我们从Python开源项目中，提取了以下41个代码示例，用于说明如何使用tensorflow.contrib.layers.conv2d_transpose()。
def autoencoder(inputs): # encoder # 32 x 32 x 1 -> 16 x 16 x 32 # 16 x 16 x 32 -> 8 x 8 x 16 # 8 x 8 x 16 -> 2 x 2 x 8 net = lays.conv2d(inputs, 32, [5, 5], stride=2, padding='SAME') net = lays.conv2d(net, 16, [5, 5], stride=2, padding='SAME') net = lays.conv2d(net, 8, [5, 5], stride=4, padding='SAME') # decoder # 2 x 2 x 8 -> 8 x 8 x 16 # 8 x 8 x 16 -> 16 x 16 x 32 # 16 x 16 x 32 -> 32 x 32 x 1 net = lays.conv2d_transpose(net, 16, [5, 5], stride=4, padding='SAME') net = lays.conv2d_transpose(net, 32, [5, 5], stride=2, padding='SAME') net = lays.conv2d_transpose(net, 1, [5, 5], stride=2, padding='SAME', activation_fn=tf.nn.tanh) return net # read MNIST dataset
def generator(input_latent): # input_latent = Input(batch_shape=noise_dim, dtype=im_dtype) with tf.variable_scope('Net_Gen') as scope: weight_decay = 0.0001 xx = layers.fully_connected(input_latent, num_outputs=4*4*512, activation_fn=None) xx = layers.batch_norm(xx) xx = tf.nn.relu(xx) xx = tf.reshape(xx, (batch_size, 4,4,512)) xx = layers.conv2d_transpose(xx, 256, kernel_size=(5,5), stride=(2, 2), padding='SAME', activation_fn=None) xx = layers.batch_norm(xx) xx = tf.nn.relu(xx) xx = layers.conv2d_transpose(xx, 128, kernel_size=(5,5), stride=(2, 2), padding='SAME', activation_fn=None) xx = layers.batch_norm(xx) xx = tf.nn.relu(xx) xx = layers.conv2d_transpose(xx, 3, kernel_size=(5,5), stride=(2, 2), padding='SAME', activation_fn=None) xx = layers.batch_norm(xx) gen_dat = tf.nn.tanh(xx) return gen_dat # specify discriminative model
def generator(input_latent): # input_latent = Input(batch_shape=noise_dim, dtype=im_dtype) with tf.variable_scope('Net_Gen') as scope: xx = layers.fully_connected(input_latent, num_outputs=4*4*512, activation_fn=None) xx = layers.batch_norm(xx) xx = tf.nn.relu(xx) xx = tf.reshape(xx, (batch_size, 4,4,512)) xx = layers.conv2d_transpose(xx, 256, kernel_size=(5,5), stride=(2, 2), padding='SAME', activation_fn=None) xx = layers.batch_norm(xx) xx = tf.nn.relu(xx) xx = layers.conv2d_transpose(xx, 128, kernel_size=(5,5), stride=(2, 2), padding='SAME', activation_fn=None) xx = layers.batch_norm(xx) xx = tf.nn.relu(xx) xx = layers.conv2d_transpose(xx, 3, kernel_size=(5,5), stride=(2, 2), padding='SAME', activation_fn=None) xx = layers.batch_norm(xx) gen_dat = tf.nn.tanh(xx) return gen_dat
def generator(z): # because up to now we can not derive bias_add's higher order derivative in tensorflow, # so I use vanilla implementation of FC instead of a FC layer in tensorflow.contrib.layers # the following conv case is out of the same reason weights = slim.model_variable( 'fn_weights', shape=(FLAGS.z_dim, 4 * 4 * 512), initializer=ly.xavier_initializer()) bias = slim.model_variable( 'fn_bias', shape=(4 * 4 * 512, ), initializer=tf.zeros_initializer) train = tf.nn.relu(ly.batch_norm(fully_connected(z, weights, bias))) train = tf.reshape(train, (-1, 4, 4, 512)) train = ly.conv2d_transpose(train, 256, 3, stride=2, activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm, padding='SAME') train = ly.conv2d_transpose(train, 128, 3, stride=2, activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm, padding='SAME') train = ly.conv2d_transpose(train, 64, 3, stride=2, activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm, padding='SAME') train = ly.conv2d_transpose(train, 1, 3, stride=1, activation_fn=None, padding='SAME', biases_initializer=None) bias = slim.model_variable('bias', shape=( 1, ), initializer=tf.zeros_initializer) train += bias train = tf.nn.tanh(train) return train
def generator(z, label): z = tf.concat(1, [z,label]) train = ly.fully_connected( z, 1024, activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm) train = tf.concat(1, [train, label]) train = ly.fully_connected( z, 4*4*512, activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm) train = tf.reshape(train, (-1, 4, 4, 512)) yb = tf.ones([FLAGS.batch_size, 4, 4, 10])*tf.reshape(label, [FLAGS.batch_size, 1, 1, 10]) train = tf.concat(3, [train, yb]) train = ly.conv2d_transpose(train, 256, 3, stride=2, activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) train = ly.conv2d_transpose(train, 128, 3, stride=2, activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) train = ly.conv2d_transpose(train, 64, 3, stride=2, activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) train = ly.conv2d_transpose(train, 1, 3, stride=1, activation_fn=tf.nn.tanh, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) return train
