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Update eidetic_3d_lstm_net.py #1

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Update eidetic_3d_lstm_net.py #1

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2b2ece6
Update eidetic_3d_lstm_net.py
Sgsouham Jun 7, 2020
e6caa20
Update eidetic_3d_lstm_net.py
Sgsouham Jun 7, 2020
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12 changes: 6 additions & 6 deletions src/models/eidetic_3d_lstm_net.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,15 +57,15 @@ def rnn(images, real_input_flag, num_layers, num_hidden, configs):

memory = zero_state

with tf.variable_scope('generator'):
with tf.compat.v1.variable_scope('generator'):
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@ucalyptus2 ucalyptus2 Jun 7, 2020

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  • Compat flags added
  • Need TF2.2 upgradation

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@ucalyptus2 ucalyptus2 Jun 7, 2020

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cc: @Sgsouham

input_list = []
reuse = False
for time_step in range(window_length - 1):
input_list.append(
tf.zeros([batch_size, ims_width, ims_height, output_channels]))

for time_step in range(total_length - 1):
with tf.variable_scope('e3d-lstm', reuse=reuse):
with tf.compat.v1.variable_scope('e3d-lstm', reuse=reuse):
if time_step < input_length:
input_frm = images[:, time_step]
else:
Expand All @@ -76,7 +76,7 @@ def rnn(images, real_input_flag, num_layers, num_hidden, configs):

if time_step % (window_length - window_stride) == 0:
input_frm = tf.stack(input_list[time_step:])
input_frm = tf.transpose(input_frm, [1, 0, 2, 3, 4])
input_frm = tf.transpose(a=input_frm, perm=[1, 0, 2, 3, 4])

for i in range(num_layers):
if time_step == 0:
Expand All @@ -90,17 +90,17 @@ def rnn(images, real_input_flag, num_layers, num_hidden, configs):
hidden[i], cell[i], memory = lstm_layer[i](
inputs, hidden[i], cell[i], memory, c_history[i])

x_gen = tf.layers.conv3d(hidden[num_layers - 1], output_channels,
x_gen = tf.compat.v1.layers.conv3d(hidden[num_layers - 1], output_channels,
[window_length, 1, 1], [window_length, 1, 1],
'same')
x_gen = tf.squeeze(x_gen)
gen_images.append(x_gen)
reuse = True

gen_images = tf.stack(gen_images)
gen_images = tf.transpose(gen_images, [1, 0, 2, 3, 4])
gen_images = tf.transpose(a=gen_images, perm=[1, 0, 2, 3, 4])
loss = tf.nn.l2_loss(gen_images - images[:, 1:])
loss += tf.reduce_sum(tf.abs(gen_images - images[:, 1:]))
loss += tf.reduce_sum(input_tensor=tf.abs(gen_images - images[:, 1:]))

out_len = total_length - input_length
out_ims = gen_images[:, -out_len:]
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