatives = mx.sym.FullyConnected(data=self.get_constant_symbol(1), name='fc_representatives', num_hidden=((cfg.network.EMBEDDING_DIM * cfg.network.REPS_PER_CLASS) * (num_classes - 1)), no_bias=True, lr_mult=lr_mult)
representatives = mx.sym.reshape(representatives, shape=(cfg.network.EMBEDDING_DIM, cfg.network.REPS_PER_CLASS, (num_classes - 1)))
if cfg.network.REP_L2_NORM:
representatives = mx.sym.transpose(mx.sym.L2Normalization(mx.sym.transpose(representatives, axes=(1, 0, 2)), mode='channel'), axes=(1, 0, 2))
extra_outputs = [mx.sym.BlockGrad(representatives)]
eps = 1e-05
x = mx.sym.FullyConnected(name='embed_dense_1', data=fc_new_2_relu, num_hidden=2048)
x = mx.sym.BatchNorm(name='embed_batchNorm_1', data=x, use_global_stats=True, fix_gamma=False, eps=eps)
x = mx.sym.Activation(name='embed_relu_1', data=x, act_type='relu')
x = mx.sym.FullyConnected(name='embed_dense_2', data=x, num_hidden=1024)
x = mx.sym.BatchNorm(name='embed_batchNorm_2', data=x, use_global_stats=True, fix_gamma=False, eps=eps)
x = mx.sym.Activation(name='embed_relu_2', data=x, act_type='relu')
x = mx.sym.FullyConnected(name='embed_dense_3', data=x, num_hidden=1024)
batch_embed = mx.sym.identity(name='batch_embed', data=fc_new_2_relu)
if cfg.network.EMBED_L2_NORM:
batch_embed = mx.sym.L2Normalization(data=batch_embed, name='batch_embed_nrm', mode='instance')
cos_sim = mx.sym.dot(batch_embed, representatives, transpose_b=False)
all_cls_rep_dist = self.cos_sim_2_dist(cos_sim, cfg, embd=batch_embed, reps=representatives)
if (is_train and cfg.network.EMBED_LOSS_ENABLED):
all_cls_min_dist = mx.sym.min_axis(all_cls_rep_dist, axis=1, keepdims=True)
all_cls_min_dist = mx.sym.reshape(all_cls_min_dist, shape=(0, (num_classes - 1)))
mod_true_class = mx.sym.slice_axis(mx.sym.one_hot(label, depth=num_classes, on_value=1, off_value=0), axis=1, begin=1, end=None)
mod_false_class = mx.sym.slice_axis(mx.sym.one_hot(label, depth=num_classes, on_value=1000, off_value=0), axis=1, begin=1, end=None)
min_dist_true = mx.sym.sum_axis(mx.sym.broadcast_mul(all_cls_min_dist, mod_true_class), axis=1)
min_dist_false = mx.sym.min_axis(mx.sym.broadcast_add(all_cls_min_dist, mod_false_class), axis=1)
embed_loss_val = mx.sym.broadcast_sub(min_dist_true, min_dist_false)
embed_loss_val = mx.sym.broadcast_add(embed_loss_val, self.get_constant_symbol(cfg.network.EMBED_LOSS_MARGIN))
embed_loss_val = mx.sym.relu(embed_loss_val)
embed_loss_val = mx.sym.reshape(embed_loss_val, shape=(0, 1))
if (is_train and cfg.network.REPS_CLS_LOSS):
mask_block_ones = mx.sym.ones(shape=(cfg.network.REPS_PER_CLASS, cfg.network.REPS_PER_CLASS))
mask_block_zeros = mx.sym.zeros(shape=(cfg.network.REPS_PER_CLASS, cfg.network.REPS_PER_CLASS))
mask = None
for iC1 in range((num_classes - 1)):
mask_row = None
for iC2 in range((num_classes - 1)):
if (iC1 == iC2):
cblock = mask_block_ones
else:
cblock = mask_block_zeros
if (mask_row is None):
mask_row = cblock
else:
mask_row = mx.sym.concat(mask_row, cblock, dim=1)
if (mask is None):
mask = mask_row
else:
mask = mx.sym.concat(mask, mask_row, dim=0)
mask_NC = mx.sym.broadcast_mul(self.get_constant_symbol(1000), mask)
mask_C = mx.sym.broadcast_sub(self.get_constant_symbol(1000), mask_NC)
mask_C = mx.sym.BlockGrad(mask_C)
mask_NC = mx.sym.BlockGrad(mask_NC)
R = mx.sym.reshape(mx.sym.transpose(representatives, axes=(0, 2, 1)), shape=(0, (- 1)))
R2R_cos_sim = mx.sym.dot(R, R, transpose_a=True)
R2R = self.cos_sim_2_dist_generic(R2R_cos_sim, x=R, y=R, x_is_norm=cfg.network.REP_L2_NORM, y_is_norm=cfg.network.REP_L2_NORM)
C2C = mx.sym.broadcast_add(R2R, mask_C)
C2NC = mx.sym.broadcast_add(R2R, mask_NC)
min_dist_C = mx.sym.topk(C2C, axis=1, k=2, ret_typ='value', is_ascend=True)
min_dist_C = mx.sym.slice_axis(min_dist_C, axis=1, begin=1, end=2)
min_dist_NC = mx.sym.min_axis(C2NC, axis=1, keepdims=True)
reps_cls_loss_val = mx.sym.broadcast_sub(min_dist_C, min_dist_NC)
reps_cls_loss_val = mx.sym.broadcast_add(reps_cls_loss_val, self.get_constant_symbol(cfg.network.EMBED_LOSS_MARGIN))
reps_cls_loss_val = mx.sym.relu(reps_cls_loss_val)
probs = mx.sym.exp(mx.sym.broadcast_mul(all_cls_rep_dist, self.get_constant_symbol(((- 0.5) / float((cfg.network.SIGMA ** 2))))))
comb_cls_scores = mx.sym.max_axis(probs, axis=1, keepdims=False)
comb_cls_scores = mx.sym.broadcast_add(comb_cls_scores, self.get_constant_symbol(1e-07))
bg_scores = mx.sym.broadcast_sub(self.get_constant_symbol((1 + 1e-07)), mx.sym.max_axis(comb_cls_scores, axis=1, keepdims=True))
cls_score = mx.sym.concat(bg_scores, comb_cls_scores, dim=1, name='bg_concat')
cls_score = mx.sym.reshape(cls_score, shape=(0, (- 1)))
if cfg.network.SOFTMAX_ENABLED:
cls_score = mx.sym.broadcast_mul(self.get_constant_symbol(cfg.network.SOFTMAX_MUL), cls_score)
if cfg.network.ADDITIONAL_LINEAR_CLS_LOSS:
cls_score_lin = mx.symbol.FullyConnected(name='cls_score_lin', data=fc_new_2_relu, num_hidden=num_classes)
bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=fc_new_2_relu, num_hidden=(num_reg_classes * 4))
if is_train:
if cfg.TRAIN.ENABLE_OHEM:
(labels_ohem, bbox_weights_ohem) = mx.sym.Custom(op_type='BoxAnnotatorOHEM', num_classes=num_classes, num_reg_classes=num_reg_classes, roi_per_img=cfg.TRAIN.BATCH_ROIS_OHEM, cls_score=cls_score, bbox_pred=bbox_pred, labels=label, bbox_targets=bbox_target, bbox_weights=bbox_weight)
if cfg.network.SOFTMAX_ENABLED:
cls_prob = mx.sym.SoftmaxOutput(name='cls_prob', data=cls_score, label=labels_ohem, normalization='valid', use_ignore=True, ignore_label=(- 1))
else:
zz = mx.sym.zeros_like(label)
cls_prob = mx.sym.BlockGrad(cls_score)
invalid = mx.sym.broadcast_equal(labels_ohem, self.get_constant_symbol((- 1)))
minoh_labels = mx.sym.one_hot(mx.sym.broadcast_add(mx.sym.cast(invalid, dtype='float32'), labels_ohem), depth=num_classes, on_value=(- 1), off_value=0)
ce_loss = mx.sym.where(invalid, x=zz, y=mx.sym.sum(mx.sym.broadcast_mul(minoh_labels, mx.sym.log(mx.sym.broadcast_add(cls_score, self.get_constant_symbol(1e-07)))), axis=1))
ce_loss = mx.sym.MakeLoss(ce_loss, normalization='valid')
bbox_loss_ = (bbox_weights_ohem * mx.sym.smooth_l1(name='bbox_loss_', scalar=1.0, data=(bbox_pred - bbox_target)))
bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=(1.0 / cfg.TRAIN.BATCH_ROIS_OHEM))
rcnn_label = labels_ohem
if cfg.network.ADDITIONAL_LINEAR_CLS_LOSS:
cls_prob_lin = mx.sym.SoftmaxOutput(name='cls_prob_lin', data=cls_score_lin, label=labels_ohem, normalization='valid', use_ignore=True, ignore_label=(- 1))
if cfg.network.EMBED_LOSS_ENABLED:
embed_loss_ = (mx.sym.slice_axis(bbox_weights_ohem, axis=1, begin=(- 1), end=None) * embed_loss_val)
embed_loss = mx.sym.MakeLoss(name='embed_loss', data=embed_loss_, grad_scale=(1.0 / cfg.TRAIN.BATCH_ROIS_OHEM))
else:
cls_prob = mx.sym.SoftmaxOutput(name='cls_prob', data=cls_score, label=label, normalization='valid')
bbox_loss_ = (bbox_weight * mx.sym.smooth_l1(name='bbox_loss_', scalar=1.0, data=(bbox_pred - bbox_target)))
bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=(1.0 / cfg.TRAIN.BATCH_ROIS))
rcnn_label = label
if cfg.network.ADDITIONAL_LINEAR_CLS_LOSS:
cls_prob_lin = mx.sym.SoftmaxOutput(name='cls_prob_lin', data=cls_score_lin, label=label, normalization='valid')
if cfg.network.EMBED_LOSS_ENABLED:
embed_loss_ = (mx.sym.slice_axis(bbox_weight, axis=1, begin=0, end=1) * embed_loss_val)
embed_loss = mx.sym.MakeLoss(name='embed_loss', data=embed_loss_, grad_scale=(1.0 / cfg.TRAIN.BATCH_ROIS))
if cfg.network.EMBED_LOSS_ENABLED:
extra_outputs += [embed_loss]
if cfg.network.REPS_CLS_LOSS:
extra_outputs += [mx.sym.MakeLoss(name='reps_cls_loss', data=reps_cls_loss_val, grad_scale=(1.0 / (cfg.network.REPS_PER_CLASS * (num_classes - 1))))]
if cfg.network.ADDITIONAL_LINEAR_CLS_LOSS:
extra_outputs += [cls_prob_lin]
if (not cfg.network.SOFTMAX_ENABLED):
extra_outputs += [ce_loss]
extra_outputs += [mx.sym.BlockGrad(rois), mx.sym.identity(mx.sym.BlockGrad(batch_embed), name='psp_final_embed')]
rcnn_label = mx.sym.Reshape(data=rcnn_label, shape=(cfg.TRAIN.BATCH_IMAGES, (- 1)), name='label_reshape')
cls_prob = mx.sym.Reshape(data=cls_prob, shape=(cfg.TRAIN.BATCH_IMAGES, (- 1), num_classes), name='cls_prob_reshape')
bbox_loss = mx.sym.Reshape(data=bbox_loss, shape=(cfg.TRAIN.BATCH_IMAGES, (- 1), (4 * num_reg_classes)), name='bbox_loss_reshape')
if cfg.network.base_net_lock:
group = mx.sym.Group(([cls_prob, bbox_loss, mx.sym.BlockGrad(rcnn_label)] + extra_outputs))
else:
group = mx.sym.Group(([rpn_cls_output, rpn_bbox_loss, cls_prob, bbox_loss, mx.sym.BlockGrad(rcnn_label)] + extra_outputs))
else:
cls_prob = mx.sym.SoftmaxActivation(name='cls_prob', data=cls_score)
cls_prob = mx.sym.Reshape(data=cls_prob, shape=(cfg.TEST.BATCH_IMAGES, (- 1), num_classes), name='cls_prob_reshape')
bbox_pred = mx.sym.Reshape(data=bbox_pred, shape=(cfg.TEST.BATCH_IMAGES, (- 1), (4 * num_reg_classes)), name='bbox_pred_reshape')
cls_score_orig = cls_score
if cfg.network.SOFTMAX_ENABLED:
cls_score_orig = mx.sym.broadcast_div(cls_score_orig, self.get_constant_symbol(cfg.network.SOFTMAX_MUL))
cls_score_orig = mx.sym.Reshape(data=cls_score_orig, shape=(cfg.TEST.BATCH_IMAGES, (- 1), num_classes))
group = mx.sym.Group([rois, cls_prob, bbox_pred, mx.sym.identity(batch_embed, name='psp_final_embed'), mx.sym.identity(cls_score_orig, name='cls_score')])
self.sym = group
return group
def init_weight_rcnn(self, cfg, arg_params, aux_params):
arg_params['fc_new_1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_1_weight'])
arg_params['fc_new_1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_1_bias'])
arg_params['fc_new_2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_2_weight'])
arg_params['fc_new_2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_2_bias'])
arg_params['bbox_pred_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['bbox_pred_weight'])
arg_params['bbox_pred_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['bbox_pred_bias'])
name = 'fc_representatives'
if cfg.network.SEPARABLE_REPS:
arg_params[(name + '_base_weight')] = mx.random.normal(0, 0.1, shape=self.arg_shape_dict[(name + '_base_weight')])
arg_params[(name + '_offset_weight')] = mx.random.normal(0, 0.1, shape=self.arg_shape_dict[(name + '_offset_weight')])
elif cfg.network.SEPARABLE_REPS_INIT:
C = mx.random.normal(0, 0.1, shape=(cfg.network.EMBEDDING_DIM, 1, (cfg.dataset.NUM_CLASSES - 1)))
R = mx.random.normal(0, 0.05, shape=(cfg.network.EMBEDDING_DIM, cfg.network.REPS_PER_CLASS, 1))
CR = (C + R)
arg_params[(name + '_weight')] = mx.nd.reshape(CR, shape=((- 1), 1))
else:
arg_params[(name + '_weight')] = mx.random.normal(0, 0.1, shape=self.arg_shape_dict[(name + '_weight')])
if cfg.network.ADDITIONAL_LINEAR_CLS_LOSS:
arg_params['cls_score_lin_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['cls_score_lin_weight'])
arg_params['cls_score_lin_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['cls_score_lin_bias'])
def init_deformable_convnet(self, cfg, arg_params, aux_params):
arg_params['res5a_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res5a_branch2b_offset_weight'])
arg_params['res5a_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res5a_branch2b_offset_bias'])
arg_params['res5b_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res5b_branch2b_offset_weight'])
arg_params['res5b_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res5b_branch2b_offset_bias'])
arg_params['res5c_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res5c_branch2b_offset_weight'])
arg_params['res5c_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res5c_branch2b_offset_bias'])
arg_params['res3b3_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res3b3_branch2b_offset_weight'])
arg_params['res3b3_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res3b3_branch2b_offset_bias'])
arg_params['res4b22_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res4b22_branch2b_offset_weight'])
arg_params['res4b22_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res4b22_branch2b_offset_bias'])
def init_weight_fpn(self, cfg, arg_params, aux_params):
arg_params['fpn_p6_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p6_weight'])
arg_params['fpn_p6_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p6_bias'])
arg_params['fpn_p5_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p5_weight'])
arg_params['fpn_p5_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p5_bias'])
arg_params['fpn_p4_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p4_weight'])
arg_params['fpn_p4_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p4_bias'])
arg_params['fpn_p3_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p3_weight'])
arg_params['fpn_p3_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p3_bias'])
arg_params['fpn_p2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p2_weight'])
arg_params['fpn_p2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p2_bias'])
arg_params['fpn_p5_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p5_1x1_weight'])
arg_params['fpn_p5_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p5_1x1_bias'])
arg_params['fpn_p4_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p4_1x1_weight'])
arg_params['fpn_p4_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p4_1x1_bias'])
arg_params['fpn_p3_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p3_1x1_weight'])
arg_params['fpn_p3_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p3_1x1_bias'])
arg_params['fpn_p2_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p2_1x1_weight'])
arg_params['fpn_p2_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p2_1x1_bias'])
def init_weight(self, cfg, arg_params, aux_params):
(arg_params2, aux_params2) = ({}, {})
for name in self.shared_param_list:
if ('offset' in name):
arg_params2[(name + '_weight')] = mx.nd.zeros(shape=self.arg_shape_dict[(name + '_weight')])
else:
arg_params2[(name + '_weight')] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict[(name + '_weight')])
arg_params2[(name + '_bias')] = mx.nd.zeros(shape=self.arg_shape_dict[(name + '_bias')])
self.init_deformable_convnet(cfg, arg_params2, aux_params2)
self.init_weight_rcnn(cfg, arg_params2, aux_params2)
self.init_weight_fpn(cfg, arg_params2, aux_params2)
for k in arg_params2:
if cfg.network.pretrained_weights_are_priority:
if ((k not in arg_params) or (arg_params[k].shape != arg_params2[k].shape)):
arg_params[k] = arg_params2[k]
else:
arg_params[k] = arg_params2[k]
for k in aux_params2:
if cfg.network.pretrained_weights_are_priority:
if (k not in aux_params):
aux_params[k] = aux_params2[k]
else:
aux_params[k] = aux_params2[k]
|
class resnet_v1_101_fpn_rcnn(Symbol):
def __init__(self):
'\n Use __init__ to define parameter network needs\n '
self.shared_param_list = ['rpn_conv', 'rpn_cls_score', 'rpn_bbox_pred']
self.shared_param_dict = {}
for name in self.shared_param_list:
self.shared_param_dict[(name + '_weight')] = mx.sym.Variable((name + '_weight'))
self.shared_param_dict[(name + '_bias')] = mx.sym.Variable((name + '_bias'))
def get_resnet_backbone(self, data, with_dilated=False, with_dconv=False, with_dpyramid=False, eps=1e-05):
conv1 = mx.symbol.Convolution(name='conv1', data=data, num_filter=64, pad=(3, 3), kernel=(7, 7), stride=(2, 2), no_bias=True)
bn_conv1 = mx.symbol.BatchNorm(name='bn_conv1', data=conv1, use_global_stats=True, fix_gamma=False, eps=eps)
scale_conv1 = bn_conv1
conv1_relu = mx.symbol.Activation(name='conv1_relu', data=scale_conv1, act_type='relu')
pool1 = mx.symbol.Pooling(name='pool1', data=conv1_relu, pooling_convention='full', pad=(0, 0), kernel=(3, 3), stride=(2, 2), pool_type='max')