def generator(z): weights = slim.model_variable( 'fn_weights', shape=(FLAGS.z_dim, 4 * 4 * 512), initializer=ly.xavier_initializer()) bias = slim.model_variable( 'fn_bias', shape=(4 * 4 * 512, ), initializer=tf.zeros_initializer) train = tf.nn.relu(fully_connected(z, weights, bias)) train = tf.reshape(train, (-1, 4, 4, 512)) train = ly.conv2d_transpose(train, 256, 3, stride=2, activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) train = ly.conv2d_transpose(train, 128, 3, stride=2, activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) train = ly.conv2d_transpose(train, 64, 3, stride=2, activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) train = ly.conv2d_transpose(train, 1, 3, stride=1, activation_fn=None, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02), biases_initializer=None) bias = slim.model_variable('bias', shape=( 1, ), initializer=tf.zeros_initializer) train += bias train = tf.nn.tanh(train) return train
def testOutputSizeWithStrideOneSamePaddingNCHW(self): # `NCHW` data fomat is only supported for `GPU` device. if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True) as sess: num_filters = 32 input_size = [5, 3, 10, 12] expected_size = [5, num_filters, 10, 12] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [3, 3], stride=1, padding='SAME', data_format='NCHW') self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') sess.run(variables_lib.global_variables_initializer()) self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeWithStrideOneValidPaddingNCHW(self): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True) as sess: num_filters = 32 input_size = [5, 3, 10, 12] expected_size = [5, num_filters, 12, 14] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [3, 3], stride=1, padding='VALID', data_format='NCHW') self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') sess.run(variables_lib.global_variables_initializer()) self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeWithStrideTwoValidPaddingNCHW(self): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True) as sess: num_filters = 32 input_size = [5, 3, 9, 11] expected_size = [5, num_filters, 19, 23] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [3, 3], stride=[2, 2], padding='VALID', data_format='NCHW') self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.get_shape().as_list()), expected_size) sess.run(variables_lib.global_variables_initializer()) self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeWith1x1StrideTwoSamePaddingNCHW(self): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True) as sess: num_filters = 1 input_size = [1, 1, 1, 1] expected_size = [1, num_filters, 2, 2] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 2], stride=[2, 2], padding='SAME', data_format='NCHW') self.assertListEqual(list(output.get_shape().as_list()), expected_size) sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeWith2x2StrideTwoSamePaddingNCHW(self): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True) as sess: num_filters = 1 input_size = [1, 1, 2, 2] expected_size = [1, num_filters, 4, 4] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 2], stride=[2, 2], padding='SAME', data_format='NCHW') sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeWith2x2StrideTwoValidPaddingNCHW(self): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True) as sess: num_filters = 1 input_size = [1, 1, 2, 2] expected_size = [1, num_filters, 4, 4] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 2], stride=[2, 2], padding='VALID', data_format='NCHW') sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeWithStride2x1NCHW(self): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True) as sess: num_filters = 1 input_size = [1, 1, 3, 2] expected_size = [1, num_filters, 6, 5] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 4], stride=[2, 1], padding='VALID', data_format='NCHW') sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeWithStride2x4NCHW(self): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True) as