def testCreateAvgPoolStrideSAME(self): height, width = 3, 3 with self.test_session(): images = tf.random_uniform((5, height, width, 3), seed=1) output = ops.avg_pool(images, [3, 3], stride=1, padding='SAME') self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3])
def testCreateSquareAvgPool(self): height, width = 3, 3 with self.test_session(): images = tf.random_uniform((5, height, width, 3), seed=1) output = ops.avg_pool(images, 3) self.assertEquals(output.op.name, 'AvgPool/AvgPool') self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
o = conv2d(o, num_filters_out=128, kernel_size=(3, 3), stride=2) o = conv2d(o, num_filters_out=128, kernel_size=(3, 3), stride=1) flat = flatten(o) z = fc(flat, num_units_out=z_dim, activation=None) # normalized between -2 and 2 because of batchnorm return tf.nn.sigmoid(z * 2) #batch_size = 32 #batch_size = 64 batch_size = 48 with tf.variable_scope("data"): with tf.device("/cpu:0"): images32, _ = data_cifar10.get_inputs(batch_size) images16 = avg_pool(images32, kernel_size=[2, 2]) images8 = avg_pool(images16, kernel_size=[2, 2]) images4 = avg_pool(images8, kernel_size=[2, 2]) with tf.variable_scope("generator") as gen_scope: z = tf.random_uniform([batch_size, z_dim], 0, 1) gen4 = generator(z, size=4) print [n.name for n in tf.trainable_variables()] with tf.variable_scope(gen_scope, reuse=True): z = tf.random_uniform([batch_size, z_dim], 0, 1) gen8 = generator(z, size=8) with tf.variable_scope(gen_scope, reuse=True): z = tf.random_uniform([batch_size, z_dim], 0, 1) gen16 = generator(z, size=16)
def inception_v3(inputs, dropout_keep_prob=0.8, num_classes=1000, is_training=True, restore_logits=True, scope=''): """Latest Inception from http://arxiv.org/abs/1512.00567. "Rethinking the Inception Architecture for Computer Vision" Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna Args: inputs: a tensor of size [batch_size, height, width, channels]. dropout_keep_prob: dropout keep_prob. num_classes: number of predicted classes. is_training: whether is training or not. restore_logits: whether or not the logits layers should be restored. Useful for fine-tuning a model with different num_classes. scope: Optional scope for name_scope. Returns: a list containing 'logits', 'aux_logits' Tensors. """ # end_points will collect relevant activations for external use, for example # summaries or losses. end_points = {} with tf.name_scope(scope, 'inception_v3', [inputs]): with scopes.arg_scope( [ops.conv2d, ops.fc, ops.batch_norm, ops.dropout], is_training=is_training): with scopes.arg_scope([ops.conv2d, ops.max_pool, ops.avg_pool], stride=1, padding='VALID'): # 299 x 299 x 3 end_points['conv0'] = ops.conv2d(inputs, 32, [3, 3], stride=2, scope='conv0') # 149 x 149 x 32 end_points['conv1'] = ops.conv2d(end_points['conv0'], 32, [3, 3], scope='conv1') # 147 x 147 x 32 end_points['conv2'] = ops.conv2d(end_points['conv1'], 64, [3, 3], padding='SAME', scope='conv2') # 147 x 147 x 64 end_points['pool1'] = ops.max_pool(end_points['conv2'], [3, 3], stride=2, scope='pool1') # 73 x 73 x 64 end_points['conv3'] = ops.conv2d(end_points['pool1'], 80, [1, 1], scope='conv3') # 73 x 73 x 80. end_points['conv4'] = ops.conv2d(end_points['conv3'], 192, [3, 3], scope='conv4') # 71 x 71 x 192. end_points['pool2'] = ops.max_pool(end_points['conv4'], [3, 3], stride=2, scope='pool2') # 35 x 35 x 192. net = end_points['pool2'] # Inception blocks with scopes.arg_scope([ops.conv2d, ops.max_pool, ops.avg_pool], stride=1, padding='SAME'): # mixed: 