res2a_branch1 = mx.symbol.Convolution(name='res2a_branch1', data=pool1, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2a_branch1 = mx.symbol.BatchNorm(name='bn2a_branch1', data=res2a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch1 = bn2a_branch1
res2a_branch2a = mx.symbol.Convolution(name='res2a_branch2a', data=pool1, num_filter=64, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2a_branch2a = mx.symbol.BatchNorm(name='bn2a_branch2a', data=res2a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch2a = bn2a_branch2a
res2a_branch2a_relu = mx.symbol.Activation(name='res2a_branch2a_relu', data=scale2a_branch2a, act_type='relu')
res2a_branch2b = mx.symbol.Convolution(name='res2a_branch2b', data=res2a_branch2a_relu, num_filter=64, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn2a_branch2b = mx.symbol.BatchNorm(name='bn2a_branch2b', data=res2a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch2b = bn2a_branch2b
res2a_branch2b_relu = mx.symbol.Activation(name='res2a_branch2b_relu', data=scale2a_branch2b, act_type='relu')
res2a_branch2c = mx.symbol.Convolution(name='res2a_branch2c', data=res2a_branch2b_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2a_branch2c = mx.symbol.BatchNorm(name='bn2a_branch2c', data=res2a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale2a_branch2c = bn2a_branch2c
res2a = mx.symbol.broadcast_add(*[scale2a_branch1, scale2a_branch2c], name='res2a')
res2a_relu = mx.symbol.Activation(name='res2a_relu', data=res2a, act_type='relu')
res2b_branch2a = mx.symbol.Convolution(name='res2b_branch2a', data=res2a_relu, num_filter=64, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2b_branch2a = mx.symbol.BatchNorm(name='bn2b_branch2a', data=res2b_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale2b_branch2a = bn2b_branch2a
res2b_branch2a_relu = mx.symbol.Activation(name='res2b_branch2a_relu', data=scale2b_branch2a, act_type='relu')
res2b_branch2b = mx.symbol.Convolution(name='res2b_branch2b', data=res2b_branch2a_relu, num_filter=64, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn2b_branch2b = mx.symbol.BatchNorm(name='bn2b_branch2b', data=res2b_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale2b_branch2b = bn2b_branch2b
res2b_branch2b_relu = mx.symbol.Activation(name='res2b_branch2b_relu', data=scale2b_branch2b, act_type='relu')
res2b_branch2c = mx.symbol.Convolution(name='res2b_branch2c', data=res2b_branch2b_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2b_branch2c = mx.symbol.BatchNorm(name='bn2b_branch2c', data=res2b_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale2b_branch2c = bn2b_branch2c
res2b = mx.symbol.broadcast_add(*[res2a_relu, scale2b_branch2c], name='res2b')
res2b_relu = mx.symbol.Activation(name='res2b_relu', data=res2b, act_type='relu')
res2c_branch2a = mx.symbol.Convolution(name='res2c_branch2a', data=res2b_relu, num_filter=64, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2c_branch2a = mx.symbol.BatchNorm(name='bn2c_branch2a', data=res2c_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale2c_branch2a = bn2c_branch2a
res2c_branch2a_relu = mx.symbol.Activation(name='res2c_branch2a_relu', data=scale2c_branch2a, act_type='relu')
res2c_branch2b = mx.symbol.Convolution(name='res2c_branch2b', data=res2c_branch2a_relu, num_filter=64, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn2c_branch2b = mx.symbol.BatchNorm(name='bn2c_branch2b', data=res2c_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale2c_branch2b = bn2c_branch2b
res2c_branch2b_relu = mx.symbol.Activation(name='res2c_branch2b_relu', data=scale2c_branch2b, act_type='relu')
res2c_branch2c = mx.symbol.Convolution(name='res2c_branch2c', data=res2c_branch2b_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn2c_branch2c = mx.symbol.BatchNorm(name='bn2c_branch2c', data=res2c_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale2c_branch2c = bn2c_branch2c
res2c = mx.symbol.broadcast_add(*[res2b_relu, scale2c_branch2c], name='res2c')
res2c_relu = mx.symbol.Activation(name='res2c_relu', data=res2c, act_type='relu')
res3a_branch1 = mx.symbol.Convolution(name='res3a_branch1', data=res2c_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn3a_branch1 = mx.symbol.BatchNorm(name='bn3a_branch1', data=res3a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch1 = bn3a_branch1
res3a_branch2a = mx.symbol.Convolution(name='res3a_branch2a', data=res2c_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn3a_branch2a = mx.symbol.BatchNorm(name='bn3a_branch2a', data=res3a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch2a = bn3a_branch2a
res3a_branch2a_relu = mx.symbol.Activation(name='res3a_branch2a_relu', data=scale3a_branch2a, act_type='relu')
res3a_branch2b = mx.symbol.Convolution(name='res3a_branch2b', data=res3a_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3a_branch2b = mx.symbol.BatchNorm(name='bn3a_branch2b', data=res3a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch2b = bn3a_branch2b
res3a_branch2b_relu = mx.symbol.Activation(name='res3a_branch2b_relu', data=scale3a_branch2b, act_type='relu')
res3a_branch2c = mx.symbol.Convolution(name='res3a_branch2c', data=res3a_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3a_branch2c = mx.symbol.BatchNorm(name='bn3a_branch2c', data=res3a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3a_branch2c = bn3a_branch2c
res3a = mx.symbol.broadcast_add(*[scale3a_branch1, scale3a_branch2c], name='res3a')
res3a_relu = mx.symbol.Activation(name='res3a_relu', data=res3a, act_type='relu')
res3b1_branch2a = mx.symbol.Convolution(name='res3b1_branch2a', data=res3a_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b1_branch2a = mx.symbol.BatchNorm(name='bn3b1_branch2a', data=res3b1_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b1_branch2a = bn3b1_branch2a
res3b1_branch2a_relu = mx.symbol.Activation(name='res3b1_branch2a_relu', data=scale3b1_branch2a, act_type='relu')
res3b1_branch2b = mx.symbol.Convolution(name='res3b1_branch2b', data=res3b1_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3b1_branch2b = mx.symbol.BatchNorm(name='bn3b1_branch2b', data=res3b1_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b1_branch2b = bn3b1_branch2b
res3b1_branch2b_relu = mx.symbol.Activation(name='res3b1_branch2b_relu', data=scale3b1_branch2b, act_type='relu')
res3b1_branch2c = mx.symbol.Convolution(name='res3b1_branch2c', data=res3b1_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b1_branch2c = mx.symbol.BatchNorm(name='bn3b1_branch2c', data=res3b1_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b1_branch2c = bn3b1_branch2c
res3b1 = mx.symbol.broadcast_add(*[res3a_relu, scale3b1_branch2c], name='res3b1')
res3b1_relu = mx.symbol.Activation(name='res3b1_relu', data=res3b1, act_type='relu')
res3b2_branch2a = mx.symbol.Convolution(name='res3b2_branch2a', data=res3b1_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b2_branch2a = mx.symbol.BatchNorm(name='bn3b2_branch2a', data=res3b2_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b2_branch2a = bn3b2_branch2a
res3b2_branch2a_relu = mx.symbol.Activation(name='res3b2_branch2a_relu', data=scale3b2_branch2a, act_type='relu')
res3b2_branch2b = mx.symbol.Convolution(name='res3b2_branch2b', data=res3b2_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3b2_branch2b = mx.symbol.BatchNorm(name='bn3b2_branch2b', data=res3b2_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b2_branch2b = bn3b2_branch2b
res3b2_branch2b_relu = mx.symbol.Activation(name='res3b2_branch2b_relu', data=scale3b2_branch2b, act_type='relu')
res3b2_branch2c = mx.symbol.Convolution(name='res3b2_branch2c', data=res3b2_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b2_branch2c = mx.symbol.BatchNorm(name='bn3b2_branch2c', data=res3b2_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b2_branch2c = bn3b2_branch2c
res3b2 = mx.symbol.broadcast_add(*[res3b1_relu, scale3b2_branch2c], name='res3b2')
res3b2_relu = mx.symbol.Activation(name='res3b2_relu', data=res3b2, act_type='relu')
res3b3_branch2a = mx.symbol.Convolution(name='res3b3_branch2a', data=res3b2_relu, num_filter=128, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b3_branch2a = mx.symbol.BatchNorm(name='bn3b3_branch2a', data=res3b3_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b3_branch2a = bn3b3_branch2a
res3b3_branch2a_relu = mx.symbol.Activation(name='res3b3_branch2a_relu', data=scale3b3_branch2a, act_type='relu')
if with_dpyramid:
res3b3_branch2b_offset = mx.symbol.Convolution(name='res3b3_branch2b_offset', data=res3b3_branch2a_relu, num_filter=72, pad=(1, 1), kernel=(3, 3), stride=(1, 1))
res3b3_branch2b = mx.contrib.symbol.DeformableConvolution(name='res3b3_branch2b', data=res3b3_branch2a_relu, offset=res3b3_branch2b_offset, num_filter=128, pad=(1, 1), kernel=(3, 3), num_deformable_group=4, stride=(1, 1), no_bias=True)
else:
res3b3_branch2b = mx.symbol.Convolution(name='res3b3_branch2b', data=res3b3_branch2a_relu, num_filter=128, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn3b3_branch2b = mx.symbol.BatchNorm(name='bn3b3_branch2b', data=res3b3_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b3_branch2b = bn3b3_branch2b
res3b3_branch2b_relu = mx.symbol.Activation(name='res3b3_branch2b_relu', data=scale3b3_branch2b, act_type='relu')
res3b3_branch2c = mx.symbol.Convolution(name='res3b3_branch2c', data=res3b3_branch2b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn3b3_branch2c = mx.symbol.BatchNorm(name='bn3b3_branch2c', data=res3b3_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale3b3_branch2c = bn3b3_branch2c
res3b3 = mx.symbol.broadcast_add(*[res3b2_relu, scale3b3_branch2c], name='res3b3')
res3b3_relu = mx.symbol.Activation(name='res3b3_relu', data=res3b3, act_type='relu')
res4a_branch1 = mx.symbol.Convolution(name='res4a_branch1', data=res3b3_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn4a_branch1 = mx.symbol.BatchNorm(name='bn4a_branch1', data=res4a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch1 = bn4a_branch1
res4a_branch2a = mx.symbol.Convolution(name='res4a_branch2a', data=res3b3_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(2, 2), no_bias=True)
bn4a_branch2a = mx.symbol.BatchNorm(name='bn4a_branch2a', data=res4a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch2a = bn4a_branch2a
res4a_branch2a_relu = mx.symbol.Activation(name='res4a_branch2a_relu', data=scale4a_branch2a, act_type='relu')
res4a_branch2b = mx.symbol.Convolution(name='res4a_branch2b', data=res4a_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4a_branch2b = mx.symbol.BatchNorm(name='bn4a_branch2b', data=res4a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch2b = bn4a_branch2b
res4a_branch2b_relu = mx.symbol.Activation(name='res4a_branch2b_relu', data=scale4a_branch2b, act_type='relu')
res4a_branch2c = mx.symbol.Convolution(name='res4a_branch2c', data=res4a_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4a_branch2c = mx.symbol.BatchNorm(name='bn4a_branch2c', data=res4a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4a_branch2c = bn4a_branch2c
res4a = mx.symbol.broadcast_add(*[scale4a_branch1, scale4a_branch2c], name='res4a')
res4a_relu = mx.symbol.Activation(name='res4a_relu', data=res4a, act_type='relu')
res4b1_branch2a = mx.symbol.Convolution(name='res4b1_branch2a', data=res4a_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b1_branch2a = mx.symbol.BatchNorm(name='bn4b1_branch2a', data=res4b1_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b1_branch2a = bn4b1_branch2a
res4b1_branch2a_relu = mx.symbol.Activation(name='res4b1_branch2a_relu', data=scale4b1_branch2a, act_type='relu')
res4b1_branch2b = mx.symbol.Convolution(name='res4b1_branch2b', data=res4b1_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b1_branch2b = mx.symbol.BatchNorm(name='bn4b1_branch2b', data=res4b1_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b1_branch2b = bn4b1_branch2b
res4b1_branch2b_relu = mx.symbol.Activation(name='res4b1_branch2b_relu', data=scale4b1_branch2b, act_type='relu')
res4b1_branch2c = mx.symbol.Convolution(name='res4b1_branch2c', data=res4b1_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b1_branch2c = mx.symbol.BatchNorm(name='bn4b1_branch2c', data=res4b1_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b1_branch2c = bn4b1_branch2c
res4b1 = mx.symbol.broadcast_add(*[res4a_relu, scale4b1_branch2c], name='res4b1')
res4b1_relu = mx.symbol.Activation(name='res4b1_relu', data=res4b1, act_type='relu')
res4b2_branch2a = mx.symbol.Convolution(name='res4b2_branch2a', data=res4b1_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b2_branch2a = mx.symbol.BatchNorm(name='bn4b2_branch2a', data=res4b2_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b2_branch2a = bn4b2_branch2a
res4b2_branch2a_relu = mx.symbol.Activation(name='res4b2_branch2a_relu', data=scale4b2_branch2a, act_type='relu')
res4b2_branch2b = mx.symbol.Convolution(name='res4b2_branch2b', data=res4b2_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b2_branch2b = mx.symbol.BatchNorm(name='bn4b2_branch2b', data=res4b2_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b2_branch2b = bn4b2_branch2b
res4b2_branch2b_relu = mx.symbol.Activation(name='res4b2_branch2b_relu', data=scale4b2_branch2b, act_type='relu')
res4b2_branch2c = mx.symbol.Convolution(name='res4b2_branch2c', data=res4b2_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b2_branch2c = mx.symbol.BatchNorm(name='bn4b2_branch2c', data=res4b2_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b2_branch2c = bn4b2_branch2c
res4b2 = mx.symbol.broadcast_add(*[res4b1_relu, scale4b2_branch2c], name='res4b2')
res4b2_relu = mx.symbol.Activation(name='res4b2_relu', data=res4b2, act_type='relu')
res4b3_branch2a = mx.symbol.Convolution(name='res4b3_branch2a', data=res4b2_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b3_branch2a = mx.symbol.BatchNorm(name='bn4b3_branch2a', data=res4b3_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b3_branch2a = bn4b3_branch2a
res4b3_branch2a_relu = mx.symbol.Activation(name='res4b3_branch2a_relu', data=scale4b3_branch2a, act_type='relu')
res4b3_branch2b = mx.symbol.Convolution(name='res4b3_branch2b', data=res4b3_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b3_branch2b = mx.symbol.BatchNorm(name='bn4b3_branch2b', data=res4b3_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b3_branch2b = bn4b3_branch2b
res4b3_branch2b_relu = mx.symbol.Activation(name='res4b3_branch2b_relu', data=scale4b3_branch2b, act_type='relu')
res4b3_branch2c = mx.symbol.Convolution(name='res4b3_branch2c', data=res4b3_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b3_branch2c = mx.symbol.BatchNorm(name='bn4b3_branch2c', data=res4b3_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b3_branch2c = bn4b3_branch2c
res4b3 = mx.symbol.broadcast_add(*[res4b2_relu, scale4b3_branch2c], name='res4b3')
res4b3_relu = mx.symbol.Activation(name='res4b3_relu', data=res4b3, act_type='relu')
res4b4_branch2a = mx.symbol.Convolution(name='res4b4_branch2a', data=res4b3_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b4_branch2a = mx.symbol.BatchNorm(name='bn4b4_branch2a', data=res4b4_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b4_branch2a = bn4b4_branch2a
res4b4_branch2a_relu = mx.symbol.Activation(name='res4b4_branch2a_relu', data=scale4b4_branch2a, act_type='relu')
res4b4_branch2b = mx.symbol.Convolution(name='res4b4_branch2b', data=res4b4_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b4_branch2b = mx.symbol.BatchNorm(name='bn4b4_branch2b', data=res4b4_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b4_branch2b = bn4b4_branch2b