sess: num_filters = 1 input_size = [1, 1, 3, 2] expected_size = [1, num_filters, 6, 8] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 4], stride=[2, 4], padding='VALID', data_format='NCHW') sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeWithStride2x5NCHW(self): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True) as sess: num_filters = 1 input_size = [1, 1, 3, 2] expected_size = [1, num_filters, 6, 10] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 4], stride=[2, 5], padding='VALID', data_format='NCHW') sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size)
def testDynamicOutputSizeWithStrideTwoValidPadding(self): num_filters = 32 input_size = [5, 9, 11, 3] expected_size = [None, None, None, num_filters] expected_size_dynamic = [5, 19, 23, num_filters] images = array_ops.placeholder(np.float32, [None, None, None, input_size[3]]) output = layers_lib.conv2d_transpose( images, num_filters, [3, 3], stride=[2, 2], padding='VALID') self.assertListEqual(output.get_shape().as_list(), expected_size) with self.test_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') eval_output = output.eval({images: np.zeros(input_size, np.float32)}) self.assertListEqual(list(eval_output.shape), expected_size_dynamic)
def testWithScopeWithoutActivation(self): num_filters = 32 input_size = [5, 9, 11, 3] expected_size = [5, 19, 23, num_filters] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [3, 3], stride=2, padding='VALID', activation_fn=None, scope='conv7') self.assertEqual(output.op.name, 'conv7/BiasAdd') with self.test_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertListEqual(list(output.eval().shape), expected_size)
def up_block(block_fn, filters): def f(inputs, down): inputs_ = tcl.conv2d_transpose(down, # double size of 'down' num_outputs = inputs.shape.as_list()[3], kernel_size = (2, 2), stride = (2, 2), padding = 'SAME') x = tf.concat([inputs, inputs_], axis = 3) x = block_fn(filters)(x) x = block_fn(filters)(x) return x # same size of 'inputs' return f
def semi_supervised_decoder_convolutional(input_tensor, batch_size, n_dimensions, network_scale=1.0, img_res=28, img_channels=1): f_multiplier = network_scale net = layers.fully_connected(input_tensor, 2*2*int(128*f_multiplier)) net = tf.reshape(net, [-1, 2, 2, int(128*f_multiplier)]) assert(img_res in [28, 32]) if img_res==28: net = layers.conv2d_transpose(net, int(64*f_multiplier), 3, stride=2) net = layers.conv2d_transpose(net, int(64*f_multiplier), 3, stride=1) net = layers.conv2d_transpose(net, int(32*f_multiplier), 4, stride=1, padding='VALID') net = layers.conv2d_transpose(net, int(32*f_multiplier), 4, stride=1) net = layers.conv2d_transpose(net, int(16*f_multiplier), 3, stride=2) net = layers.conv2d_transpose(net, int(16*f_multiplier), 3, stride=1) net = layers.conv2d_transpose(net, int(8*f_multiplier), 3, stride=2) net = layers.conv2d_transpose(net, int(8*f_multiplier), 3, stride=1) else: net = layers.conv2d_transpose(net, int(64*f_multiplier), 3, stride=2) net = layers.conv2d_transpose(net, int(64*f_multiplier), 3, stride=1) net = layers.conv2d_transpose(net, int(32*f_multiplier), 3, stride=2) net = layers.conv2d_transpose(net, int(32*f_multiplier), 3, stride=1) net = layers.conv2d_transpose(net, int(16*f_multiplier), 3, stride=2) net = layers.conv2d_transpose(net, int(16*f_multiplier), 3, stride=1) net = layers.conv2d_transpose(net, int(8*f_multiplier), 3, stride=2) net = layers.conv2d_transpose(net, int(8*f_multiplier), 3, stride=1) net = layers.conv2d_transpose(net, img_channels, 5, stride=1, activation_fn=tf.nn.sigmoid) net = layers.flatten(net) return net
def vae_conv(observed, n, n_x, n_z, n_particles, is_training): with zs.BayesianNet(observed=observed) as model: normalizer_params = {'is_training': is_training, 'updates_collections': None} z_mean = tf.zeros([n, n_z]) z = zs.Normal('z', z_mean, std=1., n_samples=n_particles, group_ndims=1) lx_z = tf.reshape(z, [-1, 1, 1, n_z]) lx_z = layers.conv2d_transpose( lx_z, 128, kernel_size=3, padding='VALID', normalizer_fn=layers.batch_norm, normalizer_params=normalizer_params) lx_z = layers.conv2d_transpose( lx_z, 64, kernel_size=5, padding='VALID', normalizer_fn=layers.batch_norm, normalizer_params=normalizer_params) lx_z = layers.conv2d_transpose( lx_z, 32, kernel_size=5, stride=2, normalizer_fn=layers.batch_norm, normalizer_params=normalizer_params) lx_z = layers.conv2d_transpose( lx_z, 1, kernel_size=5, stride=2, activation_fn=None) x_logits = tf.reshape(lx_z, [n_particles, n, -1]) x = zs.Bernoulli('x', x_logits, group_ndims=1) return model