35 x 35 x 256. with tf.variable_scope('mixed_35x35x256a'): with tf.variable_scope('branch1x1'): branch1x1 = ops.conv2d(net, 64, [1, 1]) with tf.variable_scope('branch5x5'): branch5x5 = ops.conv2d(net, 48, [1, 1]) branch5x5 = ops.conv2d(branch5x5, 64, [5, 5]) with tf.variable_scope('branch3x3dbl'): branch3x3dbl = ops.conv2d(net, 64, [1, 1]) branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3]) branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3]) with tf.variable_scope('branch_pool'): branch_pool = ops.avg_pool(net, [3, 3]) branch_pool = ops.conv2d(branch_pool, 32, [1, 1]) net = tf.concat(axis=3, values=[ branch1x1, branch5x5, branch3x3dbl, branch_pool ]) end_points['mixed_35x35x256a'] = net # mixed_1: 35 x 35 x 288. with tf.variable_scope('mixed_35x35x288a'): with tf.variable_scope('branch1x1'): branch1x1 = ops.conv2d(net, 64, [1, 1]) with tf.variable_scope('branch5x5'): branch5x5 = ops.conv2d(net, 48, [1, 1]) branch5x5 = ops.conv2d(branch5x5, 64, [5, 5]) with tf.variable_scope('branch3x3dbl'): branch3x3dbl = ops.conv2d(net, 64, [1, 1]) branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3]) branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3]) with tf.variable_scope('branch_pool'): branch_pool = ops.avg_pool(net, [3, 3]) branch_pool = ops.conv2d(branch_pool, 64, [1, 1]) net = tf.concat(axis=3, values=[ branch1x1, branch5x5, branch3x3dbl, branch_pool ]) end_points['mixed_35x35x288a'] = net # mixed_2: 35 x 35 x 288. with tf.variable_scope('mixed_35x35x288b'): with tf.variable_scope('branch1x1'): branch1x1 = ops.conv2d(net, 64, [1, 1]) with tf.variable_scope('branch5x5'): branch5x5 = ops.conv2d(net, 48, [1, 1]) branch5x5 = ops.conv2d(branch5x5, 64, [5, 5]) with tf.variable_scope('branch3x3dbl'): branch3x3dbl = ops.conv2d(net, 64, [1, 1]) branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3]) branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3]) with tf.variable_scope('branch_pool'): branch_pool = ops.avg_pool(net, [3, 3]) branch_pool = ops.conv2d(branch_pool, 64, [1, 1]) net = tf.concat(axis=3, values=[ branch1x1, branch5x5, branch3x3dbl, branch_pool ]) end_points['mixed_35x35x288b'] = net # mixed_3: 17 x 17 x 768. with tf.variable_scope('mixed_17x17x768a'): with tf.variable_scope('branch3x3'): branch3x3 = ops.conv2d(net, 384, [3, 3], stride=2, padding='VALID') with tf.variable_scope('branch3x3dbl'): branch3x3dbl = ops.conv2d(net, 64, [1, 1]) branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3]) branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3], stride=2, padding='VALID') with tf.variable_scope('branch_pool'): branch_pool = ops.max_pool(net, [3, 3], stride=2, padding='VALID') net = tf.concat( axis=3, values=[branch3x3, branch3x3dbl, branch_pool]) end_points['mixed_17x17x768a'] = net # mixed4: 17 x 17 x 768. with tf.variable_scope('mixed_17x17x768b'): with tf.variable_scope('branch1x1'): branch1x1 = ops.conv2d(net, 192, [1, 1]) with tf.variable_scope('branch7x7'): branch7x7 = ops.conv2d(net, 128, [1, 1]) branch7x7 = ops.conv2d(branch7x7, 128, [1, 7]) branch7x7 = ops.conv2d(branch7x7, 192, [7, 1]) with tf.variable_scope('branch7x7dbl'): branch7x7dbl = ops.conv2d(net, 128, [1, 1]) branch7x7dbl = ops.conv2d(branch7x7dbl, 128, [7, 1]) branch7x7dbl = ops.conv2d(branch7x7dbl, 128, [1, 7]) branch7x7dbl = ops.conv2d(branch7x7dbl, 128, [7, 1]) branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [1, 7]) with tf.variable_scope('branch_pool'): branch_pool = ops.avg_pool(net, [3, 3]) branch_pool = ops.conv2d(branch_pool, 192, [1, 1]) net = tf.concat(axis=3, values=[ branch1x1, branch7x7, branch7x7dbl, branch_pool ]) end_points['mixed_17x17x768b'] = net # mixed_5: 17 x 17 x 768. with tf.variable_scope('mixed_17x17x768c'): with tf.variable_scope('branch1x1'): branch1x1 = ops.conv2d(net, 192, [1, 1]) with tf.variable_scope('branch7x7'): branch7x7 = ops.conv2d(net, 160, [1, 1]) branch7x7 = ops.conv2d(branch7x7, 160, [1, 7]) branch7x7 = ops.conv2d(branch7x7, 192, [7, 1]) with tf.variable_scope('branch7x7dbl'): branch7x7dbl = ops.conv2d(net, 160, [1, 1]) branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [7, 1]) branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [1, 7]) branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [7, 1]) branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [1, 7]) with tf.variable_scope('branch_pool'): branch_pool = ops.avg_pool(net, [3, 3]) branch_pool = ops.conv2d(branch_pool, 192, [1, 1]) net = tf.concat(axis=3, values=[ branch1x1, branch7x7, branch7x7dbl, branch_pool ]) end_points['mixed_17x17x768c'] = net # mixed_6: 17 x 17 x 768. with tf.variable_scope('mixed_17x17x768d'): with tf.variable_scope('branch1x1'): branch1x1 = ops.conv2d(net, 192, [1, 1]) with tf.variable_scope('branch7x7'): branch7x7 = ops.conv2d(net, 160, [1, 1]) branch7x7 = ops.conv2d(branch7x7, 160, [1, 7]) branch7x7 = ops.conv2d(branch7x7, 192, [7, 1]) with tf.variable_scope('branch7x7dbl'): branch7x7dbl = ops.conv2d(net, 160, [1, 1]) branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [7, 1]) branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [1, 7]) branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [7, 1]) branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [1, 7]) with tf.variable_scope('branch_pool'): branch_pool = ops.avg_pool(net, [3, 3]) branch_pool = ops.conv2d(branch_pool, 192, [1, 1]) net = tf.concat(axis=3, values=[ branch1x1, branch7x7, branch7x7dbl, branch_pool ]) end_points['mixed_17x17x768d'] = net # mixed_7: 17 x 17 x 768. with tf.variable_scope('mixed_17x17x768e'): with tf.variable_scope('branch1x1'): branch1x1 = ops.conv2d(net, 192, [1, 1]) with tf.variable_scope('branch7x7'): branch7x7 = ops.conv2d(net, 192, [1, 1]) branch7x7 = ops.conv2d(branch7x7, 192, [1, 7]) branch7x7 = ops.conv2d(branch7x7, 192, [7, 1]) with tf.variable_scope('branch7x7dbl'): branch7x7dbl = ops.conv2d(net, 192, [1, 1]) branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [7, 1]) branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [1, 7]) branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [7, 1]) branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [1, 7]) with tf.variable_scope('branch_pool'): branch_pool = ops.avg_pool(net, [3, 3]) branch_pool = ops.conv2d(branch_pool, 192, [1, 1]) net = tf.concat(axis=3, values=[ branch1x1, branch7x7, branch7x7dbl, branch_pool ]) end_points['mixed_17x17x768e'] = net # Auxiliary Head logits aux_logits = tf.identity(end_points['mixed_17x17x768e']) with tf.variable_scope('aux_logits'): aux_logits = ops.avg_pool(aux_logits, [5, 5], stride=3, padding='VALID') aux_logits = ops.conv2d(aux_logits, 128, [1, 1], scope='proj') # Shape of feature map before the final layer. shape = aux_logits.get_shape() aux_logits = ops.conv2d(aux_logits, 768, shape[1:3], stddev=0.01, padding='VALID') aux_logits = ops.flatten(aux_logits) aux_logits = ops.fc(aux_logits, num_classes, activation=None, stddev=0.001, restore=restore_logits) end_points['aux_logits'] = aux_logits # mixed_8: 8 x 