res4b4_branch2b_relu = mx.symbol.Activation(name='res4b4_branch2b_relu', data=scale4b4_branch2b, act_type='relu')
res4b4_branch2c = mx.symbol.Convolution(name='res4b4_branch2c', data=res4b4_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b4_branch2c = mx.symbol.BatchNorm(name='bn4b4_branch2c', data=res4b4_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b4_branch2c = bn4b4_branch2c
res4b4 = mx.symbol.broadcast_add(*[res4b3_relu, scale4b4_branch2c], name='res4b4')
res4b4_relu = mx.symbol.Activation(name='res4b4_relu', data=res4b4, act_type='relu')
res4b5_branch2a = mx.symbol.Convolution(name='res4b5_branch2a', data=res4b4_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b5_branch2a = mx.symbol.BatchNorm(name='bn4b5_branch2a', data=res4b5_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b5_branch2a = bn4b5_branch2a
res4b5_branch2a_relu = mx.symbol.Activation(name='res4b5_branch2a_relu', data=scale4b5_branch2a, act_type='relu')
res4b5_branch2b = mx.symbol.Convolution(name='res4b5_branch2b', data=res4b5_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b5_branch2b = mx.symbol.BatchNorm(name='bn4b5_branch2b', data=res4b5_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b5_branch2b = bn4b5_branch2b
res4b5_branch2b_relu = mx.symbol.Activation(name='res4b5_branch2b_relu', data=scale4b5_branch2b, act_type='relu')
res4b5_branch2c = mx.symbol.Convolution(name='res4b5_branch2c', data=res4b5_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b5_branch2c = mx.symbol.BatchNorm(name='bn4b5_branch2c', data=res4b5_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b5_branch2c = bn4b5_branch2c
res4b5 = mx.symbol.broadcast_add(*[res4b4_relu, scale4b5_branch2c], name='res4b5')
res4b5_relu = mx.symbol.Activation(name='res4b5_relu', data=res4b5, act_type='relu')
res4b6_branch2a = mx.symbol.Convolution(name='res4b6_branch2a', data=res4b5_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b6_branch2a = mx.symbol.BatchNorm(name='bn4b6_branch2a', data=res4b6_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b6_branch2a = bn4b6_branch2a
res4b6_branch2a_relu = mx.symbol.Activation(name='res4b6_branch2a_relu', data=scale4b6_branch2a, act_type='relu')
res4b6_branch2b = mx.symbol.Convolution(name='res4b6_branch2b', data=res4b6_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b6_branch2b = mx.symbol.BatchNorm(name='bn4b6_branch2b', data=res4b6_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b6_branch2b = bn4b6_branch2b
res4b6_branch2b_relu = mx.symbol.Activation(name='res4b6_branch2b_relu', data=scale4b6_branch2b, act_type='relu')
res4b6_branch2c = mx.symbol.Convolution(name='res4b6_branch2c', data=res4b6_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b6_branch2c = mx.symbol.BatchNorm(name='bn4b6_branch2c', data=res4b6_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b6_branch2c = bn4b6_branch2c
res4b6 = mx.symbol.broadcast_add(*[res4b5_relu, scale4b6_branch2c], name='res4b6')
res4b6_relu = mx.symbol.Activation(name='res4b6_relu', data=res4b6, act_type='relu')
res4b7_branch2a = mx.symbol.Convolution(name='res4b7_branch2a', data=res4b6_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b7_branch2a = mx.symbol.BatchNorm(name='bn4b7_branch2a', data=res4b7_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b7_branch2a = bn4b7_branch2a
res4b7_branch2a_relu = mx.symbol.Activation(name='res4b7_branch2a_relu', data=scale4b7_branch2a, act_type='relu')
res4b7_branch2b = mx.symbol.Convolution(name='res4b7_branch2b', data=res4b7_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b7_branch2b = mx.symbol.BatchNorm(name='bn4b7_branch2b', data=res4b7_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b7_branch2b = bn4b7_branch2b
res4b7_branch2b_relu = mx.symbol.Activation(name='res4b7_branch2b_relu', data=scale4b7_branch2b, act_type='relu')
res4b7_branch2c = mx.symbol.Convolution(name='res4b7_branch2c', data=res4b7_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b7_branch2c = mx.symbol.BatchNorm(name='bn4b7_branch2c', data=res4b7_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b7_branch2c = bn4b7_branch2c
res4b7 = mx.symbol.broadcast_add(*[res4b6_relu, scale4b7_branch2c], name='res4b7')
res4b7_relu = mx.symbol.Activation(name='res4b7_relu', data=res4b7, act_type='relu')
res4b8_branch2a = mx.symbol.Convolution(name='res4b8_branch2a', data=res4b7_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b8_branch2a = mx.symbol.BatchNorm(name='bn4b8_branch2a', data=res4b8_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b8_branch2a = bn4b8_branch2a
res4b8_branch2a_relu = mx.symbol.Activation(name='res4b8_branch2a_relu', data=scale4b8_branch2a, act_type='relu')
res4b8_branch2b = mx.symbol.Convolution(name='res4b8_branch2b', data=res4b8_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b8_branch2b = mx.symbol.BatchNorm(name='bn4b8_branch2b', data=res4b8_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b8_branch2b = bn4b8_branch2b
res4b8_branch2b_relu = mx.symbol.Activation(name='res4b8_branch2b_relu', data=scale4b8_branch2b, act_type='relu')
res4b8_branch2c = mx.symbol.Convolution(name='res4b8_branch2c', data=res4b8_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b8_branch2c = mx.symbol.BatchNorm(name='bn4b8_branch2c', data=res4b8_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b8_branch2c = bn4b8_branch2c
res4b8 = mx.symbol.broadcast_add(*[res4b7_relu, scale4b8_branch2c], name='res4b8')
res4b8_relu = mx.symbol.Activation(name='res4b8_relu', data=res4b8, act_type='relu')
res4b9_branch2a = mx.symbol.Convolution(name='res4b9_branch2a', data=res4b8_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b9_branch2a = mx.symbol.BatchNorm(name='bn4b9_branch2a', data=res4b9_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b9_branch2a = bn4b9_branch2a
res4b9_branch2a_relu = mx.symbol.Activation(name='res4b9_branch2a_relu', data=scale4b9_branch2a, act_type='relu')
res4b9_branch2b = mx.symbol.Convolution(name='res4b9_branch2b', data=res4b9_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b9_branch2b = mx.symbol.BatchNorm(name='bn4b9_branch2b', data=res4b9_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b9_branch2b = bn4b9_branch2b
res4b9_branch2b_relu = mx.symbol.Activation(name='res4b9_branch2b_relu', data=scale4b9_branch2b, act_type='relu')
res4b9_branch2c = mx.symbol.Convolution(name='res4b9_branch2c', data=res4b9_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b9_branch2c = mx.symbol.BatchNorm(name='bn4b9_branch2c', data=res4b9_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b9_branch2c = bn4b9_branch2c
res4b9 = mx.symbol.broadcast_add(*[res4b8_relu, scale4b9_branch2c], name='res4b9')
res4b9_relu = mx.symbol.Activation(name='res4b9_relu', data=res4b9, act_type='relu')
res4b10_branch2a = mx.symbol.Convolution(name='res4b10_branch2a', data=res4b9_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b10_branch2a = mx.symbol.BatchNorm(name='bn4b10_branch2a', data=res4b10_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b10_branch2a = bn4b10_branch2a
res4b10_branch2a_relu = mx.symbol.Activation(name='res4b10_branch2a_relu', data=scale4b10_branch2a, act_type='relu')
res4b10_branch2b = mx.symbol.Convolution(name='res4b10_branch2b', data=res4b10_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b10_branch2b = mx.symbol.BatchNorm(name='bn4b10_branch2b', data=res4b10_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b10_branch2b = bn4b10_branch2b
res4b10_branch2b_relu = mx.symbol.Activation(name='res4b10_branch2b_relu', data=scale4b10_branch2b, act_type='relu')
res4b10_branch2c = mx.symbol.Convolution(name='res4b10_branch2c', data=res4b10_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b10_branch2c = mx.symbol.BatchNorm(name='bn4b10_branch2c', data=res4b10_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b10_branch2c = bn4b10_branch2c
res4b10 = mx.symbol.broadcast_add(*[res4b9_relu, scale4b10_branch2c], name='res4b10')
res4b10_relu = mx.symbol.Activation(name='res4b10_relu', data=res4b10, act_type='relu')
res4b11_branch2a = mx.symbol.Convolution(name='res4b11_branch2a', data=res4b10_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b11_branch2a = mx.symbol.BatchNorm(name='bn4b11_branch2a', data=res4b11_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b11_branch2a = bn4b11_branch2a
res4b11_branch2a_relu = mx.symbol.Activation(name='res4b11_branch2a_relu', data=scale4b11_branch2a, act_type='relu')
res4b11_branch2b = mx.symbol.Convolution(name='res4b11_branch2b', data=res4b11_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b11_branch2b = mx.symbol.BatchNorm(name='bn4b11_branch2b', data=res4b11_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b11_branch2b = bn4b11_branch2b
res4b11_branch2b_relu = mx.symbol.Activation(name='res4b11_branch2b_relu', data=scale4b11_branch2b, act_type='relu')
res4b11_branch2c = mx.symbol.Convolution(name='res4b11_branch2c', data=res4b11_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b11_branch2c = mx.symbol.BatchNorm(name='bn4b11_branch2c', data=res4b11_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b11_branch2c = bn4b11_branch2c
res4b11 = mx.symbol.broadcast_add(*[res4b10_relu, scale4b11_branch2c], name='res4b11')
res4b11_relu = mx.symbol.Activation(name='res4b11_relu', data=res4b11, act_type='relu')
res4b12_branch2a = mx.symbol.Convolution(name='res4b12_branch2a', data=res4b11_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b12_branch2a = mx.symbol.BatchNorm(name='bn4b12_branch2a', data=res4b12_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b12_branch2a = bn4b12_branch2a
res4b12_branch2a_relu = mx.symbol.Activation(name='res4b12_branch2a_relu', data=scale4b12_branch2a, act_type='relu')
res4b12_branch2b = mx.symbol.Convolution(name='res4b12_branch2b', data=res4b12_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b12_branch2b = mx.symbol.BatchNorm(name='bn4b12_branch2b', data=res4b12_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b12_branch2b = bn4b12_branch2b
res4b12_branch2b_relu = mx.symbol.Activation(name='res4b12_branch2b_relu', data=scale4b12_branch2b, act_type='relu')
res4b12_branch2c = mx.symbol.Convolution(name='res4b12_branch2c', data=res4b12_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b12_branch2c = mx.symbol.BatchNorm(name='bn4b12_branch2c', data=res4b12_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b12_branch2c = bn4b12_branch2c
res4b12 = mx.symbol.broadcast_add(*[res4b11_relu, scale4b12_branch2c], name='res4b12')
res4b12_relu = mx.symbol.Activation(name='res4b12_relu', data=res4b12, act_type='relu')
res4b13_branch2a = mx.symbol.Convolution(name='res4b13_branch2a', data=res4b12_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b13_branch2a = mx.symbol.BatchNorm(name='bn4b13_branch2a', data=res4b13_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b13_branch2a = bn4b13_branch2a
res4b13_branch2a_relu = mx.symbol.Activation(name='res4b13_branch2a_relu', data=scale4b13_branch2a, act_type='relu')
res4b13_branch2b = mx.symbol.Convolution(name='res4b13_branch2b', data=res4b13_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b13_branch2b = mx.symbol.BatchNorm(name='bn4b13_branch2b', data=res4b13_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b13_branch2b = bn4b13_branch2b
res4b13_branch2b_relu = mx.symbol.Activation(name='res4b13_branch2b_relu', data=scale4b13_branch2b, act_type='relu')
res4b13_branch2c = mx.symbol.Convolution(name='res4b13_branch2c', data=res4b13_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b13_branch2c = mx.symbol.BatchNorm(name='bn4b13_branch2c', data=res4b13_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b13_branch2c = bn4b13_branch2c
res4b13 = mx.symbol.broadcast_add(*[res4b12_relu, scale4b13_branch2c], name='res4b13')
res4b13_relu = mx.symbol.Activation(name='res4b13_relu', data=res4b13, act_type='relu')
res4b14_branch2a = mx.symbol.Convolution(name='res4b14_branch2a', data=res4b13_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b14_branch2a = mx.symbol.BatchNorm(name='bn4b14_branch2a', data=res4b14_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b14_branch2a = bn4b14_branch2a
res4b14_branch2a_relu = mx.symbol.Activation(name='res4b14_branch2a_relu', data=scale4b14_branch2a, act_type='relu')
res4b14_branch2b = mx.symbol.Convolution(name='res4b14_branch2b', data=res4b14_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b14_branch2b = mx.symbol.BatchNorm(name='bn4b14_branch2b', data=res4b14_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b14_branch2b = bn4b14_branch2b
res4b14_branch2b_relu = mx.symbol.Activation(name='res4b14_branch2b_relu', data=scale4b14_branch2b, act_type='relu')
res4b14_branch2c = mx.symbol.Convolution(name='res4b14_branch2c', data=res4b14_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b14_branch2c = mx.symbol.BatchNorm(name='bn4b14_branch2c', data=res4b14_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b14_branch2c = bn4b14_branch2c
res4b14 = mx.symbol.broadcast_add(*[res4b13_relu, scale4b14_branch2c], name='res4b14')
res4b14_relu = mx.symbol.Activation(name='res4b14_relu', data=res4b14, act_type='relu')
res4b15_branch2a = mx.symbol.Convolution(name='res4b15_branch2a', data=res4b14_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b15_branch2a = mx.symbol.BatchNorm(name='bn4b15_branch2a', data=res4b15_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b15_branch2a = bn4b15_branch2a
res4b15_branch2a_relu = mx.symbol.Activation(name='res4b15_branch2a_relu', data=scale4b15_branch2a, act_type='relu')
res4b15_branch2b = mx.symbol.Convolution(name='res4b15_branch2b', data=res4b15_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b15_branch2b = mx.symbol.BatchNorm(name='bn4b15_branch2b', data=res4b15_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b15_branch2b = bn4b15_branch2b
res4b15_branch2b_relu = mx.symbol.Activation(name='res4b15_branch2b_relu', data=scale4b15_branch2b, act_type='relu')
res4b15_branch2c = mx.symbol.Convolution(name='res4b15_branch2c', data=res4b15_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b15_branch2c = mx.symbol.BatchNorm(name='bn4b15_branch2c', data=res4b15_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b15_branch2c = bn4b15_branch2c
res4b15 = mx.symbol.broadcast_add(*[res4b14_relu, scale4b15_branch2c], name='res4b15')
res4b15_relu = mx.symbol.Activation(name='res4b15_relu', data=res4b15, act_type='relu')
res4b16_branch2a = mx.symbol.Convolution(name='res4b16_branch2a', data=res4b15_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b16_branch2a = mx.symbol.BatchNorm(name='bn4b16_branch2a', data=res4b16_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b16_branch2a = bn4b16_branch2a
res4b16_branch2a_relu = mx.symbol.Activation(name='res4b16_branch2a_relu', data=scale4b16_branch2a, act_type='relu')
res4b16_branch2b = mx.symbol.Convolution(name='res4b16_branch2b', data=res4b16_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b16_branch2b = mx.symbol.BatchNorm(name='bn4b16_branch2b', data=res4b16_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b16_branch2b = bn4b16_branch2b
res4b16_branch2b_relu = mx.symbol.Activation(name='res4b16_branch2b_relu', data=scale4b16_branch2b, act_type='relu')
res4b16_branch2c = mx.symbol.Convolution(name='res4b16_branch2c', data=res4b16_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b16_branch2c = mx.symbol.BatchNorm(name='bn4b16_branch2c', data=res4b16_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b16_branch2c = bn4b16_branch2c
res4b16 = mx.symbol.broadcast_add(*[res4b15_relu, scale4b16_branch2c], name='res4b16')