def __call__(self, z): with tf.variable_scope(self.name) as scope: g = tcl.fully_connected(z, self.size * self.size * 512, activation_fn=tf.nn.relu, normalizer_fn=tcl.batch_norm) g = tf.reshape(g, (-1, self.size, self.size, 512)) # size g = tcl.conv2d_transpose(g, 256, 3, stride=2, # size*2 activation_fn=tf.nn.relu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) g = tcl.conv2d_transpose(g, 128, 3, stride=2, # size*4 activation_fn=tf.nn.relu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) g = tcl.conv2d_transpose(g, 64, 3, stride=2, # size*8 32x32x64 activation_fn=tf.nn.relu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) g = tcl.conv2d_transpose(g, self.channel, 3, stride=2, # size*16 activation_fn=tf.nn.sigmoid, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) return g
def __call__(self, x, reuse=False): with tf.variable_scope(self.name) as scope: if reuse: scope.reuse_variables() # --- conv size = 64 d = tcl.conv2d(x, num_outputs=size, kernel_size=3, # bzx64x64x3 -> bzx32x32x64 stride=2, activation_fn=lrelu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) d = tcl.conv2d(d, num_outputs=size * 2, kernel_size=3, # 16x16x128 stride=2, activation_fn=lrelu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) d = tcl.conv2d(d, num_outputs=size * 4, kernel_size=3, # 8x8x256 stride=2, activation_fn=lrelu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) d = tcl.conv2d(d, num_outputs=size * 8, kernel_size=3, # 4x4x512 stride=2, activation_fn=lrelu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) h = tcl.fully_connected(tcl.flatten(d), self.n_hidden, activation_fn=lrelu, weights_initializer=tf.random_normal_initializer(0, 0.02)) # -- deconv d = tcl.fully_connected(h, 4 * 4 * 512, activation_fn=tf.nn.relu, normalizer_fn=tcl.batch_norm) d = tf.reshape(d, (-1, 4, 4, 512)) # size d = tcl.conv2d_transpose(d, 256, 3, stride=2, # size*2 activation_fn=tf.nn.relu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) d = tcl.conv2d_transpose(d, 128, 3, stride=2, # size*4 activation_fn=tf.nn.relu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) d = tcl.conv2d_transpose(d, 64, 3, stride=2, # size*8 activation_fn=tf.nn.relu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) d = tcl.conv2d_transpose(d, 3, 3, stride=2, # size*16 activation_fn=tf.nn.sigmoid, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02)) return d
def resBlock(inputs, input_num, output_num, kernel_size, resample=None): """ resample: None, 'down', or 'up' """ if resample == 'down': conv_shortcut = functools.partial(tcl.conv2d, stride=2) conv_1 = functools.partial(tcl.conv2d, num_outputs=input_num/2) conv_1b = functools.partial(tcl.conv2d, num_outputs=output_num/2, stride=2) conv_2 = functools.partial(tcl.conv2d, num_outputs=output_num) elif resample == 'up': conv_shortcut = subpixelConv2D conv_1 = functools.partial(tcl.conv2d, num_outputs=input_num/2) conv_1b = functools.partial(tcl.conv2d_transpose, num_outputs=output_num/2, stride=2) conv_2 = functools.partial(tcl.conv2d, num_outputs=output_num) elif resample == None: conv_shortcut = tcl.conv2d conv_1 = functools.partial(tcl.conv2d, num_outputs=input_num/2) conv_1b = functools.partial(tcl.conv2d, num_outputs=output_num/2) conv_2 = functools.partial(tcl.conv2d, num_outputs=output_num) else: raise Exception('invalid resample value') if output_num==input_num and resample==None: shortcut = inputs # Identity skip-connection else: shortcut = conv_shortcut(inputs=inputs, num_outputs=output_num, kernel_size=1) # Should kernel_size be larger? output = inputs output = conv_1(inputs=output, kernel_size=1) output = conv_1b(inputs=output, kernel_size=kernel_size) output = conv_2(inputs=output, kernel_size=1, biases_initializer=None) # Should skip bias here? # output = Batchnorm(name+'.BN', [0,2,3], output) # Should skip BN op here? return shortcut + (0.3*output) # use depth-to-space for upsampling image