8 x 1280. # Note that the scope below is not changed to not void previous # checkpoints. # (TODO) Fix the scope when appropriate. with tf.variable_scope('mixed_17x17x1280a'): with tf.variable_scope('branch3x3'): branch3x3 = ops.conv2d(net, 192, [1, 1]) branch3x3 = ops.conv2d(branch3x3, 320, [3, 3], stride=2, padding='VALID') with tf.variable_scope('branch7x7x3'): branch7x7x3 = ops.conv2d(net, 192, [1, 1]) branch7x7x3 = ops.conv2d(branch7x7x3, 192, [1, 7]) branch7x7x3 = ops.conv2d(branch7x7x3, 192, [7, 1]) branch7x7x3 = ops.conv2d(branch7x7x3, 192, [3, 3], stride=2, padding='VALID') with tf.variable_scope('branch_pool'): branch_pool = ops.max_pool(net, [3, 3], stride=2, padding='VALID') net = tf.concat( axis=3, values=[branch3x3, branch7x7x3, branch_pool]) end_points['mixed_17x17x1280a'] = net # mixed_9: 8 x 8 x 2048. with tf.variable_scope('mixed_8x8x2048a'): with tf.variable_scope('branch1x1'): branch1x1 = ops.conv2d(net, 320, [1, 1]) with tf.variable_scope('branch3x3'): branch3x3 = ops.conv2d(net, 384, [1, 1]) branch3x3 = tf.concat( axis=3, values=[ ops.conv2d(branch3x3, 384, [1, 3]), ops.conv2d(branch3x3, 384, [3, 1]) ]) with tf.variable_scope('branch3x3dbl'): branch3x3dbl = ops.conv2d(net, 448, [1, 1]) branch3x3dbl = ops.conv2d(branch3x3dbl, 384, [3, 3]) branch3x3dbl = tf.concat( axis=3, values=[ ops.conv2d(branch3x3dbl, 384, [1, 3]), ops.conv2d(branch3x3dbl, 384, [3, 1]) ]) with tf.variable_scope('branch_pool'): branch_pool = ops.avg_pool(net, [3, 3]) branch_pool = ops.conv2d(branch_pool, 192, [1, 1]) net = tf.concat(axis=3, values=[ branch1x1, branch3x3, branch3x3dbl, branch_pool ]) end_points['mixed_8x8x2048a'] = net # mixed_10: 8 x 8 x 2048. with tf.variable_scope('mixed_8x8x2048b'): with tf.variable_scope('branch1x1'): branch1x1 = ops.conv2d(net, 320, [1, 1]) with tf.variable_scope('branch3x3'): branch3x3 = ops.conv2d(net, 384, [1, 1]) branch3x3 = tf.concat( axis=3, values=[ ops.conv2d(branch3x3, 384, [1, 3]), ops.conv2d(branch3x3, 384, [3, 1]) ]) with tf.variable_scope('branch3x3dbl'): branch3x3dbl = ops.conv2d(net, 448, [1, 1]) branch3x3dbl = ops.conv2d(branch3x3dbl, 384, [3, 3]) branch3x3dbl = tf.concat( axis=3, values=[ ops.conv2d(branch3x3dbl, 384, [1, 3]), ops.conv2d(branch3x3dbl, 384, [3, 1]) ]) with tf.variable_scope('branch_pool'): branch_pool = ops.avg_pool(net, [3, 3]) branch_pool = ops.conv2d(branch_pool, 192, [1, 1]) net = tf.concat(axis=3, values=[ branch1x1, branch3x3, branch3x3dbl, branch_pool ]) end_points['mixed_8x8x2048b'] = net # Final pooling and prediction with tf.variable_scope('logits'): shape = net.get_shape() net = ops.avg_pool(net, shape[1:3], padding='VALID', scope='pool') # 1 x 1 x 2048 net = ops.dropout(net, dropout_keep_prob, scope='dropout') net = ops.flatten(net, scope='flatten') # 2048 logits = ops.fc(net, num_classes, activation=None, scope='logits', restore=restore_logits) # 1000 end_points['logits'] = logits end_points['predictions'] = tf.nn.softmax( logits, name='predictions') return logits, end_points
def testGlobalAvgPool(self): height, width = 3, 3 with self.test_session(): images = tf.random_uniform((5, height, width, 3), seed=1) output = ops.avg_pool(images, images.get_shape()[1:3], stride=1) self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
def testCreateAvgPoolWithScope(self): height, width = 3, 3 with self.test_session(): images = tf.random_uniform((5, height, width, 3), seed=1) output = ops.avg_pool(images, [3, 3], scope='pool1') self.assertEquals(output.op.name, 'pool1/AvgPool')