res4b16_relu = mx.symbol.Activation(name='res4b16_relu', data=res4b16, act_type='relu')
res4b17_branch2a = mx.symbol.Convolution(name='res4b17_branch2a', data=res4b16_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b17_branch2a = mx.symbol.BatchNorm(name='bn4b17_branch2a', data=res4b17_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b17_branch2a = bn4b17_branch2a
res4b17_branch2a_relu = mx.symbol.Activation(name='res4b17_branch2a_relu', data=scale4b17_branch2a, act_type='relu')
res4b17_branch2b = mx.symbol.Convolution(name='res4b17_branch2b', data=res4b17_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b17_branch2b = mx.symbol.BatchNorm(name='bn4b17_branch2b', data=res4b17_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b17_branch2b = bn4b17_branch2b
res4b17_branch2b_relu = mx.symbol.Activation(name='res4b17_branch2b_relu', data=scale4b17_branch2b, act_type='relu')
res4b17_branch2c = mx.symbol.Convolution(name='res4b17_branch2c', data=res4b17_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b17_branch2c = mx.symbol.BatchNorm(name='bn4b17_branch2c', data=res4b17_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b17_branch2c = bn4b17_branch2c
res4b17 = mx.symbol.broadcast_add(*[res4b16_relu, scale4b17_branch2c], name='res4b17')
res4b17_relu = mx.symbol.Activation(name='res4b17_relu', data=res4b17, act_type='relu')
res4b18_branch2a = mx.symbol.Convolution(name='res4b18_branch2a', data=res4b17_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b18_branch2a = mx.symbol.BatchNorm(name='bn4b18_branch2a', data=res4b18_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b18_branch2a = bn4b18_branch2a
res4b18_branch2a_relu = mx.symbol.Activation(name='res4b18_branch2a_relu', data=scale4b18_branch2a, act_type='relu')
res4b18_branch2b = mx.symbol.Convolution(name='res4b18_branch2b', data=res4b18_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b18_branch2b = mx.symbol.BatchNorm(name='bn4b18_branch2b', data=res4b18_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b18_branch2b = bn4b18_branch2b
res4b18_branch2b_relu = mx.symbol.Activation(name='res4b18_branch2b_relu', data=scale4b18_branch2b, act_type='relu')
res4b18_branch2c = mx.symbol.Convolution(name='res4b18_branch2c', data=res4b18_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b18_branch2c = mx.symbol.BatchNorm(name='bn4b18_branch2c', data=res4b18_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b18_branch2c = bn4b18_branch2c
res4b18 = mx.symbol.broadcast_add(*[res4b17_relu, scale4b18_branch2c], name='res4b18')
res4b18_relu = mx.symbol.Activation(name='res4b18_relu', data=res4b18, act_type='relu')
res4b19_branch2a = mx.symbol.Convolution(name='res4b19_branch2a', data=res4b18_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b19_branch2a = mx.symbol.BatchNorm(name='bn4b19_branch2a', data=res4b19_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b19_branch2a = bn4b19_branch2a
res4b19_branch2a_relu = mx.symbol.Activation(name='res4b19_branch2a_relu', data=scale4b19_branch2a, act_type='relu')
res4b19_branch2b = mx.symbol.Convolution(name='res4b19_branch2b', data=res4b19_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b19_branch2b = mx.symbol.BatchNorm(name='bn4b19_branch2b', data=res4b19_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b19_branch2b = bn4b19_branch2b
res4b19_branch2b_relu = mx.symbol.Activation(name='res4b19_branch2b_relu', data=scale4b19_branch2b, act_type='relu')
res4b19_branch2c = mx.symbol.Convolution(name='res4b19_branch2c', data=res4b19_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b19_branch2c = mx.symbol.BatchNorm(name='bn4b19_branch2c', data=res4b19_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b19_branch2c = bn4b19_branch2c
res4b19 = mx.symbol.broadcast_add(*[res4b18_relu, scale4b19_branch2c], name='res4b19')
res4b19_relu = mx.symbol.Activation(name='res4b19_relu', data=res4b19, act_type='relu')
res4b20_branch2a = mx.symbol.Convolution(name='res4b20_branch2a', data=res4b19_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b20_branch2a = mx.symbol.BatchNorm(name='bn4b20_branch2a', data=res4b20_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b20_branch2a = bn4b20_branch2a
res4b20_branch2a_relu = mx.symbol.Activation(name='res4b20_branch2a_relu', data=scale4b20_branch2a, act_type='relu')
res4b20_branch2b = mx.symbol.Convolution(name='res4b20_branch2b', data=res4b20_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b20_branch2b = mx.symbol.BatchNorm(name='bn4b20_branch2b', data=res4b20_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b20_branch2b = bn4b20_branch2b
res4b20_branch2b_relu = mx.symbol.Activation(name='res4b20_branch2b_relu', data=scale4b20_branch2b, act_type='relu')
res4b20_branch2c = mx.symbol.Convolution(name='res4b20_branch2c', data=res4b20_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b20_branch2c = mx.symbol.BatchNorm(name='bn4b20_branch2c', data=res4b20_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b20_branch2c = bn4b20_branch2c
res4b20 = mx.symbol.broadcast_add(*[res4b19_relu, scale4b20_branch2c], name='res4b20')
res4b20_relu = mx.symbol.Activation(name='res4b20_relu', data=res4b20, act_type='relu')
res4b21_branch2a = mx.symbol.Convolution(name='res4b21_branch2a', data=res4b20_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b21_branch2a = mx.symbol.BatchNorm(name='bn4b21_branch2a', data=res4b21_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b21_branch2a = bn4b21_branch2a
res4b21_branch2a_relu = mx.symbol.Activation(name='res4b21_branch2a_relu', data=scale4b21_branch2a, act_type='relu')
res4b21_branch2b = mx.symbol.Convolution(name='res4b21_branch2b', data=res4b21_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b21_branch2b = mx.symbol.BatchNorm(name='bn4b21_branch2b', data=res4b21_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b21_branch2b = bn4b21_branch2b
res4b21_branch2b_relu = mx.symbol.Activation(name='res4b21_branch2b_relu', data=scale4b21_branch2b, act_type='relu')
res4b21_branch2c = mx.symbol.Convolution(name='res4b21_branch2c', data=res4b21_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b21_branch2c = mx.symbol.BatchNorm(name='bn4b21_branch2c', data=res4b21_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b21_branch2c = bn4b21_branch2c
res4b21 = mx.symbol.broadcast_add(*[res4b20_relu, scale4b21_branch2c], name='res4b21')
res4b21_relu = mx.symbol.Activation(name='res4b21_relu', data=res4b21, act_type='relu')
res4b22_branch2a = mx.symbol.Convolution(name='res4b22_branch2a', data=res4b21_relu, num_filter=256, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b22_branch2a = mx.symbol.BatchNorm(name='bn4b22_branch2a', data=res4b22_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b22_branch2a = bn4b22_branch2a
res4b22_branch2a_relu = mx.symbol.Activation(name='res4b22_branch2a_relu', data=scale4b22_branch2a, act_type='relu')
if with_dpyramid:
res4b22_branch2b_offset = mx.symbol.Convolution(name='res4b22_branch2b_offset', data=res4b22_branch2a_relu, num_filter=72, pad=(1, 1), kernel=(3, 3), stride=(1, 1))
res4b22_branch2b = mx.contrib.symbol.DeformableConvolution(name='res4b22_branch2b', data=res4b22_branch2a_relu, offset=res4b22_branch2b_offset, num_filter=256, pad=(1, 1), kernel=(3, 3), num_deformable_group=4, stride=(1, 1), no_bias=True)
else:
res4b22_branch2b = mx.symbol.Convolution(name='res4b22_branch2b', data=res4b22_branch2a_relu, num_filter=256, pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
bn4b22_branch2b = mx.symbol.BatchNorm(name='bn4b22_branch2b', data=res4b22_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b22_branch2b = bn4b22_branch2b
res4b22_branch2b_relu = mx.symbol.Activation(name='res4b22_branch2b_relu', data=scale4b22_branch2b, act_type='relu')
res4b22_branch2c = mx.symbol.Convolution(name='res4b22_branch2c', data=res4b22_branch2b_relu, num_filter=1024, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn4b22_branch2c = mx.symbol.BatchNorm(name='bn4b22_branch2c', data=res4b22_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale4b22_branch2c = bn4b22_branch2c
res4b22 = mx.symbol.broadcast_add(*[res4b21_relu, scale4b22_branch2c], name='res4b22')
res4b22_relu = mx.symbol.Activation(name='res4b22_relu', data=res4b22, act_type='relu')
if with_dilated:
res5_stride = (1, 1)
res5_dilate = (2, 2)
else:
res5_stride = (2, 2)
res5_dilate = (1, 1)
res5a_branch2a = mx.symbol.Convolution(name='res5a_branch2a', data=res4b22_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=res5_stride, no_bias=True)
bn5a_branch2a = mx.symbol.BatchNorm(name='bn5a_branch2a', data=res5a_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch2a = bn5a_branch2a
res5a_branch2a_relu = mx.symbol.Activation(name='res5a_branch2a_relu', data=scale5a_branch2a, act_type='relu')
if with_dconv:
res5a_branch2b_offset = mx.symbol.Convolution(name='res5a_branch2b_offset', data=res5a_branch2a_relu, num_filter=72, pad=res5_dilate, kernel=(3, 3), dilate=res5_dilate)
res5a_branch2b = mx.contrib.symbol.DeformableConvolution(name='res5a_branch2b', data=res5a_branch2a_relu, offset=res5a_branch2b_offset, num_filter=512, pad=res5_dilate, kernel=(3, 3), num_deformable_group=4, stride=(1, 1), dilate=res5_dilate, no_bias=True)
else:
res5a_branch2b = mx.symbol.Convolution(name='res5a_branch2b', data=res5a_branch2a_relu, num_filter=512, pad=res5_dilate, kernel=(3, 3), stride=(1, 1), dilate=res5_dilate, no_bias=True)
bn5a_branch2b = mx.symbol.BatchNorm(name='bn5a_branch2b', data=res5a_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch2b = bn5a_branch2b
res5a_branch2b_relu = mx.symbol.Activation(name='res5a_branch2b_relu', data=scale5a_branch2b, act_type='relu')
res5a_branch2c = mx.symbol.Convolution(name='res5a_branch2c', data=res5a_branch2b_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5a_branch2c = mx.symbol.BatchNorm(name='bn5a_branch2c', data=res5a_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch2c = bn5a_branch2c
res5a_branch1 = mx.symbol.Convolution(name='res5a_branch1', data=res4b22_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=res5_stride, no_bias=True)
bn5a_branch1 = mx.symbol.BatchNorm(name='bn5a_branch1', data=res5a_branch1, use_global_stats=True, fix_gamma=False, eps=eps)
scale5a_branch1 = bn5a_branch1
res5a = mx.symbol.broadcast_add(*[scale5a_branch1, scale5a_branch2c], name='res5a')
res5a_relu = mx.symbol.Activation(name='res5a_relu', data=res5a, act_type='relu')
res5b_branch2a = mx.symbol.Convolution(name='res5b_branch2a', data=res5a_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5b_branch2a = mx.symbol.BatchNorm(name='bn5b_branch2a', data=res5b_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale5b_branch2a = bn5b_branch2a
res5b_branch2a_relu = mx.symbol.Activation(name='res5b_branch2a_relu', data=scale5b_branch2a, act_type='relu')
if with_dconv:
res5b_branch2b_offset = mx.symbol.Convolution(name='res5b_branch2b_offset', data=res5b_branch2a_relu, num_filter=72, pad=res5_dilate, kernel=(3, 3), dilate=res5_dilate)
res5b_branch2b = mx.contrib.symbol.DeformableConvolution(name='res5b_branch2b', data=res5b_branch2a_relu, offset=res5b_branch2b_offset, num_filter=512, pad=res5_dilate, kernel=(3, 3), num_deformable_group=4, dilate=res5_dilate, no_bias=True)
else:
res5b_branch2b = mx.symbol.Convolution(name='res5b_branch2b', data=res5b_branch2a_relu, num_filter=512, pad=res5_dilate, kernel=(3, 3), stride=(1, 1), dilate=res5_dilate, no_bias=True)
bn5b_branch2b = mx.symbol.BatchNorm(name='bn5b_branch2b', data=res5b_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale5b_branch2b = bn5b_branch2b
res5b_branch2b_relu = mx.symbol.Activation(name='res5b_branch2b_relu', data=scale5b_branch2b, act_type='relu')
res5b_branch2c = mx.symbol.Convolution(name='res5b_branch2c', data=res5b_branch2b_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5b_branch2c = mx.symbol.BatchNorm(name='bn5b_branch2c', data=res5b_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale5b_branch2c = bn5b_branch2c
res5b = mx.symbol.broadcast_add(*[res5a_relu, scale5b_branch2c], name='res5b')
res5b_relu = mx.symbol.Activation(name='res5b_relu', data=res5b, act_type='relu')
res5c_branch2a = mx.symbol.Convolution(name='res5c_branch2a', data=res5b_relu, num_filter=512, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5c_branch2a = mx.symbol.BatchNorm(name='bn5c_branch2a', data=res5c_branch2a, use_global_stats=True, fix_gamma=False, eps=eps)
scale5c_branch2a = bn5c_branch2a
res5c_branch2a_relu = mx.symbol.Activation(name='res5c_branch2a_relu', data=scale5c_branch2a, act_type='relu')
if with_dconv:
res5c_branch2b_offset = mx.symbol.Convolution(name='res5c_branch2b_offset', data=res5c_branch2a_relu, num_filter=72, pad=res5_dilate, kernel=(3, 3), dilate=res5_dilate)
res5c_branch2b = mx.contrib.symbol.DeformableConvolution(name='res5c_branch2b', data=res5c_branch2a_relu, offset=res5c_branch2b_offset, num_filter=512, pad=res5_dilate, kernel=(3, 3), num_deformable_group=4, dilate=res5_dilate, no_bias=True)
else:
res5c_branch2b = mx.symbol.Convolution(name='res5c_branch2b', data=res5c_branch2a_relu, num_filter=512, pad=res5_dilate, kernel=(3, 3), stride=(1, 1), dilate=res5_dilate, no_bias=True)
bn5c_branch2b = mx.symbol.BatchNorm(name='bn5c_branch2b', data=res5c_branch2b, use_global_stats=True, fix_gamma=False, eps=eps)
scale5c_branch2b = bn5c_branch2b
res5c_branch2b_relu = mx.symbol.Activation(name='res5c_branch2b_relu', data=scale5c_branch2b, act_type='relu')
res5c_branch2c = mx.symbol.Convolution(name='res5c_branch2c', data=res5c_branch2b_relu, num_filter=2048, pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
bn5c_branch2c = mx.symbol.BatchNorm(name='bn5c_branch2c', data=res5c_branch2c, use_global_stats=True, fix_gamma=False, eps=eps)
scale5c_branch2c = bn5c_branch2c
res5c = mx.symbol.broadcast_add(*[res5b_relu, scale5c_branch2c], name='res5c')
res5c_relu = mx.symbol.Activation(name='res5c_relu', data=res5c, act_type='relu')
return (res2c_relu, res3b3_relu, res4b22_relu, res5c_relu)
def get_fpn_feature(self, c2, c3, c4, c5, feature_dim=256):
fpn_p5_1x1 = mx.symbol.Convolution(data=c5, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p5_1x1')
fpn_p4_1x1 = mx.symbol.Convolution(data=c4, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p4_1x1')
fpn_p3_1x1 = mx.symbol.Convolution(data=c3, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p3_1x1')
fpn_p2_1x1 = mx.symbol.Convolution(data=c2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), num_filter=feature_dim, name='fpn_p2_1x1')
fpn_p5_upsample = mx.symbol.UpSampling(fpn_p5_1x1, scale=2, sample_type='nearest', name='fpn_p5_upsample')
fpn_p4_plus = mx.sym.ElementWiseSum(*[fpn_p5_upsample, fpn_p4_1x1], name='fpn_p4_sum')
fpn_p4_upsample = mx.symbol.UpSampling(fpn_p4_plus, scale=2, sample_type='nearest', name='fpn_p4_upsample')
fpn_p3_plus = mx.sym.ElementWiseSum(*[fpn_p4_upsample, fpn_p3_1x1], name='fpn_p3_sum')
fpn_p3_upsample = mx.symbol.UpSampling(fpn_p3_plus, scale=2, sample_type='nearest', name='fpn_p3_upsample')
fpn_p2_plus = mx.sym.ElementWiseSum(*[fpn_p3_upsample, fpn_p2_1x1], name='fpn_p2_sum')
fpn_p6 = mx.sym.Convolution(data=c5, kernel=(3, 3), pad=(1, 1), stride=(2, 2), num_filter=feature_dim, name='fpn_p6')
fpn_p5 = mx.symbol.Convolution(data=fpn_p5_1x1, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p5')
fpn_p4 = mx.symbol.Convolution(data=fpn_p4_plus, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p4')
fpn_p3 = mx.symbol.Convolution(data=fpn_p3_plus, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p3')
fpn_p2 = mx.symbol.Convolution(data=fpn_p2_plus, kernel=(3, 3), pad=(1, 1), stride=(1, 1), num_filter=feature_dim, name='fpn_p2')
