Spaces:
Sleeping
Sleeping
| import numpy as np | |
| import tensorflow as tf | |
| initializer = tf.compat.v1.random_normal_initializer(stddev=0.001) | |
| def dense_nn(x, dims, dim_out, norm=True, name='dense_nn'): | |
| with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE): | |
| for i, size in enumerate(dims): | |
| x = tf.compat.v1.layers.dense(x, size, name=f'd{i}', kernel_initializer=initializer) | |
| if norm: | |
| x = tf.contrib.layers.layer_norm(x) | |
| x = tf.nn.leaky_relu(x) | |
| x = tf.compat.v1.layers.dense(x, dim_out, name='d_out', kernel_initializer=initializer) | |
| return x | |
| def cond_dense_nn(x, cond, dims, dim_out, norm=True, name='cond_dense_nn'): | |
| with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE): | |
| for i, size in enumerate(dims): | |
| x = tf.compat.v1.layers.dense(x, size, name=f'd{i}', kernel_initializer=initializer) | |
| if norm: | |
| x = tf.contrib.layers.layer_norm(x) | |
| x = tf.nn.leaky_relu(x) | |
| c = tf.compat.v1.layers.dense(cond, size, name=f'c{i}', kernel_initializer=initializer) | |
| x = tf.sigmoid(c) * x | |
| x = tf.compat.v1.layers.dense(x, dim_out, name='d_out', kernel_initializer=initializer) | |
| return x | |
| def large_cond_dense_nn(x, cond, dims, dim_out, norm=True, name='cond_dense_nn'): | |
| with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE): | |
| for i, size in enumerate(dims): | |
| x = tf.compat.v1.layers.dense(x, size, name=f'd{i}', kernel_initializer=initializer) | |
| if norm: | |
| x = tf.contrib.layers.layer_norm(x) | |
| x = tf.nn.leaky_relu(x) | |
| c = dense_nn(cond, [256,256], size, False, name=f'c{i}') | |
| x = tf.sigmoid(c) * x | |
| x = tf.compat.v1.layers.dense(x, dim_out, name='d_out') | |
| return x | |
| def res_block(input, dim, block_name): | |
| x = tf.compat.v1.layers.dense(input, dim, name=f'{block_name}_1', kernel_initializer=initializer) | |
| x = tf.nn.leaky_relu(x) | |
| x = tf.compat.v1.layers.dense(input, dim, name=f'{block_name}_2', kernel_initializer=initializer) | |
| x += input | |
| x = tf.nn.leaky_relu(x) | |
| return x | |
| def cond_resnet(x, cond, dims, dim_out, norm=True, name='cond_resnet'): | |
| with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE): | |
| x = tf.compat.v1.layers.dense(x, dims[0], name='1', kernel_initializer=initializer) | |
| for i, size in enumerate(dims): | |
| x = res_block(x, size, block_name=f'res_block_{i}') | |
| if norm: | |
| x = tf.contrib.layers.layer_norm(x) | |
| c = dense_nn(cond, [256,256], size, False, name=f'c{i}') | |
| x = tf.sigmoid(c) * x | |
| x = tf.compat.v1.layers.dense(x, dim_out, name='d_out', kernel_initializer=initializer) | |
| return x | |
| def convnet(x, dims, dim_out, name='convnet'): | |
| with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE): | |
| for i, d in enumerate(dims): | |
| x = tf.compat.v1.layers.conv2d(x, d, 3, padding='same', name=f'c{i}_1') | |
| x = tf.contrib.layers.instance_norm(x) | |
| x = tf.nn.leaky_relu(x) | |
| x = tf.compat.v1.layers.conv2d(x, d, 3, padding='same', name=f'c{i}_2') | |
| x = tf.contrib.layers.instance_norm(x) | |
| x = tf.nn.leaky_relu(x) | |
| x = tf.compat.v1.layers.max_pooling2d(x, 2, 2) | |
| x = tf.compat.v1.layers.flatten(x) | |
| x = tf.compat.v1.layers.dense(x, dim_out, name='d1') | |
| x = tf.contrib.layers.instance_norm(x) | |
| x = tf.nn.leaky_relu(x) | |
| x = tf.compat.v1.layers.dense(x, dim_out, name='d2') | |
| return x | |
| def peq_convnet(x, dims, dim_out, attention, name='peq_convnet'): | |
| B,N,H,W,C = tf.shape(input=x)[0], tf.shape(input=x)[1], *x.get_shape().as_list()[2:] | |
| with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE): | |
| # downsample | |
| x = tf.reshape(x, [-1,H,W,C]) | |
| for d in dims[:2]: | |
| x = tf.compat.v1.layers.conv2d(x, d, 3, strides=(1,1), padding='same') | |
| x = tf.contrib.layers.instance_norm(x) | |
| x = tf.nn.leaky_relu(x) | |
| x = tf.compat.v1.layers.conv2d(x, d, 3, strides=(2,2), padding='same') | |
| x = tf.contrib.layers.instance_norm(x) | |
| x = tf.nn.leaky_relu(x) | |
| H, W, C = H//2, W//2, d | |
| x = tf.reshape(x, [B,N,H,W,C]) | |
| # attention across set dimension | |
| x = tf.reshape(tf.transpose(a=x, perm=[0,2,3,1,4]), [B*H*W,N,C]) | |
| rep = attention(x, x, x) | |
| x += rep | |
| x = tf.transpose(a=tf.reshape(x, [B,H,W,N,C]), perm=[0,3,1,2,4]) | |
| # downsample | |
| x = tf.reshape(x, [-1,H,W,C]) | |
| for d in dims[2:]: | |
| x = tf.compat.v1.layers.conv2d(x, d, 3, strides=(1,1), padding='same') | |
| x = tf.contrib.layers.instance_norm(x) | |
| x = tf.nn.leaky_relu(x) | |
| x = tf.compat.v1.layers.conv2d(x, d, 3, strides=(2,2), padding='same') | |
| x = tf.contrib.layers.instance_norm(x) | |
| x = tf.nn.leaky_relu(x) | |
| H, W, C = H//2, W//2, d | |
| x = tf.compat.v1.layers.flatten(x) | |
| x = tf.compat.v1.layers.dense(x, dim_out) | |
| x = tf.contrib.layers.instance_norm(x) | |
| x = tf.nn.leaky_relu(x) | |
| x = tf.compat.v1.layers.dense(x, dim_out) | |
| x = tf.reshape(x, [B,N,dim_out]) | |
| return x | |
| def peq_resblock(x, dim, attention, name='peq_resnet'): | |
| B,N,H,W,C = tf.shape(input=x)[0], tf.shape(input=x)[1], *x.get_shape().as_list()[2:] | |
| with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE): | |
| res = tf.reshape(tf.transpose(a=x, perm=[0,2,3,1,4]), [B*H*W,N,C]) | |
| res = attention(res, res, res) | |
| res = tf.transpose(a=tf.reshape(res, [B,H,W,N,C]), perm=[0,3,1,2,4]) | |
| res = tf.reshape(res, [B*N,H,W,C]) | |
| res = tf.compat.v1.layers.conv2d(res, dim, 3, strides=(1,1), padding='same') | |
| res = tf.contrib.layers.instance_norm(res) | |
| res = tf.nn.leaky_relu(res) | |
| res = tf.compat.v1.layers.conv2d(res, dim, 3, strides=(1,1), padding='same') | |
| res = tf.contrib.layers.instance_norm(res) | |
| res = tf.reshape(res, [B,N,H,W,C]) | |
| x += res | |
| x = tf.nn.leaky_relu(x) | |
| return x | |