def _build_dis_cond_cnn_graph(self):
print('frame_cond_cnn_graph')
cond_input = tf.reshape(
self.cond_inputs,
[self.batch_size, self.num_steps, self.mus_ebd_dim, 1])
mot_input = tf.reshape(
self.inputs,
[self.batch_size, self.num_steps, self.mus_ebd_dim, 1])
# bs * mus_ebd_dim * num_steps * 1
mot_input = tf.transpose(mot_input, [0, 2, 1, 3])
cond_input = tf.transpose(cond_input, [0, 2, 1, 3])
all_input = tf.concat([mot_input, cond_input],
axis=self.cond_axis,
name='concat_cond')
[batch_size, m_dim, num_steps, chl] = all_input.get_shape()
all_input = tf.transpose(all_input, [0, 2, 1, 3])
all_input = tf.reshape(
all_input,
[int(batch_size) * int(num_steps),
int(m_dim), 1,
int(chl)])
print('all_input: ', all_input)
conv_list_d = [[
64, self.kernel_size, self.stride, 'SAME', self.act_type
], [128, self.kernel_size, self.stride, 'SAME', self.act_type]]
fc_list_d = [[1, '']]
outputs = md.cnn(mot_input,
conv_list_d,
fc_list_d,
name='discriminator',
reuse=self.is_reuse)
return outputs
def _build_dis_time_tgan_cond_cnn_graph(self):
print('time_tgan_cond_cnn_graph')
# bs * 1 * num_steps * 72
cond_input = tf.reshape(
self.cond_inputs,
[self.batch_size, 1, self.num_steps, self.mus_ebd_dim])
mot_input = tf.reshape(
self.inputs,
[self.batch_size, 1, self.num_steps, self.mus_ebd_dim])
all_input = tf.concat([mot_input, cond_input],
axis=self.cond_axis,
name='concat_cond')
if self.is_shuffle:
original_shape = all_input.get_shape().as_list()
np.random.seed(1234567890)
shuffle_list = list(np.random.permutation(original_shape[0]))
all_inputs = []
for i, idx in enumerate(shuffle_list):
all_inputs.append(all_input[idx:idx + 1, :, :, :])
all_input = tf.concat(all_inputs, axis=0)
print('all_input: ', all_input)
# [1, 3] [1, 2]
conv_list_d = [
[32, self.kernel_size, self.stride, 'SAME', self.act_type],
[64, self.kernel_size, self.stride, 'SAME', self.act_type, 'bn'],
[128, self.kernel_size, self.stride, 'SAME', self.act_type, 'bn'],
[256, self.kernel_size, self.stride, 'SAME', self.act_type, 'bn']
]
outputs = md.cnn(all_input,
conv_list_d, [],
name='discriminator',
reuse=self.is_reuse)
return outputs
def _build_dis_cnn_graph(self):
print('frame_cnn_graph')
mot_input = tf.reshape(self.inputs, [-1, 20, 1, 3])
conv_list_d = [[
64, self.kernel_size, self.stride, 'SAME', self.act_type
], [128, self.kernel_size, self.stride, 'SAME', self.act_type]]
fc_list_d = [[1, '']]
outputs = md.cnn(mot_input,
conv_list_d,
fc_list_d,
name='discriminator',
reuse=self.is_reuse)
return outputs
def _build_dis_cnn_graph(self):
print('seg_cnn_graph')
mot_input = tf.reshape(self.inputs,
[self.batch_size, self.num_steps, 20, 3])
# bs * 20 * num_steps * 3
tf.transpose(mot_input, [0, 2, 1, 3])
# [3, 3] [2, 2]
conv_list_d = [
[64, self.kernel_size, self.stride, 'SAME', self.act_type],
[128, self.kernel_size, self.stride, 'SAME', self.act_type],
[256, self.kernel_size, self.stride, 'SAME', self.act_type],
[512, self.kernel_size, self.stride, 'SAME', self.act_type]
]
fc_list_d = [[1, '']]
outputs = md.cnn(mot_input,
conv_list_d,
fc_list_d,
name='discriminator',
reuse=self.is_reuse)
return outputs
def _build_dis_sig_cnn_graph(self):
print('frame_sig_cnn_graph')
inputs = tf.reshape(self.inputs, [-1, 20, 1, 3])
idx_lists = [
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 2, 4, 6, 8, 10, 12, 14, 16,
18, 1, 4, 7, 10, 13, 16, 19, 3, 6, 9, 12, 15, 18, 2, 5, 8, 11, 14,
17, 1, 5, 9, 13, 17, 2, 6, 10, 14, 18, 3, 7, 11, 15, 19, 4, 8, 12,
16, 1, 6, 11, 16, 2, 7, 12, 17, 3, 8, 13, 18, 4, 9, 14, 19, 5, 10,
15, 1, 7, 13, 19, 6, 12, 18, 5, 11, 17, 4, 10, 16, 3, 9, 15, 2, 8,
14, 1, 8, 15, 3, 10, 17, 5, 12, 19, 7, 14, 2, 9, 16, 4, 11, 18, 6,
13, 1, 9, 17, 6, 14, 3, 11, 19, 8, 16, 5, 13, 2, 10, 18, 7, 15, 4,
12, 1, 10, 19, 9, 18, 8, 17, 7, 16, 6, 15, 5, 14, 4, 13, 3, 12, 2,
11, 1
]
# TODO: need to check
mot_input = []
for i, idx in enumerate(idx_lists):
mot_input.append(inputs[:, idx, :, :])
mot_input = tf.reshape(tf.concat(mot_input, axis=1), [-1, 173, 1, 3])
# [3, 1], [2, 1]
conv_list_d = [
[64, self.kernel_size, self.stride, 'SAME', self.act_type],
[128, self.kernel_size, self.stride, 'SAME', self.act_type],
[256, self.kernel_size, self.stride, 'SAME', self.act_type],
[512, self.kernel_size, self.stride, 'SAME', self.act_type]
]
fc_list_d = [[1, '']]
outputs = md.cnn(mot_input,
conv_list_d,
fc_list_d,
name='discriminator',
reuse=self.is_reuse)
return outputs