return (fpn_p2, fpn_p3, fpn_p4, fpn_p5, fpn_p6)
def get_rpn_subnet(self, data, num_anchors, suffix):
rpn_conv = mx.sym.Convolution(data=data, kernel=(3, 3), pad=(1, 1), num_filter=512, name=('rpn_conv_' + suffix), weight=self.shared_param_dict['rpn_conv_weight'], bias=self.shared_param_dict['rpn_conv_bias'])
rpn_relu = mx.sym.Activation(data=rpn_conv, act_type='relu', name=('rpn_relu_' + suffix))
rpn_cls_score = mx.sym.Convolution(data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=(2 * num_anchors), name=('rpn_cls_score_' + suffix), weight=self.shared_param_dict['rpn_cls_score_weight'], bias=self.shared_param_dict['rpn_cls_score_bias'])
rpn_bbox_pred = mx.sym.Convolution(data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=(4 * num_anchors), name=('rpn_bbox_pred_' + suffix), weight=self.shared_param_dict['rpn_bbox_pred_weight'], bias=self.shared_param_dict['rpn_bbox_pred_bias'])
rpn_cls_score_t1 = mx.sym.Reshape(data=rpn_cls_score, shape=(0, 2, (- 1), 0), name=('rpn_cls_score_t1_' + suffix))
rpn_cls_score_t2 = mx.sym.Reshape(data=rpn_cls_score_t1, shape=(0, 2, (- 1)), name=('rpn_cls_score_t2_' + suffix))
rpn_cls_prob = mx.sym.SoftmaxActivation(data=rpn_cls_score_t1, mode='channel', name=('rpn_cls_prob_' + suffix))
rpn_cls_prob_t = mx.sym.Reshape(data=rpn_cls_prob, shape=(0, (2 * num_anchors), (- 1), 0), name=('rpn_cls_prob_t_' + suffix))
rpn_bbox_pred_t = mx.sym.Reshape(data=rpn_bbox_pred, shape=(0, 0, (- 1)), name=('rpn_bbox_pred_t_' + suffix))
return (rpn_cls_score_t2, rpn_cls_prob_t, rpn_bbox_pred_t, rpn_bbox_pred)
def get_symbol(self, cfg, is_train=True):
num_classes = cfg.dataset.NUM_CLASSES
num_reg_classes = (2 if cfg.CLASS_AGNOSTIC else num_classes)
data = mx.sym.Variable(name='data')
im_info = mx.sym.Variable(name='im_info')
(res2, res3, res4, res5) = self.get_resnet_backbone(data)
(fpn_p2, fpn_p3, fpn_p4, fpn_p5, fpn_p6) = self.get_fpn_feature(res2, res3, res4, res5)
(rpn_cls_score_p2, rpn_prob_p2, rpn_bbox_loss_p2, rpn_bbox_pred_p2) = self.get_rpn_subnet(fpn_p2, cfg.network.NUM_ANCHORS, 'p2')
(rpn_cls_score_p3, rpn_prob_p3, rpn_bbox_loss_p3, rpn_bbox_pred_p3) = self.get_rpn_subnet(fpn_p3, cfg.network.NUM_ANCHORS, 'p3')
(rpn_cls_score_p4, rpn_prob_p4, rpn_bbox_loss_p4, rpn_bbox_pred_p4) = self.get_rpn_subnet(fpn_p4, cfg.network.NUM_ANCHORS, 'p4')
(rpn_cls_score_p5, rpn_prob_p5, rpn_bbox_loss_p5, rpn_bbox_pred_p5) = self.get_rpn_subnet(fpn_p5, cfg.network.NUM_ANCHORS, 'p5')
(rpn_cls_score_p6, rpn_prob_p6, rpn_bbox_loss_p6, rpn_bbox_pred_p6) = self.get_rpn_subnet(fpn_p6, cfg.network.NUM_ANCHORS, 'p6')
rpn_cls_prob_dict = {'rpn_cls_prob_stride64': rpn_prob_p6, 'rpn_cls_prob_stride32': rpn_prob_p5, 'rpn_cls_prob_stride16': rpn_prob_p4, 'rpn_cls_prob_stride8': rpn_prob_p3, 'rpn_cls_prob_stride4': rpn_prob_p2}
rpn_bbox_pred_dict = {'rpn_bbox_pred_stride64': rpn_bbox_pred_p6, 'rpn_bbox_pred_stride32': rpn_bbox_pred_p5, 'rpn_bbox_pred_stride16': rpn_bbox_pred_p4, 'rpn_bbox_pred_stride8': rpn_bbox_pred_p3, 'rpn_bbox_pred_stride4': rpn_bbox_pred_p2}
arg_dict = dict((rpn_cls_prob_dict.items() + rpn_bbox_pred_dict.items()))
if is_train:
rpn_label = mx.sym.Variable(name='label')
rpn_bbox_target = mx.sym.Variable(name='bbox_target')
rpn_bbox_weight = mx.sym.Variable(name='bbox_weight')
gt_boxes = mx.sym.Variable(name='gt_boxes')
rpn_cls_score = mx.sym.Concat(rpn_cls_score_p2, rpn_cls_score_p3, rpn_cls_score_p4, rpn_cls_score_p5, rpn_cls_score_p6, dim=2)
rpn_bbox_loss = mx.sym.Concat(rpn_bbox_loss_p2, rpn_bbox_loss_p3, rpn_bbox_loss_p4, rpn_bbox_loss_p5, rpn_bbox_loss_p6, dim=2)
rpn_cls_output = mx.sym.SoftmaxOutput(data=rpn_cls_score, label=rpn_label, multi_output=True, normalization='valid', use_ignore=True, ignore_label=(- 1), name='rpn_cls_prob')
rpn_bbox_loss = (rpn_bbox_weight * mx.sym.smooth_l1(name='rpn_bbox_loss_l1', scalar=3.0, data=(rpn_bbox_loss - rpn_bbox_target)))
rpn_bbox_loss = mx.sym.MakeLoss(name='rpn_bbox_loss', data=rpn_bbox_loss, grad_scale=(1.0 / cfg.TRAIN.RPN_BATCH_SIZE))
aux_dict = {'op_type': 'pyramid_proposal', 'name': 'rois', 'im_info': im_info, 'feat_stride': tuple(cfg.network.RPN_FEAT_STRIDE), 'scales': tuple(cfg.network.ANCHOR_SCALES), 'ratios': tuple(cfg.network.ANCHOR_RATIOS), 'rpn_pre_nms_top_n': cfg.TRAIN.RPN_PRE_NMS_TOP_N, 'rpn_post_nms_top_n': cfg.TRAIN.RPN_POST_NMS_TOP_N, 'threshold': cfg.TRAIN.RPN_NMS_THRESH, 'rpn_min_size': cfg.TRAIN.RPN_MIN_SIZE}
rois = mx.sym.Custom(**dict((arg_dict.items() + aux_dict.items())))
gt_boxes_reshape = mx.sym.Reshape(data=gt_boxes, shape=((- 1), 5), name='gt_boxes_reshape')
(rois, label, bbox_target, bbox_weight) = mx.sym.Custom(rois=rois, gt_boxes=gt_boxes_reshape, op_type='proposal_target', num_classes=num_reg_classes, batch_images=cfg.TRAIN.BATCH_IMAGES, batch_rois=cfg.TRAIN.BATCH_ROIS, cfg=cPickle.dumps(cfg), fg_fraction=cfg.TRAIN.FG_FRACTION)
else:
aux_dict = {'op_type': 'pyramid_proposal', 'name': 'rois', 'im_info': im_info, 'feat_stride': tuple(cfg.network.RPN_FEAT_STRIDE), 'scales': tuple(cfg.network.ANCHOR_SCALES), 'ratios': tuple(cfg.network.ANCHOR_RATIOS), 'rpn_pre_nms_top_n': cfg.TEST.RPN_PRE_NMS_TOP_N, 'rpn_post_nms_top_n': cfg.TEST.RPN_POST_NMS_TOP_N, 'threshold': cfg.TEST.RPN_NMS_THRESH, 'rpn_min_size': cfg.TEST.RPN_MIN_SIZE}
rois = mx.sym.Custom(**dict((arg_dict.items() + aux_dict.items())))
roi_pool = mx.symbol.Custom(data_p2=fpn_p2, data_p3=fpn_p3, data_p4=fpn_p4, data_p5=fpn_p5, rois=rois, op_type='fpn_roi_pooling', name='fpn_roi_pooling')
fc_new_1 = mx.symbol.FullyConnected(name='fc_new_1', data=roi_pool, num_hidden=1024)
fc_new_1_relu = mx.sym.Activation(data=fc_new_1, act_type='relu', name='fc_new_1_relu')
fc_new_2 = mx.symbol.FullyConnected(name='fc_new_2', data=fc_new_1_relu, num_hidden=1024)
fc_new_2_relu = mx.sym.Activation(data=fc_new_2, act_type='relu', name='fc_new_2_relu')
cls_score = mx.symbol.FullyConnected(name='cls_score', data=fc_new_2_relu, num_hidden=num_classes)
bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=fc_new_2_relu, num_hidden=(num_reg_classes * 4))
if is_train:
if cfg.TRAIN.ENABLE_OHEM:
(labels_ohem, bbox_weights_ohem) = mx.sym.Custom(op_type='BoxAnnotatorOHEM', num_classes=num_classes, num_reg_classes=num_reg_classes, roi_per_img=cfg.TRAIN.BATCH_ROIS_OHEM, cls_score=cls_score, bbox_pred=bbox_pred, labels=label, bbox_targets=bbox_target, bbox_weights=bbox_weight)
cls_prob = mx.sym.SoftmaxOutput(name='cls_prob', data=cls_score, label=labels_ohem, normalization='valid', use_ignore=True, ignore_label=(- 1))
bbox_loss_ = (bbox_weights_ohem * mx.sym.smooth_l1(name='bbox_loss_', scalar=1.0, data=(bbox_pred - bbox_target)))
bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=(1.0 / cfg.TRAIN.BATCH_ROIS_OHEM))
rcnn_label = labels_ohem
else:
cls_prob = mx.sym.SoftmaxOutput(name='cls_prob', data=cls_score, label=label, normalization='valid')
bbox_loss_ = (bbox_weight * mx.sym.smooth_l1(name='bbox_loss_', scalar=1.0, data=(bbox_pred - bbox_target)))
bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=(1.0 / cfg.TRAIN.BATCH_ROIS))
rcnn_label = label
rcnn_label = mx.sym.Reshape(data=rcnn_label, shape=(cfg.TRAIN.BATCH_IMAGES, (- 1)), name='label_reshape')
cls_prob = mx.sym.Reshape(data=cls_prob, shape=(cfg.TRAIN.BATCH_IMAGES, (- 1), num_classes), name='cls_prob_reshape')
bbox_loss = mx.sym.Reshape(data=bbox_loss, shape=(cfg.TRAIN.BATCH_IMAGES, (- 1), (4 * num_reg_classes)), name='bbox_loss_reshape')
group = mx.sym.Group([rpn_cls_output, rpn_bbox_loss, cls_prob, bbox_loss, mx.sym.BlockGrad(rcnn_label)])
else:
cls_prob = mx.sym.SoftmaxActivation(name='cls_prob', data=cls_score)
cls_prob = mx.sym.Reshape(data=cls_prob, shape=(cfg.TEST.BATCH_IMAGES, (- 1), num_classes), name='cls_prob_reshape')
bbox_pred = mx.sym.Reshape(data=bbox_pred, shape=(cfg.TEST.BATCH_IMAGES, (- 1), (4 * num_reg_classes)), name='bbox_pred_reshape')
group = mx.sym.Group([rois, cls_prob, bbox_pred])
self.sym = group
return group
def init_weight_rcnn(self, cfg, arg_params, aux_params):
arg_params['fc_new_1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_1_weight'])
arg_params['fc_new_1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_1_bias'])
arg_params['fc_new_2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_2_weight'])
arg_params['fc_new_2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_2_bias'])
arg_params['cls_score_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['cls_score_weight'])
arg_params['cls_score_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['cls_score_bias'])
arg_params['bbox_pred_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['bbox_pred_weight'])
arg_params['bbox_pred_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['bbox_pred_bias'])
def init_weight_fpn(self, cfg, arg_params, aux_params):
arg_params['fpn_p6_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p6_weight'])
arg_params['fpn_p6_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p6_bias'])
arg_params['fpn_p5_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p5_weight'])
arg_params['fpn_p5_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p5_bias'])
arg_params['fpn_p4_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p4_weight'])
arg_params['fpn_p4_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p4_bias'])
arg_params['fpn_p3_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p3_weight'])
arg_params['fpn_p3_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p3_bias'])
arg_params['fpn_p2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p2_weight'])
arg_params['fpn_p2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p2_bias'])
arg_params['fpn_p5_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p5_1x1_weight'])
arg_params['fpn_p5_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p5_1x1_bias'])
arg_params['fpn_p4_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p4_1x1_weight'])
arg_params['fpn_p4_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p4_1x1_bias'])
arg_params['fpn_p3_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p3_1x1_weight'])
arg_params['fpn_p3_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p3_1x1_bias'])
arg_params['fpn_p2_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p2_1x1_weight'])
arg_params['fpn_p2_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p2_1x1_bias'])
def init_weight(self, cfg, arg_params, aux_params):
for name in self.shared_param_list:
arg_params[(name + '_weight')] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict[(name + '_weight')])
arg_params[(name + '_bias')] = mx.nd.zeros(shape=self.arg_shape_dict[(name + '_bias')])
self.init_weight_rcnn(cfg, arg_params, aux_params)
self.init_weight_fpn(cfg, arg_params, aux_params)
|
def customize_compiler_for_nvcc(self):
"inject deep into distutils to customize how the dispatch\n to gcc/nvcc works.\n If you subclass UnixCCompiler, it's not trivial to get your subclass\n injected in, and still have the right customizations (i.e.\n distutils.sysconfig.customize_compiler) run on it. So instead of going\n the OO route, I have this. Note, it's kindof like a wierd functional\n subclassing going on."
self.src_extensions.append('.cu')
default_compiler_so = self.compiler_so
super = self._compile
def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts):
if (os.path.splitext(src)[1] == '.cu'):
self.set_executable('compiler_so', CUDA['nvcc'])
postargs = extra_postargs['nvcc']
else:
postargs = extra_postargs['gcc']
super(obj, src, ext, cc_args, postargs, pp_opts)
self.compiler_so = default_compiler_so
self._compile = _compile
|
class custom_build_ext(build_ext):
def build_extensions(self):
customize_compiler_for_nvcc(self.compiler)
build_ext.build_extensions(self)
|
def find_in_path(name, path):
'Find a file in a search path'
for dir in path.split(os.pathsep):
binpath = pjoin(dir, name)
if os.path.exists(binpath):
return os.path.abspath(binpath)
return None
|
def locate_cuda():
"Locate the CUDA environment on the system\n Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64'\n and values giving the absolute path to each directory.\n Starts by looking for the CUDAHOME env variable. If not found, everything\n is based on finding 'nvcc' in the PATH.\n "
if ('CUDAHOME' in os.environ):
home = os.environ['CUDAHOME']
nvcc = pjoin(home, 'bin', 'nvcc')
else:
default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin')
nvcc = find_in_path('nvcc', ((os.environ['PATH'] + os.pathsep) + default_path))
if (nvcc is None):
raise EnvironmentError('The nvcc binary could not be located in your $PATH. Either add it to your path, or set $CUDAHOME')
home = os.path.dirname(os.path.dirname(nvcc))
cudaconfig = {'home': home, 'nvcc': nvcc, 'include': pjoin(home, 'include'), 'lib64': pjoin(home, 'lib64')}
for (k, v) in cudaconfig.iteritems():
if (not os.path.exists(v)):
raise EnvironmentError(('The CUDA %s path could not be located in %s' % (k, v)))
return cudaconfig
|
def customize_compiler_for_nvcc(self):
"inject deep into distutils to customize how the dispatch\n to gcc/nvcc works.\n If you subclass UnixCCompiler, it's not trivial to get your subclass\n injected in, and still have the right customizations (i.e.\n distutils.sysconfig.customize_compiler) run on it. So instead of going\n the OO route, I have this. Note, it's kindof like a wierd functional\n subclassing going on."
self.src_extensions.append('.cu')
default_compiler_so = self.compiler_so
super = self._compile
def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts):
if (os.path.splitext(src)[1] == '.cu'):
self.set_executable('compiler_so', CUDA['nvcc'])
postargs = extra_postargs['nvcc']
else:
postargs = extra_postargs['gcc']
super(obj, src, ext, cc_args, postargs, pp_opts)
self.compiler_so = default_compiler_so
self._compile = _compile
|
class custom_build_ext(build_ext):
def build_extensions(self):
customize_compiler_for_nvcc(self.compiler)
build_ext.build_extensions(self)
|
def find_in_path(name, path):
'Find a file in a search path'
for dir in path.split(os.pathsep):
binpath = pjoin(dir, name)
if os.path.exists(binpath):
return os.path.abspath(binpath)
return None
|
def locate_cuda():
"Locate the CUDA environment on the system\n\n Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64'\n and values giving the absolute path to each directory.\n\n Starts by looking for the CUDAHOME env variable. If not found, everything\n is based on finding 'nvcc' in the PATH.\n "
if ('CUDA_PATH' in os.environ):
home = os.environ['CUDA_PATH']
print(('home = %s\n' % home))
nvcc = pjoin(home, 'bin', nvcc_bin)
else:
default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin')
nvcc = find_in_path(nvcc_bin, ((os.environ['PATH'] + os.pathsep) + default_path))
if (nvcc is None):
raise EnvironmentError('The nvcc binary could not be located in your $PATH. Either add it to your path, or set $CUDA_PATH')
home = os.path.dirname(os.path.dirname(nvcc))
print(('home = %s, nvcc = %s\n' % (home, nvcc)))
cudaconfig = {'home': home, 'nvcc': nvcc, 'include': pjoin(home, 'include'), 'lib64': pjoin(home, lib_dir)}
for (k, v) in cudaconfig.iteritems():
if (not os.path.exists(v)):
raise EnvironmentError(('The CUDA %s path could not be located in %s' % (k, v)))
return cudaconfig
|
def customize_compiler_for_nvcc(self):
"inject deep into distutils to customize how the dispatch\n to gcc/nvcc works.\n\n If you subclass UnixCCompiler, it's not trivial to get your subclass\n injected in, and still have the right customizations (i.e.\n distutils.sysconfig.customize_compiler) run on it. So instead of going\n the OO route, I have this. Note, it's kindof like a wierd functional\n subclassing going on."