def conv2d_transpose( inputs , outputs_dim , kernel_size , stride , padding = "SAME" , he_init = False , activation_fn = None , regularization_scale = 0.0 ): C = inputs.get_shape()[-1].value fan_in = C * kernel_size**2 / stride**2 fan_out = C * kernel_size**2 avg_fan = ( fan_in + fan_out ) / 2 if he_init: var = 2.0/avg_fan else : var = 1.0/avg_fan # var = ( b - a )**2 /12 , b==-a , (zero mean) upper_bound = np.sqrt( 12.0 * var ) *0.5 weights_initializer = tf.random_uniform_initializer( -upper_bound , upper_bound , seed = None , dtype = tf.float32 ) weights_regularizer = layers.l2_regularizer( scale = regularization_scale ) return layers.conv2d_transpose( inputs , outputs_dim , kernel_size = kernel_size , stride = stride , padding = padding , weights_initializer = weights_initializer , activation_fn = activation_fn , weights_regularizer = weights_regularizer )
def upsample( inputs , scale , dim , upsample_method = "subpixel" , activation_fn = None , regularization_scale = 0.0 ): "upsample layer" act = activation_fn if act == None: act = tf.identity #with tf.variable_scope(scope) as scope : if upsample_method == "subpixel": if scale == 2 : outputs = conv2d( inputs , dim * 2**2, 3 , 1 , he_init = (activation_fn == tf.nn.relu ) , activation_fn = activation_fn , regularization_scale = regularization_scale ) outputs = tf.depth_to_space( outputs , 2 ) outputs = act( outputs ) elif scale == 3 : outputs = conv2d( inputs , dim * 3**2 , 3 , 1 , he_init = (activation_fn == tf.nn.relu ) , activation_fn = activation_fn , regularization_scale = regularization_scale ) outputs = tf.depth_to_space( outputs , 3 ) outputs = act( outputs ) elif scale == 4 : outputs = conv2d( inputs , dim * 2**2, 3 , 1 , regularization_scale = regularization_scale ) outputs = tf.depth_to_space( outputs , 2 ) outputs = conv2d( outputs , dim * 2**2 , 3 , 1 , he_init = (activation_fn == tf.nn.relu ) , activation_fn = activation_fn , regularization_scale = regularization_scale ) outputs = tf.depth_to_space( outputs , 2 ) outputs = act( outputs ) elif upsample_method == "conv_transpose": if scale == 2 : outputs = utils.conv2d_transpose( inputs , dim , 3 , 2 , he_init = (activation_fn == tf.nn.relu ) , activation_fn = activation_fn , regularization_scale = regularization_scale ) outputs = act( outputs ) elif scale == 3: outputs = utils.conv2d_transpose( inputs , dim , 3 , 3 , he_init = (activation_fn == tf.nn.relu ) , activation_fn = activation_fn , regularization_scale = regularization_scale ) outputs = act( outputs ) elif scale == 4: outputs = utils.conv2d_transpose( inputs , dim , 3 , 2 , regularization_scale = regularization_scale ) outputs = utils.conv2d_transpose( outputs , dim , 3 , 2 , he_init = (activation_fn == tf.nn.relu ) , activation_fn = activation_fn , regularization_scale = regularization_scale ) outputs = act( outputs ) return outputs
def generator(inputs, reuse=False): with tf.variable_scope('generator'): if reuse: tf.get_variable_scope().reuse_variables() net = lays.fully_connected(inputs, 4*4*256, scope='fc1') net = tf.reshape(net, (batch_size, 4, 4, 256)) net = lays.conv2d_transpose(net, 128, 3, stride=2, scope='conv1', padding='SAME', activation_fn=leaky_relu) net = lays.conv2d_transpose(net, 64, 3, stride=2, scope='conv2', padding='SAME', activation_fn=leaky_relu) net = lays.conv2d_transpose(net, 64, 3, stride=2, scope='conv3', padding='SAME', activation_fn=leaky_relu) net = lays.conv2d(net, 3, 3, scope='conv4', padding='SAME', activation_fn=tf.nn.tanh) return net # Define the Discriminator, a simple CNN with 3 convolution and 2 fully connected layers
def discriminator(inputs, reuse=False): with tf.variable_scope('discriminator'): if reuse: tf.get_variable_scope().reuse_variables() net = lays.conv2d_transpose(inputs, 64, 3, stride=1, scope='conv1', padding='SAME', activation_fn=leaky_relu) net = lays.max_pool2d(net, 2, 2, 'SAME', scope='max1') net = lays.conv2d_transpose(net, 128, 3, stride=1, scope='conv2', padding='SAME', activation_fn=leaky_relu) net = lays.max_pool2d(net, 2, 2, 'SAME', scope='max2') net = lays.conv2d_transpose(net, 256, 3, stride=1, scope='conv3', padding='SAME', activation_fn=leaky_relu) net = lays.max_pool2d(net, 2, 2, 'SAME', scope='max3') net = tf.reshape(net, (batch_size, 4 * 4 * 256)) net = lays.fully_connected(net, 128, scope='fc1', activation_fn=leaky_relu) net = lays.dropout(net, 0.5) net = lays.fully_connected(net, 1, scope='fc2', activation_fn=None) return net
