def tf_baseline_conv2d():
import tensorflow as tf
import cntk.contrib.crosstalk.crosstalk_tensorflow as crtf
ci = crtf.instance
tf.reset_default_graph()
x = tf.placeholder(tf.float32, [batch_size, seq_len, num_chars, char_emb_dim])
filter_bank = tf.get_variable("char_filter_bank",
shape=[filter_width, char_emb_dim, num_filters],
dtype=tf.float32)
bias = tf.get_variable("char_filter_biases", shape=[num_filters], dtype=tf.float32)
x_reshape = tf.reshape(x, [-1] + x.get_shape().as_list()[-2:])
char_conv = tf.expand_dims(tf.transpose(tf.nn.conv1d(x_reshape, filter_bank, stride=1, padding='VALID') + bias, perm=[0,2,1]), -1)
char_conv = tf.reshape(char_conv, [-1, seq_len] + char_conv.shape.as_list()[-3:])
ci.watch(cstk.Conv2DArgs(W=crtf.find_trainable('char_filter_bank'), b=crtf.find_trainable('char_filter_biases')), 'conv2d', var_type=cstk.Conv2DAttr,
attr=cstk.Conv2DAttr(filter_shape=(filter_width, char_emb_dim,), num_filters=num_filters))
ci.watch(char_conv, 'conv2d_out', var_type=crtf.VariableType) # note the output is transposed to NCHW
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
data = {x:input_data}
ci.set_workdir(workdir)
ci.set_data(sess, data)
ci.fetch('conv2d_out', save=True)
ci.fetch('conv2d', save=True)
ci.assign('conv2d', load=True)
assert ci.compare('conv2d_out')
ci.reset()
sess.close()
def _get(pd, attr):
W = trainable_getter(sess)(pd.W)
if pd.b:
b = trainable_getter(sess)(pd.b)
else:
b = None
return cstk.Conv2DArgs(W=W.transpose(2, 0, 1),
b=b.reshape(attr.num_filters, ))
def _conv2d_getter(f, attr):
W = _parameter_getter(find_func_param(f, shape=(attr.num_filters, 1,) + attr.filter_shape))
bias_param = find_func_param(f, shape=(attr.num_filters, 1, 1,), allow_not_found=True)
if bias_param:
b = _parameter_getter(bias_param)
else:
b = None
return cstk.Conv2DArgs(W=W[:,0,:,:], b=None if b is None else b.reshape(-1))
def _get(pd, attr):
W = _trainable_getter(sess)(pd.W)
#handling input with sequence axis:
W_rank = len(W.shape)
#the transpose from tf [H, W, C] to cntk's [C, H, W] happens at the tailing axes excluding the leading dynamic
#axes (batch and sequence axes) in the data format:
axis_perm = (list(range(W_rank - 3)) if W_rank > 3 else []) + [i + W_rank - 3 for i in [2,0,1]]
if pd.b:
b = _trainable_getter(sess)(pd.b)
else:
b = None
return cstk.Conv2DArgs(W=W.transpose(axis_perm), b=b.reshape(attr.num_filters,))
def test_cntk_conv2d():
try:
import tensorflow
has_tensorflow = True
except:
has_tensorflow = False
if has_tensorflow:
tf_baseline_conv2d()
else:
cntk_baseline_conv2d()
import cntk as C
import cntk.contrib.crosstalk.crosstalk_cntk as crct
ci = crct.instance
input_var = C.sequence.input_variable(shape=sample_shape)
input_reshaped = C.reshape(input_var, (1,)+sample_shape)
conv_out = C.layers.Convolution2D(filter_shape, num_filters, activation=None)(input_reshaped)
ci.watch(conv_out, 'conv2d', var_type=cstk.Conv2DAttr,
attr=cstk.Conv2DAttr(filter_shape=filter_shape, num_filters=num_filters))
ci.watch(conv_out, 'conv2d_out')
data = {input_var:input_data}
ci.set_data(data)
ci.set_workdir(workdir)
conv_out_values = conv_out.eval(data)
# load parameters from crosstalk and verify results are the same
ci.assign('conv2d', load=True)
assert ci.compare('conv2d_out', rtol=1e-4, atol=1e-6)
# test assign with value
ci.assign('conv2d', value=cstk.Conv2DArgs(W=np.random.random((num_filters,) + filter_shape).astype(np.float32),
b=np.random.random((num_filters,)).astype(np.float32)))
ci.reset()