super = self.compile
def compile(sources, output_dir=None, macros=None, include_dirs=None, debug=0, extra_preargs=None, extra_postargs=None, depends=None):
postfix = os.path.splitext(sources[0])[1]
if (postfix == '.cu'):
postargs = extra_postargs['nvcc']
else:
postargs = extra_postargs['gcc']
return super(sources, output_dir, macros, include_dirs, debug, extra_preargs, postargs, depends)
self.compile = compile
|
class custom_build_ext(build_ext):
def build_extensions(self):
customize_compiler_for_nvcc(self.compiler)
build_ext.build_extensions(self)
|
class CUDA_build_ext(build_ext):
'\n Custom build_ext command that compiles CUDA files.\n Note that all extension source files will be processed with this compiler.\n '
def build_extensions(self):
self.compiler.src_extensions.append('.cu')
self.compiler.set_executable('compiler_so', 'nvcc')
self.compiler.set_executable('linker_so', 'nvcc --shared')
if hasattr(self.compiler, '_c_extensions'):
self.compiler._c_extensions.append('.cu')
self.compiler.spawn = self.spawn
build_ext.build_extensions(self)
def spawn(self, cmd, search_path=1, verbose=0, dry_run=0):
'\n Perform any CUDA specific customizations before actually launching\n compile/link etc. commands.\n '
if ((sys.platform == 'darwin') and (len(cmd) >= 2) and (cmd[0] == 'nvcc') and (cmd[1] == '--shared') and (cmd.count('-arch') > 0)):
while True:
try:
index = cmd.index('-arch')
del cmd[index:(index + 2)]
except ValueError:
break
elif (self.compiler.compiler_type == 'msvc'):
cmd[:1] = ['nvcc', '--compiler-bindir', (os.path.dirname(find_executable('cl.exe', PATH)) or cmd[0])]
for (idx, c) in enumerate(cmd):
if (c == '/c'):
cmd[idx] = '-c'
elif (c == '/DLL'):
cmd[idx] = '--shared'
elif ('-fPIC' in c):
del cmd[idx]
elif c.startswith('/Tc'):
cmd[idx] = c[3:]
elif c.startswith('/Fo'):
cmd[idx:(idx + 1)] = ['-o', c[3:]]
elif c.startswith('/LIBPATH:'):
cmd[idx] = ('-L' + c[9:])
elif c.startswith('/OUT:'):
cmd[idx:(idx + 1)] = ['-o', c[5:]]
elif c.startswith('/EXPORT:'):
del cmd[idx]
elif (c == 'cublas.lib'):
cmd[idx] = '-lcublas'
if ('--shared' in cmd):
pass_on = '--linker-options='
cmd.append('/NODEFAULTLIB:libcmt.lib')
else:
pass_on = '--compiler-options='
cmd = ([c for c in cmd if (c[0] != '/')] + [(pass_on + ','.join((c for c in cmd if (c[0] == '/'))))])
spawn(cmd, search_path, verbose, dry_run)
|
def get_segmentation_test_batch(segdb, config):
"\n return a dict of train batch\n :param segdb: ['image', 'flipped']\n :param config: the config setting\n :return: data, label, im_info\n "
(imgs, seg_cls_gts, segdb) = get_segmentation_image(segdb, config)
im_array = imgs
im_info = [np.array([segdb[i]['im_info']], dtype=np.float32) for i in xrange(len(segdb))]
data = [{'data': im_array[i], 'im_info': im_info[i]} for i in xrange(len(segdb))]
label = [{'label': seg_cls_gts[i]} for i in xrange(len(segdb))]
return (data, label, im_info)
|
def get_segmentation_train_batch(segdb, config):
"\n return a dict of train batch\n :param segdb: ['image', 'flipped']\n :param config: the config setting\n :return: data, label, im_info\n "
assert (len(segdb) == 1), 'Single batch only'
(imgs, seg_cls_gts, segdb) = get_segmentation_image(segdb, config)
im_array = imgs[0]
seg_cls_gt = seg_cls_gts[0]
im_info = np.array([segdb[0]['im_info']], dtype=np.float32)
data = {'data': im_array, 'im_info': im_info}
label = {'label': seg_cls_gt}
return (data, label)
|
def file_lines_to_list(path):
with open(path) as f:
content = f.readlines()
content = [x.strip() for x in content]
return content
|
class Pred():
def __init__(self, id, conf, left, top, right, bottom):
self.id = id
self.conf = conf
self.left = left
self.top = top
self.right = right
self.bottom = bottom
def calc_pred_intersection(self, pred):
if (not (self.id == pred.id)):
return 0
left = max(self.left, pred.left)
right = min(self.right, pred.right)
if (left > right):
return 0
top = max(self.top, pred.top)
bottom = min(self.bottom, pred.bottom)
if (top > bottom):
return 0
return ((right - left) * (bottom - top))
|
def plot_preds(img, preds, clr=(255, 0, 255)):
if isinstance(img, str):
img = cv2.imread(img)
for pred in preds:
cv2.rectangle(img, (pred.left, pred.top), (pred.right, pred.bottom), clr)
cv2.putText(img, pred.id, (pred.left, pred.top), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
return img
|
def read_predictions(txt_file):
lines_list = file_lines_to_list(txt_file)
prediction = []
for line in lines_list:
(id, conf, left, top, right, bottom) = line.split(';')
pred = Pred(id, float(conf), int(left), int(top), int(right), int(bottom))
prediction.append(pred)
return prediction
|
class ObjDetStats():
def __init__(self, gt_roidb_fname, cat_ords, logger):
with open(gt_roidb_fname, 'rb') as fid:
self.roidb = cPickle.load(fid)
self.roidb_ni = {}
for entry in self.roidb:
image_path = entry['image']
(im_path, im_name) = os.path.split(image_path)
self.roidb_ni[im_name] = entry
self.cat_ords = cat_ords
self.logger = logger
def print_bboxes(self, dets_folder, query_images):
import cv2
from utils.show_boxes import show_dets_gt_boxes
for detsName in os.listdir(dets_folder):
if (detsName[(- 4):] == '.txt'):
detsPath = os.path.join(dets_folder, detsName)
imgName = detName_2_imgName(detsName)
entry = self.roidb_ni[imgName]
imgName = detName_2_imgName(detsName)
for q_im_path in query_images:
(_, q_im_name) = os.path.split(q_im_path)
if (q_im_name == imgName):
im = cv2.imread(q_im_path)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
dvisName = imgName_2_dvisName(imgName)
(q_dets, cat_names) = img_dets_CSV_2_A(detsPath, self.cat_ords)
show_dets_gt_boxes(im, q_dets, cat_names, entry['boxes'], entry['gt_names'], scale=1.0, FS=12, LW=1.5, save_file_path=os.path.join(dets_folder, dvisName))
break
def add_base_stats_inDomain(self, dets_folder, perf_stats, Nclasses=1, ovthresh=0.5):
for detsName in os.listdir(dets_folder):
if (detsName[(- 4):] == '.txt'):
(q_dets, cat_names) = img_dets_CSV_2_A(os.path.join(dets_folder, detsName), self.cat_ords)
imgName = detName_2_imgName(detsName)
entry = self.roidb_ni[imgName]
gt_boxes_test = []
gt_classes_test = []
for (gt_box, gt_class) in zip(entry['boxes'], entry['gt_classes']):
if (gt_class in self.cat_ords):
gt_boxes_test += [gt_box]
gt_classes_test += [gt_class]
gt_classes_test = np.asarray(gt_classes_test)
gt_boxes_test = np.asarray(gt_boxes_test)
perf_stats.comp_epi_stats_m(q_dets, gt_boxes_test, gt_classes_test, self.cat_ords, ovthresh)
perf_stats.print_perf(self.logger, prefix='_')
return perf_stats
|
class PrefetchingIter(mx.io.DataIter):
'Base class for prefetching iterators. Takes one or more DataIters (\n or any class with "reset" and "next" methods) and combine them with\n prefetching. For example:\n\n Parameters\n ----------\n iters : DataIter or list of DataIter\n one or more DataIters (or any class with "reset" and "next" methods)\n rename_data : None or list of dict\n i-th element is a renaming map for i-th iter, in the form of\n {\'original_name\' : \'new_name\'}. Should have one entry for each entry\n in iter[i].provide_data\n rename_label : None or list of dict\n Similar to rename_data\n\n Examples\n --------\n iter = PrefetchingIter([NDArrayIter({\'data\': X1}), NDArrayIter({\'data\': X2})],\n rename_data=[{\'data\': \'data1\'}, {\'data\': \'data2\'}])\n '
def __init__(self, iters, rename_data=None, rename_label=None):
super(PrefetchingIter, self).__init__()
if (not isinstance(iters, list)):
iters = [iters]
self.n_iter = len(iters)
assert (self.n_iter == 1), 'Our prefetching iter only support 1 DataIter'
self.iters = iters
self.rename_data = rename_data
self.rename_label = rename_label
self.batch_size = (len(self.provide_data) * self.provide_data[0][0][1][0])
self.data_ready = [threading.Event() for i in range(self.n_iter)]
self.data_taken = [threading.Event() for i in range(self.n_iter)]
for e in self.data_taken:
e.set()
self.started = True
self.current_batch = [None for _ in range(self.n_iter)]
self.next_batch = [None for _ in range(self.n_iter)]
def prefetch_func(self, i):
'Thread entry'
while True:
self.data_taken[i].wait()
if (not self.started):
break
try:
self.next_batch[i] = self.iters[i].next()
except StopIteration:
self.next_batch[i] = None
self.data_taken[i].clear()
self.data_ready[i].set()
self.prefetch_threads = [threading.Thread(target=prefetch_func, args=[self, i]) for i in range(self.n_iter)]
for thread in self.prefetch_threads:
thread.setDaemon(True)
thread.start()
def __del__(self):
self.started = False
for e in self.data_taken:
e.set()
for thread in self.prefetch_threads:
thread.join()
@property
def provide_data(self):
'The name and shape of data provided by this iterator'
if (self.rename_data is None):
return sum([i.provide_data for i in self.iters], [])
else:
return sum([[(DataDesc(r[x.name], x.shape, x.dtype) if isinstance(x, DataDesc) else DataDesc(*x)) for x in i.provide_data] for (r, i) in zip(self.rename_data, self.iters)], [])
@property
def provide_label(self):
'The name and shape of label provided by this iterator'
if (self.rename_label is None):
return sum([i.provide_label for i in self.iters], [])
else:
return sum([[(DataDesc(r[x.name], x.shape, x.dtype) if isinstance(x, DataDesc) else DataDesc(*x)) for x in i.provide_label] for (r, i) in zip(self.rename_label, self.iters)], [])
def reset(self):
for e in self.data_ready:
e.wait()
for i in self.iters:
i.reset()
for e in self.data_ready:
e.clear()
for e in self.data_taken:
e.set()
def iter_next(self):
for e in self.data_ready:
e.wait()
if (self.next_batch[0] is None):
return False
else:
self.current_batch = self.next_batch[0]
for e in self.data_ready:
e.clear()
for e in self.data_taken:
e.set()
return True
def next(self):
if self.iter_next():
return self.current_batch
else:
raise StopIteration
def getdata(self):
return self.current_batch.data
def getlabel(self):
return self.current_batch.label
def getindex(self):
return self.current_batch.index
def getpad(self):
return self.current_batch.pad
|
def combine_model(prefix1, epoch1, prefix2, epoch2, prefix_out, epoch_out):
(args1, auxs1) = load_checkpoint(prefix1, epoch1)
(args2, auxs2) = load_checkpoint(prefix2, epoch2)
arg_names = (args1.keys() + args2.keys())
aux_names = (auxs1.keys() + auxs2.keys())
args = dict()
for arg in arg_names:
if (arg in args1):
args[arg] = args1[arg]
if (arg in args2):
args[arg] = args2[arg]
auxs = dict()
for aux in aux_names:
if (aux in auxs1):
auxs[aux] = auxs1[aux]
if (aux in auxs2):
auxs[aux] = auxs2[aux]
save_checkpoint(prefix_out, epoch_out, args, auxs)
|
@mx.init.register
class MyConstant(mx.init.Initializer):
def __init__(self, value):
super(MyConstant, self).__init__(value=value)
self.value = value
def _init_weight(self, _, arr):
arr[:] = mx.nd.array(self.value)
|
def create_logger(root_output_path, cfg, image_set):
if (not os.path.exists(root_output_path)):
os.makedirs(root_output_path)
assert os.path.exists(root_output_path), '{} does not exist'.format(root_output_path)
cfg_name = os.path.basename(cfg).split('.')[0]
config_output_path = os.path.join(root_output_path, '{}'.format(cfg_name))
if (not os.path.exists(config_output_path)):
os.makedirs(config_output_path)
image_sets = [iset for iset in image_set.split(';')]
final_output_path = os.path.join(config_output_path, '{}'.format('_'.join(image_sets)))
if (not os.path.exists(final_output_path)):
os.makedirs(final_output_path)
log_file = '{}_{}.log'.format(cfg_name, time.strftime('%Y-%m-%d-%H-%M'))
head = '%(asctime)-15s %(message)s'
logging.basicConfig(filename=os.path.join(final_output_path, log_file), format=head)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
return (logger, final_output_path)
|
class UnknownImageFormat(Exception):
pass
|
class Image(collections.namedtuple('Image', image_fields)):
def to_str_row(self):
return ('%d\t%d\t%d\t%s\t%s' % (self.width, self.height, self.file_size, self.type, self.path.replace('\t', '\\t')))
def to_str_row_verbose(self):
return ('%d\t%d\t%d\t%s\t%s\t##%s' % (self.width, self.height, self.file_size, self.type, self.path.replace('\t', '\\t'), self))
def to_str_json(self, indent=None):
return json.dumps(self._asdict(), indent=indent)
|
def get_image_size(file_path):
'\n Return (width, height) for a given img file content - no external\n dependencies except the os and struct builtin modules\n '
img = get_image_metadata(file_path)
return (img.width, img.height)
|
def get_image_size_from_bytesio(input, size):
'\n Return (width, height) for a given img file content - no external\n dependencies except the os and struct builtin modules\n\n Args:\n input (io.IOBase): io object support read & seek\n size (int): size of buffer in byte\n '
img = get_image_metadata_from_bytesio(input, size)
return (img.width, img.height)
|
def get_image_metadata(file_path):
'\n Return an `Image` object for a given img file content - no external\n dependencies except the os and struct builtin modules\n\n Args:\n file_path (str): path to an image file\n\n Returns:\n Image: (path, type, file_size, width, height)\n '
size = os.path.getsize(file_path)
with io.open(file_path, 'rb') as input:
return get_image_metadata_from_bytesio(input, size, file_path)
|
def get_image_metadata_from_bytesio(input, size, file_path=None):
'\n Return an `Image` object for a given img file content - no external\n dependencies except the os and struct builtin modules\n\n Args:\n input (io.IOBase): io object support read & seek\n size (int): size of buffer in byte\n file_path (str): path to an image file\n\n Returns:\n Image: (path, type, file_size, width, height)\n '
height = (- 1)
width = (- 1)
data = input.read(26)
msg = ' raised while trying to decode as JPEG.'
if ((size >= 10) and (data[:6] in (b'GIF87a', b'GIF89a'))):
imgtype = GIF
(w, h) = struct.unpack('<HH', data[6:10])
width = int(w)
height = int(h)
elif ((size >= 24) and data.startswith(b'\x89PNG\r\n\x1a\n') and (data[12:16] == b'IHDR')):
imgtype = PNG
(w, h) = struct.unpack('>LL', data[16:24])
width = int(w)
height = int(h)
elif ((size >= 16) and data.startswith(b'\x89PNG\r\n\x1a\n')):
imgtype = PNG
(w, h) = struct.unpack('>LL', data[8:16])
width = int(w)
height = int(h)
elif ((size >= 2) and data.startswith(b'\xff\xd8')):
imgtype = JPEG
input.seek(0)
input.read(2)
b = input.read(1)
try:
while (b and (ord(b) != 218)):
while (ord(b) != 255):
b = input.read(1)
while (ord(b) == 255):
b = input.read(1)
if ((ord(b) >= 192) and (ord(b) <= 195)):
input.read(3)
(h, w) = struct.unpack('>HH', input.read(4))
break
else:
input.read((int(struct.unpack('>H', input.read(2))[0]) - 2))
b = input.read(1)
width = int(w)
height = int(h)
except struct.error:
raise UnknownImageFormat(('StructError' + msg))
except ValueError:
raise UnknownImageFormat(('ValueError' + msg))
except Exception as e:
raise UnknownImageFormat((e.__class__.__name__ + msg))
elif ((size >= 26) and data.startswith(b'BM')):
imgtype = 'BMP'
headersize = struct.unpack('<I', data[14:18])[0]
if (headersize == 12):
(w, h) = struct.unpack('<HH', data[18:22])
width = int(w)
height = int(h)
elif (headersize >= 40):
(w, h) = struct.unpack('<ii', data[18:26])
width = int(w)
height = abs(int(h))
else:
raise UnknownImageFormat(('Unkown DIB header size:' + str(headersize)))
elif ((size >= 8) and (data[:4] in (b'II*\x00', b'MM\x00*'))):
imgtype = TIFF
byteOrder = data[:2]
boChar = ('>' if (byteOrder == 'MM') else '<')
tiffTypes = {1: (1, (boChar + 'B')), 2: (1, (boChar + 'c')), 3: (2, (boChar + 'H')), 4: (4, (boChar + 'L')), 5: (8, (boChar + 'LL')), 6: (1, (boChar + 'b')), 7: (1, (boChar + 'c')), 8: (2, (boChar + 'h')), 9: (4, (boChar + 'l')), 10: (8, (boChar + 'll')), 11: (4, (boChar + 'f')), 12: (8, (boChar + 'd'))}
ifdOffset = struct.unpack((boChar + 'L'), data[4:8])[0]
try:
countSize = 2
input.seek(ifdOffset)
ec = input.read(countSize)
ifdEntryCount = struct.unpack((boChar + 'H'), ec)[0]
ifdEntrySize = 12
for i in range(ifdEntryCount):
entryOffset = ((ifdOffset + countSize) + (i * ifdEntrySize))
input.seek(entryOffset)
tag = input.read(2)
tag = struct.unpack((boChar + 'H'), tag)[0]
if ((tag == 256) or (tag == 257)):
type = input.read(2)
type = struct.unpack((boChar + 'H'), type)[0]
if (type not in tiffTypes):
raise UnknownImageFormat(('Unkown TIFF field type:' + str(type)))
typeSize = tiffTypes[type][0]
typeChar = tiffTypes[type][1]
input.seek((entryOffset + 8))
value = input.read(typeSize)
value = int(struct.unpack(typeChar, value)[0])
if (tag == 256):
width = value
else:
height = value
if ((width > (- 1)) and (height > (- 1))):
break
except Exception as e:
raise UnknownImageFormat(str(e))
elif (size >= 2):
imgtype = 'ICO'
input.seek(0)
reserved = input.read(2)
if (0 != struct.unpack('<H', reserved)[0]):
raise UnknownImageFormat(FILE_UNKNOWN)
format = input.read(2)
assert (1 == struct.unpack('<H', format)[0])
num = input.read(2)
num = struct.unpack('<H', num)[0]
if (num > 1):
import warnings
warnings.warn('ICO File contains more than one image')
w = input.read(1)
h = input.read(1)
width = ord(w)
height = ord(h)
else:
raise UnknownImageFormat(FILE_UNKNOWN)
return Image(path=file_path, type=imgtype, file_size=size, width=width, height=height)
|
class Test_get_image_size(unittest.TestCase):
data = [{'path': 'lookmanodeps.png', 'width': 251, 'height': 208, 'file_size': 22228, 'type': 'PNG'}]
def setUp(self):
pass
def test_get_image_size_from_bytesio(self):
img = self.data[0]
p = img['path']
with io.open(p, 'rb') as fp:
b = fp.read()
fp = io.BytesIO(b)
sz = len(b)
output = get_image_size_from_bytesio(fp, sz)
self.assertTrue(output)
self.assertEqual(output, (img['width'], img['height']))
def test_get_image_metadata_from_bytesio(self):
img = self.data[0]
p = img['path']
with io.open(p, 'rb') as fp:
b = fp.read()
fp = io.BytesIO(b)
sz = len(b)
output = get_image_metadata_from_bytesio(fp, sz)
self.assertTrue(output)
for field in image_fields:
self.assertEqual(getattr(output, field), (None if (field == 'path') else img[field]))
def test_get_image_metadata(self):
img = self.data[0]
output = get_image_metadata(img['path'])
self.assertTrue(output)
for field in image_fields:
self.assertEqual(getattr(output, field), img[field])
def test_get_image_metadata__ENOENT_OSError(self):
with self.assertRaises(OSError):
get_image_metadata('THIS_DOES_NOT_EXIST')
def test_get_image_metadata__not_an_image_UnknownImageFormat(self):
with self.assertRaises(UnknownImageFormat):
get_image_metadata('README.rst')
def test_get_image_size(self):
img = self.data[0]
output = get_image_size(img['path'])
self.assertTrue(output)
self.assertEqual(output, (img['width'], img['height']))
def tearDown(self):
pass
|
def main(argv=None):
'\n Print image metadata fields for the given file path.\n\n Keyword Arguments:\n argv (list): commandline arguments (e.g. sys.argv[1:])\n Returns:\n int: zero for OK\n '
import logging
import optparse
import sys
prs = optparse.OptionParser(usage='%prog [-v|--verbose] [--json|--json-indent] <path0> [<pathN>]', description='Print metadata for the given image paths (without image library bindings).')
prs.add_option('--json', dest='json', action='store_true')
prs.add_option('--json-indent', dest='json_indent', action='store_true')
prs.add_option('-v', '--verbose', dest='verbose', action='store_true')
prs.add_option('-q', '--quiet', dest='quiet', action='store_true')
prs.add_option('-t', '--test', dest='run_tests', action='store_true')
argv = (list(argv) if (argv is not None) else sys.argv[1:])
(opts, args) = prs.parse_args(args=argv)
loglevel = logging.INFO
if opts.verbose:
loglevel = logging.DEBUG
elif opts.quiet:
loglevel = logging.ERROR
logging.basicConfig(level=loglevel)
log = logging.getLogger()
log.debug('argv: %r', argv)
log.debug('opts: %r', opts)
log.debug('args: %r', args)
if opts.run_tests:
import sys
sys.argv = ([sys.argv[0]] + args)
import unittest
return unittest.main()
output_func = Image.to_str_row
if opts.json_indent:
import functools
output_func = functools.partial(Image.to_str_json, indent=2)
elif opts.json:
output_func = Image.to_str_json
elif opts.verbose:
output_func = Image.to_str_row_verbose
EX_OK = 0
EX_NOT_OK = 2
if (len(args) < 1):
prs.print_help()
print('')
prs.error('You must specify one or more paths to image files')
errors = []
for path_arg in args:
try:
img = get_image_metadata(path_arg)
print(output_func(img))
except KeyboardInterrupt:
raise
except OSError as e:
log.error((path_arg, e))
errors.append((path_arg, e))
except Exception as e:
log.exception(e)
errors.append((path_arg, e))
pass
if len(errors):
import pprint
print('ERRORS', file=sys.stderr)
print('======', file=sys.stderr)
print(pprint.pformat(errors, indent=2), file=sys.stderr)
return EX_NOT_OK
return EX_OK
|
def load_checkpoint(prefix, epoch):
"\n Load model checkpoint from file.\n :param prefix: Prefix of model name.\n :param epoch: Epoch number of model we would like to load.\n :return: (arg_params, aux_params)\n arg_params : dict of str to NDArray\n Model parameter, dict of name to NDArray of net's weights.\n aux_params : dict of str to NDArray\n Model parameter, dict of name to NDArray of net's auxiliary states.\n "
save_dict = mx.nd.load(('%s-%04d.params' % (prefix, epoch)))
arg_params = {}
aux_params = {}
for (k, v) in save_dict.items():
(tp, name) = k.split(':', 1)
if (tp == 'arg'):
arg_params[name] = v
if (tp == 'aux'):
aux_params[name] = v
return (arg_params, aux_params)
|
def convert_context(params, ctx):
'\n :param params: dict of str to NDArray\n :param ctx: the context to convert to\n :return: dict of str of NDArray with context ctx\n '
new_params = dict()
for (k, v) in params.items():
new_params[k] = v.as_in_context(ctx)
return new_params
|
def load_param(prefix, epoch, convert=False, ctx=None, process=False):
'\n wrapper for load checkpoint\n :param prefix: Prefix of model name.\n :param epoch: Epoch number of model we would like to load.\n :param convert: reference model should be converted to GPU NDArray first\n :param ctx: if convert then ctx must be designated.\n :param process: model should drop any test\n :return: (arg_params, aux_params)\n '
(arg_params, aux_params) = load_checkpoint(prefix, epoch)
if convert:
if (ctx is None):
ctx = mx.cpu()
arg_params = convert_context(arg_params, ctx)
aux_params = convert_context(aux_params, ctx)
if process:
tests = [k for k in arg_params.keys() if ('_test' in k)]
for test in tests:
arg_params[test.replace('_test', '')] = arg_params.pop(test)
return (arg_params, aux_params)
|
class WarmupMultiFactorScheduler(LRScheduler):
'Reduce learning rate in factor at steps specified in a list\n\n Assume the weight has been updated by n times, then the learning rate will\n be\n\n base_lr * factor^(sum((step/n)<=1)) # step is an array\n\n Parameters\n ----------\n step: list of int\n schedule learning rate after n updates\n factor: float\n the factor for reducing the learning rate\n '
def __init__(self, step, factor=1, warmup=False, warmup_lr=0, warmup_step=0):
super(WarmupMultiFactorScheduler, self).__init__()
assert (isinstance(step, list) and (len(step) >= 1))
for (i, _step) in enumerate(step):
if ((i != 0) and (step[i] <= step[(i - 1)])):
raise ValueError('Schedule step must be an increasing integer list')
if (_step < 1):
raise ValueError('Schedule step must be greater or equal than 1 round')
if (factor > 1.0):
raise ValueError('Factor must be no more than 1 to make lr reduce')
self.step = step
self.cur_step_ind = 0
self.factor = factor
self.count = 0
self.warmup = warmup
self.warmup_lr = warmup_lr
self.warmup_step = warmup_step
def __call__(self, num_update):
'\n Call to schedule current learning rate\n\n Parameters\n ----------\n num_update: int\n the maximal number of updates applied to a weight.\n '
if (self.warmup and (num_update < self.warmup_step)):
return self.warmup_lr
while (self.cur_step_ind <= (len(self.step) - 1)):
if (num_update > self.step[self.cur_step_ind]):
self.count = self.step[self.cur_step_ind]
self.cur_step_ind += 1
self.base_lr *= self.factor
logging.info('Update[%d]: Change learning rate to %0.5e', num_update, self.base_lr)
else:
return self.base_lr
return self.base_lr
|
def save_checkpoint(prefix, epoch, arg_params, aux_params):
"Checkpoint the model data into file.\n :param prefix: Prefix of model name.\n :param epoch: The epoch number of the model.\n :param arg_params: dict of str to NDArray\n Model parameter, dict of name to NDArray of net's weights.\n :param aux_params: dict of str to NDArray\n Model parameter, dict of name to NDArray of net's auxiliary states.\n :return: None\n prefix-epoch.params will be saved for parameters.\n "
save_dict = {('arg:%s' % k): v for (k, v) in arg_params.items()}
save_dict.update({('aux:%s' % k): v for (k, v) in aux_params.items()})
param_name = ('%s-%04d.params' % (prefix, epoch))
mx.nd.save(param_name, save_dict)
|
class Symbol():
def __init__(self):
self.arg_shape_dict = None
self.out_shape_dict = None
self.aux_shape_dict = None
self.sym = None
@property
def symbol(self):
return self.sym
def get_symbol(self, cfg, is_train=True):
'\n return a generated symbol, it also need to be assigned to self.sym\n '
raise NotImplementedError()
def init_weights(self, cfg, arg_params, aux_params):
raise NotImplementedError()
def get_msra_std(self, shape):
fan_in = float(shape[1])
if (len(shape) > 2):
fan_in *= np.prod(shape[2:])
print(np.sqrt((2 / fan_in)))
return np.sqrt((2 / fan_in))
def infer_shape(self, data_shape_dict):
(arg_shape, out_shape, aux_shape) = self.sym.infer_shape(**data_shape_dict)
self.arg_shape_dict = dict(zip(self.sym.list_arguments(), arg_shape))
self.out_shape_dict = dict(zip(self.sym.list_outputs(), out_shape))
self.aux_shape_dict = dict(zip(self.sym.list_auxiliary_states(), aux_shape))
def check_parameter_shapes(self, arg_params, aux_params, data_shape_dict, is_train=True):
for k in self.sym.list_arguments():
if ((k in data_shape_dict) or (False if is_train else ('label' in k)) or ('const_eq_' in k) or ('cls_reps' in k)):
continue
assert (k in arg_params), (k + ' not initialized')
assert (arg_params[k].shape == self.arg_shape_dict[k]), ((((('shape inconsistent for ' + k) + ' inferred ') + str(self.arg_shape_dict[k])) + ' provided ') + str(arg_params[k].shape))
for k in self.sym.list_auxiliary_states():
assert (k in aux_params), (k + ' not initialized')
assert (aux_params[k].shape == self.aux_shape_dict[k]), ((((('shape inconsistent for ' + k) + ' inferred ') + str(self.aux_shape_dict[k])) + ' provided ') + str(aux_params[k].shape))
|
def tic():
import time
global startTime_for_tictoc
startTime_for_tictoc = time.time()
return startTime_for_tictoc
|
def toc():
if ('startTime_for_tictoc' in globals()):
endTime = time.time()
return (endTime - startTime_for_tictoc)
else:
return None
|
def get_dets_class_names(dets_n):
for det in dets_n:
class_names = []
for prd in det[3]:
if (prd in prd2ename.keys()):
class_names += [prd2ename[prd]]
else:
class_names += [prd]
det += [class_names]
return dets_n
|
def print_typical_tray_multiview():
imgname_left = os.path.join(data_root, '15849_left.jpg')
imgname_top = os.path.join(data_root, '15849_top.jpg')
imgname_right = os.path.join(data_root, '15849_right.jpg')
img_left = mpimg.imread(imgname_left)
img_top = mpimg.imread(imgname_top)
img_right = mpimg.imread(imgname_right)
fig = plt.figure(1)
ff = 3
fig.set_size_inches(((ff * 8.5), ((3 * ff) * 11)), forward=False)
plt.subplot(131)
plt.imshow(img_left)
plt.axis('off')
plt.subplot(132)
plt.imshow(img_top)
plt.axis('off')
plt.subplot(133)
plt.imshow(img_right)
plt.axis('off')
|
def display_few_shot_examples():
image_set = ['5cadb37d4b967f67d3047964.jpg', '12686073-1-white.jpg', 'lacoste-logo-sweatshirt-white-p12654-72879_image.jpg']
fig = plt.figure(2)
ff = 2
fig.set_size_inches(((ff * 8.5), ((3 * ff) * 11)), forward=False)
for (cnt, img_basename) in enumerate(image_set):
imgname = os.path.join(data_root, img_basename)
img = mpimg.imread(imgname)
plt.subplot(1, 3, (cnt + 1))
plt.imshow(img)
plt.axis('off')
|
def test_on_query_image(fs_serv, test_img_fname, det_engines):
img = cv2.imread(test_img_fname, (cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION))
score_thresh = 0.1
dets_n = fs_serv.detect_on_image(img, score_thresh=score_thresh, det_engines=det_engines)
dets_n = get_dets_class_names(dets_n)
test_img_basename = os.path.basename(test_img_fname)
img_file_path = os.path.join(disp_folder, test_img_basename.replace('.jpg', '_disp_n.jpg'))
disp_dets(img, dets_n, img_file_path, figure_factor=2.0)
|
def get_box_proposal(fs_serv, img_path):
from show_boxes import show_detsB_boxes
q_dets_p = fs_serv.get_box_proposal(img_path)
image_basename = os.path.basename(img_path)
save_file_path = os.path.join(disp_folder, 'box_prop_{0}'.format(image_basename))
img = cv2.cvtColor(cv2.imread(img_path, (cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)), cv2.COLOR_BGR2RGB)
show_detsB_boxes(img, q_dets_p, save_file_path=save_file_path)
|
def disp_dets2(img, dets, save_file_path):
import matplotlib.pyplot as plt
fig = plt.figure(1)
fig.set_size_inches(((1.4 * 8.5), (1.4 * 11)), forward=False)
plt.axis('off')
img_d = cv2.cvtColor(copy.deepcopy(img), cv2.COLOR_BGR2RGB)
w = 12
for det in dets:
bbox = det[0]
scores = det[1]
labels = det[3]
pn = det[4]
left = int(bbox[0])
top = int(bbox[1])
right = int(bbox[2])
bottom = int(bbox[3])
if (pn == 'p'):
cv2.rectangle(img_d, (left, top), (right, bottom), (255, 0, 0), 4)
else:
cv2.rectangle(img_d, (left, top), (right, bottom), (0, 255, 0), 4)
i = 0
for (s, l) in zip(scores, labels):
cv2.rectangle(img_d, (left, (top - w)), ((left + 450), ((top + w) + ((2 * w) * i))), (255, 255, 255), cv2.FILLED)
i += 1
i = 0
for (s, l) in zip(scores, labels):
text = '{0} - {1:.3f} {2}'.format(l, s, id2name[l])
cv2.putText(img_d, text, (left, (top + ((2 * w) * i))), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color=(0, 0, 0), thickness=2)
i += 1
plt.imshow(img_d)
fig.savefig(save_file_path)
|
def test_on_query_image(fs_serv, test_img_fname, score_thresh=0.1, det_engines=1, figure_factor=2.2, FontScale=2.3):
img = cv2.imread(test_img_fname, (cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION))
dets_n = fs_serv.detect_on_image(img, score_thresh=score_thresh, det_engines=det_engines)
test_img_basename = os.path.basename(test_img_fname)
img_file_path = os.path.join(disp_folder, test_img_basename.replace('.jpg', '_disp_n.jpg'))
disp_dets2(img, dets_n, img_file_path, figure_factor=figure_factor, FontScale=FontScale)
|
def display_RedHat_examples():
image_set = ['logo1.jpg', 'logo2.jpg', 'logo3.jpg']
fig = plt.figure(2)
ff = 2
fig.set_size_inches(((ff * 8.5), ((3 * ff) * 11)), forward=False)
enrollment_root = '../data/Logo/logo_usecase_data/RedHat/RedHat_enrollment'
for (cnt, img_basename) in enumerate(image_set):
imgname = os.path.join(enrollment_root, img_basename)
img = mpimg.imread(imgname)
plt.subplot(1, 3, (cnt + 1))
plt.imshow(img)
plt.axis('off')
|
def display_lacoste_examples():
image_set = ['5cadb37d4b967f67d3047964.jpg', 'lacoste-logo-sweatshirt-white-p12654-72879_image.jpg', '12686073-1-white.jpg']
fig = plt.figure(2)
ff = 2
fig.set_size_inches(((ff * 8.5), ((3 * ff) * 11)), forward=False)
enrollment_root = '../data/Logo/logo_usecase_data/lacoste/lacoste_enrollment/images'
for (cnt, img_basename) in enumerate(image_set):
imgname = os.path.join(enrollment_root, img_basename)
img = mpimg.imread(imgname)
plt.subplot(1, 3, (cnt + 1))
plt.imshow(img)
plt.axis('off')
|
def display_few_shot_examples():
data_root = '/dccstor/jsdata1/dev/RepMet/notebooks/food_usecase_data'
image_set = ['PRDS990000000000000025_0_192_501_589_885_top.jpg', 'PRDS990000000000000024_0_119_137_523_447_top.jpg', 'PRDS990000000000000023_0_118_208_470_612_top.jpg', 'PRDS990000000000000021_0_571_234_923_608_top.jpg']
nrows = 1
ncols = 4
fig = plt.figure(2)
ff = 2
fig.set_size_inches(((ff * 8.5), ((3 * ff) * 11)), forward=False)
for (cnt, img_basename) in enumerate(image_set):
imgname = os.path.join(data_root, img_basename)
img = mpimg.imread(imgname)
plt.subplot(nrows, ncols, (cnt + 1))
plt.imshow(img)
plt.axis('off')
|
def print_typical_tray_multiview():
imgname_left = os.path.join(data_root, '1007_10000000006530_left.jpg')
imgname_top = os.path.join(data_root, '1007_10000000006530_top.jpg')
imgname_right = os.path.join(data_root, '1007_10000000006530_right.jpg')
img_left = mpimg.imread(imgname_left)
img_top = mpimg.imread(imgname_top)
img_right = mpimg.imread(imgname_right)
fig = plt.figure(1)
ff = 3
fig.set_size_inches(((ff * 8.5), ((3 * ff) * 11)), forward=False)
plt.subplot(131)
plt.imshow(img_left)
plt.axis('off')
plt.subplot(132)
plt.imshow(img_top)
plt.axis('off')
plt.subplot(133)
plt.imshow(img_right)
plt.axis('off')
|
def disp_dets(img, dets, save_file_path):
import matplotlib.pyplot as plt
fig = plt.figure(1)
ff = 2
fig.set_size_inches(((ff * 8.5), (ff * 11)), forward=False)
plt.axis('off')
img_d = cv2.cvtColor(copy.deepcopy(img), cv2.COLOR_BGR2RGB)
for det in dets:
bbox = det[0]
scores = det[1]
labels = det[3]
pn = det[4]
left = int(bbox[0])
top = int(bbox[1])
right = int(bbox[2])
bottom = int(bbox[3])
if (pn == 'p'):
cv2.rectangle(img_d, (left, top), (right, bottom), (255, 0, 0), 4)
elif (pn == 'n'):
cv2.rectangle(img_d, (left, top), (right, bottom), (0, 255, 0), 4)
elif (pn == 'c'):
cv2.rectangle(img_d, (left, top), (right, bottom), (0, 0, 255), 4)
elif (pn == 'a'):
cv2.rectangle(img_d, (left, top), (right, bottom), (0, 255, 255), 4)
for det in dets:
bbox = det[0]
scores = det[1]
labels = det[3]
pn = det[4]
left = int(bbox[0])
top = int(bbox[1])
text = '{0} - {1:.3f}'.format(labels[0], scores[0])
string_len = (len(text) * 13)
bk_w = 17
FontScale = 0.6
cv2.rectangle(img_d, ((left - 2), (top - bk_w)), ((left + string_len), (top + bk_w)), (255, 255, 255), cv2.FILLED)
cv2.putText(img_d, text, (left, top), cv2.FONT_HERSHEY_TRIPLEX, FontScale, color=(0, 0, 0), thickness=1)
if (len(det) == 6):
text = det[5][0]
string_len = (len(text) * 13)
cv2.rectangle(img_d, ((left - 2), ((top - bk_w) + (2 * bk_w))), ((left + string_len), ((top + bk_w) + (2 * bk_w))), (255, 255, 255), cv2.FILLED)
cv2.putText(img_d, text, (left, (top + (2 * bk_w))), cv2.FONT_HERSHEY_TRIPLEX, FontScale, color=(0, 0, 0), thickness=1)
plt.imshow(img_d)
fig.savefig(save_file_path)
|
def test_on_query_image(fs_serv, test_img_fname, det_engines):
prd2ename_fname = '/dccstor/jsdata1/dev/RepMet/data/JES_pilot/all_GT.csv_converted_Feb24_prd2ename.csv'
prd2ename_data = np.load(prd2ename_fname)
prd2ename = prd2ename_data['prd2ename']
img = cv2.imread(test_img_fname, (cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION))
disp_folder = '../notebooks/food_usecase_data'
score_thresh = 0.1
dets_n = fs_serv.detect_on_image(img, score_thresh=score_thresh, num_candidates=3, det_engines=det_engines)
for det in dets_n:
class_names = []
for prd in det[3]:
if (prd in prd2ename.keys()):
class_names += [prd2ename[prd]]
else:
class_names += [prd]
det += [class_names]
test_img_basename = os.path.basename(test_img_fname)
img_file_path = os.path.join(disp_folder, test_img_basename.replace('.jpg', '_disp_n.jpg'))
det_file_path = os.path.join(disp_folder, test_img_basename.replace('.jpg', '_dets_n.txt'))
disp_dets(img, dets_n, img_file_path)
|
def get_box_proposal(fs_serv, img_path):
from show_boxes import show_detsB_boxes
output_folder = '../notebooks/food_usecase_data'
q_dets_p = fs_serv.get_box_proposal(img_path)
image_basename = os.path.basename(img_path)
save_file_path = os.path.join(output_folder, 'box_prop_{0}'.format(image_basename))
img = cv2.cvtColor(cv2.imread(img_path, (cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)), cv2.COLOR_BGR2RGB)
show_detsB_boxes(img, q_dets_p, save_file_path=save_file_path)
|
def display_few_shot_examples():
data_root = '/opt/DNN/dataset/retail/retail_test'
image_set = ['0de3ec888566edc2.jpg', '2e90529bcb43f44c.jpg', '15a8f82801fe4911.jpg', '34caf4cf9ae0ae92.jpg']
nrows = 1
ncols = np.ceil((len(image_set) / nrows))
print('nrows={0},ncols={1}'.format(nrows, ncols))
fig = plt.figure(2)
ff = 2
fig.set_size_inches(((ff * 8.5), ((3 * ff) * 11)), forward=False)
for (cnt, img_basename) in enumerate(image_set):
imgname = os.path.join(data_root, img_basename)
img = mpimg.imread(imgname)
plt.subplot(nrows, ncols, (cnt + 1))
plt.imshow(img)
plt.axis('off')
|
def print_typical_tray_multiview():
data_root = '/opt/DNN/dataset/retail/retail_test'
imgname_left = os.path.join(data_root, '0de3ec888566edc2.jpg')
imgname_top = os.path.join(data_root, '2e90529bcb43f44c.jpg')
imgname_right = os.path.join(data_root, '15a8f82801fe4911.jpg')
img_left = mpimg.imread(imgname_left)
img_top = mpimg.imread(imgname_top)
img_right = mpimg.imread(imgname_right)
fig = plt.figure(1)
ff = 3
fig.set_size_inches(((ff * 8.5), ((3 * ff) * 11)), forward=False)
plt.subplot(131)
plt.imshow(img_left)
plt.axis('off')
plt.subplot(132)
plt.imshow(img_top)
plt.axis('off')
plt.subplot(133)
plt.imshow(img_right)
plt.axis('off')
|
def oid2coco():
img_dir = '/opt/DNN/dataset/openimage/openimage_train'
boxes_csv = '/opt/DNN/dataset/openimage/retail.csv'
json_file = '/opt/DNN/dataset/openimage/annotations/instances_openimage_train.json'
classes_csv = '/opt/DNN/linkdata/eval_dataset/open_images/metadata/class-descriptions-boxable.csv'
with open(classes_csv, 'r') as classes_file:
classes = csv.reader(classes_file)
class_dict = dict(((rows[0], rows[1]) for rows in classes))
classes_keys = class_dict.keys()
classes_values = class_dict.values()
(images, anns, categories) = ([], [], [])
img_paths = [x for x in glob.glob((img_dir + '/*.jpg'))]
i = 1
for img_path in sorted(img_paths):
img = Image.open(img_path)
(width, height) = img.size
(_, img_name) = os.path.split(img_path)
dic = {'file_name': img_name, 'id': i, 'height': height, 'width': width}
images.append(dic)
i += 1
ann_index = 1
i = 0
with open(boxes_csv, 'r') as boxes:
lines = csv.reader(boxes)
file_name_last_line = ''
for line in lines:
if (line[0] == 'ImageID'):
continue
file_name = (line[0] + '.jpg')
full_image_path = os.path.join(img_dir, file_name)
if (os.path.exists(full_image_path) is not True):
continue
if (file_name != file_name_last_line):
i += 1
img = Image.open(full_image_path)
(width, height) = img.size
xmin = float(line[4])
xmax = float(line[5])
ymin = float(line[6])
ymax = float(line[7])
area = int((((xmax - xmin) * width) * ((ymax - ymin) * height)))
poly = []
bbox = [int((xmin * width)), int((ymin * height)), int(((xmax - xmin) * width)), int(((ymax - ymin) * height))]
category = line[2]
cat_id = classes_keys.index(category)
dic2 = {'segmentation': poly, 'area': area, 'iscrowd': 0, 'image_id': i, 'bbox': bbox, 'category_id': cat_id, 'id': ann_index, 'ignore': 0}
anns.append(dic2)
file_name_last_line = file_name
ann_index += 1
for cate in classes_values:
cat = {'supercategory': 'none', 'id': classes_values.index(cate), 'name': cate}
categories.append(cat)
data = {'images': images, 'type': 'instances', 'annotations': anns, 'categories': categories}
with open(json_file, 'w') as outfile:
json.dump(data, outfile)
with open((os.path.split(json_file)[0] + '/train_cats_split.txt'), 'w') as fid:
fid.writelines('\n'.join(classes_values))
|
def show_detsB_boxes(im, dets_B, scale=1.0, save_file_path='temp.png'):
fig = plt.figure(1)
ff = 1.0
fig.set_size_inches(((ff * 8.5), (ff * 11)), forward=False)
plt.cla()
plt.axis('off')
plt.imshow(im)
for det in dets_B:
det_row = det[0]
cat_name = det[2]
bbox = (det_row[:4] * scale)
color = (rand(), rand(), rand())
rect = plt.Rectangle((bbox[0], bbox[1]), (bbox[2] - bbox[0]), (bbox[3] - bbox[1]), fill=False, edgecolor=color, linewidth=2.5)
plt.gca().add_patch(rect)
score = det_row[(- 1)]
plt.gca().text(bbox[0], bbox[1], '{:s} {:.3f}'.format(cat_name, score), bbox=dict(facecolor=color, alpha=0.5), fontsize=16, color='white')
|
def display_few_shot_test(benchmark, query_image):
import shutil
from FSD_engine import FSD_RepMet as DetectionEngine
from config.bench_config import bcfg, update_bench_config
import cv2
update_bench_config('../experiments/bench_configs/openimage_3_5_10_1.yaml')
(q_dets_B, q_dets_multi_B) = benchmark.det_eng.detect_on_image(query_image, num_candidates=5, cand_ver=1)
img = cv2.cvtColor(cv2.imread(query_image, (cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)), cv2.COLOR_BGR2RGB)
prob = [item for item in q_dets_B if (item[0][4] > 0.2)]
save_file_path = '/opt/DNN/dataset/retail/openimage_3_5_10_1/temp.jpg'
show_detsB_boxes(img, prob, save_file_path=save_file_path)
|
class Data():
def __init__(self, args):
kwargs = {}
if (not args.cpu):
kwargs['collate_fn'] = default_collate
kwargs['pin_memory'] = True
else:
kwargs['collate_fn'] = default_collate
kwargs['pin_memory'] = False
self.loader_train = None
if (not args.test_only):
module_train = import_module(('data.' + args.data_train.lower()))
trainset = getattr(module_train, args.data_train)(args)
self.loader_train = MSDataLoader(args, trainset, batch_size=args.batch_size, shuffle=True, **kwargs)
if (args.data_test in ['Set5', 'Set14', 'B100', 'Urban100', 'BSDS300', 'DIV2K']):
if (not args.benchmark_noise):
module_test = import_module('data.benchmark')
testset = getattr(module_test, 'Benchmark')(args, train=False)
else:
module_test = import_module('data.benchmark_noise')
testset = getattr(module_test, 'BenchmarkNoise')(args, train=False)
else:
module_test = import_module(('data.' + args.data_test.lower()))
testset = getattr(module_test, args.data_test)(args, train=False)
self.loader_test = MSDataLoader(args, testset, batch_size=1, shuffle=False, **kwargs)
|
class Benchmark(srdata.SRData):
def __init__(self, args, train=True):
super(Benchmark, self).__init__(args, train, benchmark=True)
def _scan(self):
list_hr = []
list_lr = [[] for _ in self.scale]
for entry in os.scandir(self.dir_hr):
filename = os.path.splitext(entry.name)[0]
list_hr.append(os.path.join(self.dir_hr, (filename + self.ext)))
for (si, s) in enumerate(self.scale):
list_lr[si].append(os.path.join(self.dir_lr, 'X{}/{}x{}{}'.format(s, filename, s, self.ext)))
list_hr.sort()
for l in list_lr:
l.sort()
return (list_hr, list_lr)
def _set_filesystem(self, dir_data):
self.apath = os.path.join(dir_data, self.args.data_test)
self.dir_hr = os.path.join(self.apath, 'HR')
self.dir_lr = os.path.join(self.apath, 'LR')
self.ext = '.png'
|
class Demo(data.Dataset):
def __init__(self, args, train=False):
self.args = args
self.name = 'Demo'
self.scale = args.scale
self.idx_scale = 0
self.train = False
self.benchmark = False
self.filelist = []
for f in os.listdir(args.dir_demo):
if ((f.find('.png') >= 0) or (f.find('.jp') >= 0)):
self.filelist.append(os.path.join(args.dir_demo, f))
self.filelist.sort()
def __getitem__(self, idx):
filename = os.path.split(self.filelist[idx])[(- 1)]
(filename, _) = os.path.splitext(filename)
lr = misc.imread(self.filelist[idx])
lr = common.set_channel([lr], self.args.n_colors)[0]
return (common.np2Tensor([lr], self.args.rgb_range)[0], (- 1), filename)
def __len__(self):
return len(self.filelist)
def set_scale(self, idx_scale):
self.idx_scale = idx_scale
|
class DIV2K(srdata.SRData):
def __init__(self, args, train=True):
super(DIV2K, self).__init__(args, train)
self.repeat = (args.test_every // (args.n_train // args.batch_size))
def _scan(self):
list_hr = []
list_lr = [[] for _ in self.scale]
if self.train:
idx_begin = 0
idx_end = self.args.n_train
else:
idx_begin = self.args.n_train
idx_end = (self.args.offset_val + self.args.n_val)
for i in range((idx_begin + 1), (idx_end + 1)):
filename = '{:0>4}'.format(i)
list_hr.append(os.path.join(self.dir_hr, (filename + self.ext)))
for (si, s) in enumerate(self.scale):
list_lr[si].append(os.path.join(self.dir_lr, 'X{}/{}x{}{}'.format(s, filename, s, self.ext)))
return (list_hr, list_lr)
def _set_filesystem(self, dir_data):
self.apath = ((dir_data + '/DIV2K') + '/train')
self.dir_hr = os.path.join(self.apath, 'DIV2K_train_HR')
self.dir_lr = os.path.join(self.apath, ('DIV2K_train_LR_' + self.args.manner_of_downsampling))
self.ext = '.png'
def _name_hrbin(self):
return os.path.join(self.apath, 'bin', '{}_bin_HR.npy'.format(self.split))
def _name_lrbin(self, scale):
return os.path.join(self.apath, 'bin', '{}_bin_LR_X{}.npy'.format(self.split, scale))
def __len__(self):
if self.train:
return (len(self.images_hr) * self.repeat)
else:
return len(self.images_hr)
def _get_index(self, idx):
if self.train:
return (idx % len(self.images_hr))
else:
return idx
|
def _ms_loop(dataset, index_queue, data_queue, collate_fn, scale, seed, init_fn, worker_id):
global _use_shared_memory
_use_shared_memory = True
_set_worker_signal_handlers()
torch.set_num_threads(1)
torch.manual_seed(seed)
while True:
r = index_queue.get()
if (r is None):
break
(idx, batch_indices) = r
try:
idx_scale = 0
if ((len(scale) > 1) and dataset.train):
idx_scale = random.randrange(0, len(scale))
dataset.set_scale(idx_scale)
samples = collate_fn([dataset[i] for i in batch_indices])
samples.append(idx_scale)
except Exception:
data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
else:
data_queue.put((idx, samples))
|
class _MSDataLoaderIter(_DataLoaderIter):
def __init__(self, loader):
self.dataset = loader.dataset
self.scale = loader.scale
self.collate_fn = loader.collate_fn
self.batch_sampler = loader.batch_sampler
self.num_workers = loader.num_workers
self.pin_memory = (loader.pin_memory and torch.cuda.is_available())
self.timeout = loader.timeout
self.done_event = threading.Event()
self.sample_iter = iter(self.batch_sampler)
if (self.num_workers > 0):
self.worker_init_fn = loader.worker_init_fn
self.index_queues = [multiprocessing.Queue() for _ in range(self.num_workers)]
self.worker_queue_idx = 0
self.worker_result_queue = multiprocessing.SimpleQueue()
self.batches_outstanding = 0
self.worker_pids_set = False
self.shutdown = False
self.send_idx = 0
self.rcvd_idx = 0
self.reorder_dict = {}
base_seed = torch.LongTensor(1).random_()[0]
self.workers = [multiprocessing.Process(target=_ms_loop, args=(self.dataset, self.index_queues[i], self.worker_result_queue, self.collate_fn, self.scale, (base_seed + i), self.worker_init_fn, i)) for i in range(self.num_workers)]
if (self.pin_memory or (self.timeout > 0)):
self.data_queue = queue.Queue()
if self.pin_memory:
maybe_device_id = torch.cuda.current_device()
else:
maybe_device_id = None
self.worker_manager_thread = threading.Thread(target=_worker_manager_loop, args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory, maybe_device_id))
self.worker_manager_thread.daemon = True
self.worker_manager_thread.start()
else:
self.data_queue = self.worker_result_queue
for w in self.workers:
w.daemon = True
w.start()
_update_worker_pids(id(self), tuple((w.pid for w in self.workers)))
_set_SIGCHLD_handler()
self.worker_pids_set = True
for _ in range((2 * self.num_workers)):
self._put_indices()
|
class MSDataLoader(DataLoader):
def __init__(self, args, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, collate_fn=default_collate, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None):
super(MSDataLoader, self).__init__(dataset, batch_size=batch_size, shuffle=shuffle, sampler=sampler, batch_sampler=batch_sampler, num_workers=args.n_threads, collate_fn=collate_fn, pin_memory=pin_memory, drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn)
self.scale = args.scale
def __iter__(self):
return _MSDataLoaderIter(self)
|
class Adversarial(nn.Module):
def __init__(self, args, gan_type):
super(Adversarial, self).__init__()
self.gan_type = gan_type
self.gan_k = args.gan_k
self.discriminator = discriminator.Discriminator(args, gan_type)
if (gan_type != 'WGAN_GP'):
self.optimizer = utility.make_optimizer(args, self.discriminator)
else:
self.optimizer = optim.Adam(self.discriminator.parameters(), betas=(0, 0.9), eps=1e-08, lr=1e-05)
self.scheduler = utility.make_scheduler(args, self.optimizer)
def forward(self, fake, real):
fake_detach = fake.detach()
self.loss = 0
for _ in range(self.gan_k):
self.optimizer.zero_grad()
d_fake = self.discriminator(fake_detach)
d_real = self.discriminator(real)
if (self.gan_type == 'GAN'):
label_fake = torch.zeros_like(d_fake)
label_real = torch.ones_like(d_real)
loss_d = (F.binary_cross_entropy_with_logits(d_fake, label_fake) + F.binary_cross_entropy_with_logits(d_real, label_real))
elif (self.gan_type.find('WGAN') >= 0):
loss_d = (d_fake - d_real).mean()
if (self.gan_type.find('GP') >= 0):
epsilon = torch.rand_like(fake).view((- 1), 1, 1, 1)
hat = (fake_detach.mul((1 - epsilon)) + real.mul(epsilon))
hat.requires_grad = True
d_hat = self.discriminator(hat)
gradients = torch.autograd.grad(outputs=d_hat.sum(), inputs=hat, retain_graph=True, create_graph=True, only_inputs=True)[0]
gradients = gradients.view(gradients.size(0), (- 1))
gradient_norm = gradients.norm(2, dim=1)
gradient_penalty = (10 * gradient_norm.sub(1).pow(2).mean())
loss_d += gradient_penalty
self.loss += loss_d.item()
loss_d.backward()
self.optimizer.step()
if (self.gan_type == 'WGAN'):
for p in self.discriminator.parameters():
p.data.clamp_((- 1), 1)
self.loss /= self.gan_k
d_fake_for_g = self.discriminator(fake)
if (self.gan_type == 'GAN'):
loss_g = F.binary_cross_entropy_with_logits(d_fake_for_g, label_real)
elif (self.gan_type.find('WGAN') >= 0):
loss_g = (- d_fake_for_g.mean())
return loss_g
def state_dict(self, *args, **kwargs):
state_discriminator = self.discriminator.state_dict(*args, **kwargs)
state_optimizer = self.optimizer.state_dict()
return dict(**state_discriminator, **state_optimizer)
|
class Discriminator(nn.Module):
def __init__(self, args, gan_type='GAN'):
super(Discriminator, self).__init__()
in_channels = 3
out_channels = 64
depth = 7
bn = True
act = nn.LeakyReLU(negative_slope=0.2, inplace=True)
m_features = [common.BasicBlock(args.n_colors, out_channels, 3, bn=bn, act=act)]
for i in range(depth):
in_channels = out_channels
if ((i % 2) == 1):
stride = 1
out_channels *= 2
else:
stride = 2
m_features.append(common.BasicBlock(in_channels, out_channels, 3, stride=stride, bn=bn, act=act))
self.features = nn.Sequential(*m_features)
patch_size = (args.patch_size // (2 ** ((depth + 1) // 2)))
m_classifier = [nn.Linear((out_channels * (patch_size ** 2)), 1024), act, nn.Linear(1024, 1)]
self.classifier = nn.Sequential(*m_classifier)
def forward(self, x):
features = self.features(x)
output = self.classifier(features.view(features.size(0), (- 1)))
return output
|
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