def testTrainableFlagIsPassedOn(self): for trainable in [True, False]: with ops.Graph().as_default(): num_filters = 32 input_size = [5, 10, 12, 3] images = random_ops.random_uniform(input_size, seed=1) layers_lib.conv2d_transpose( images, num_filters, [3, 3], stride=1, trainable=trainable) model_variables = variables.get_model_variables() trainable_variables = variables_lib.trainable_variables() for model_variable in model_variables: self.assertEqual(trainable, model_variable in trainable_variables)
def testOutputSizeWithStrideOneValidPadding(self): num_filters = 32 input_size = [5, 10, 12, 3] expected_size = [5, 12, 14, num_filters] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [3, 3], stride=1, padding='VALID') self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') with self.test_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeWithStrideTwoValidPadding(self): num_filters = 32 input_size = [5, 9, 11, 3] expected_size = [5, 19, 23, num_filters] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [3, 3], stride=[2, 2], padding='VALID') self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.get_shape().as_list()), expected_size) with self.test_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeWith1x1StrideTwoSamePadding(self): num_filters = 1 input_size = [1, 1, 1, 1] expected_size = [1, 2, 2, num_filters] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 2], stride=[2, 2], padding='SAME') self.assertListEqual(list(output.get_shape().as_list()), expected_size) with self.test_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeWith1x1StrideTwoValidPadding(self): num_filters = 1 input_size = [1, 1, 1, 1] expected_size = [1, 2, 2, num_filters] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 2], stride=[2, 2], padding='VALID') with self.test_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeWith2x2StrideTwoSamePadding(self): num_filters = 1 input_size = [1, 2, 2, 1] expected_size = [1, 4, 4, num_filters] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 2], stride=[2, 2], padding='SAME') with self.test_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeWithStride2x1(self): num_filters = 1 input_size = [1, 3, 2, 1] expected_size = [1, 6, 5, num_filters] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 4], stride=[2, 1], padding='VALID') with self.test_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeWithStride2x4(self): num_filters = 1 input_size = [1, 3, 2, 1] expected_size = [1, 6, 8, num_filters] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 4], stride=[2, 4], padding='VALID') with self.test_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeWithStride2x5(self): num_filters = 1 input_size = [1, 3, 2, 1] expected_size = [1, 6, 10, num_filters] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 4], stride=[2, 5], padding='VALID') with self.test_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size)
def testOutputSizeRandomSizesAndStridesValidPadding(self): np.random.seed(0) max_image_size = 10 for _ in range(10): num_filters = 1 input_size = [ 1, np.random.randint(1, max_image_size), np.random.randint(1, max_image_size), 1 ] filter_size = [ np.random.randint(1, input_size[1] + 1), np.random.randint(1, input_size[2] + 1) ] stride = [np.random.randint(1, 3), np.random.randint(1, 3)] ops.reset_default_graph() graph = ops.Graph() with graph.as_default(): images = random_ops.random_uniform(input_size, seed=1) transpose = layers_lib.conv2d_transpose( images, num_filters, filter_size, stride=stride, padding='VALID') conv = layers_lib.conv2d( transpose, num_filters, filter_size, stride=stride, padding='VALID') with self.test_session(graph=graph) as sess: sess.run(variables_lib.global_variables_initializer()) self.assertListEqual(list(conv.eval().shape), input_size)
def testWithScope(self): num_filters = 32 input_size = [5, 9, 11, 3] expected_size = [5, 19, 23, num_filters] images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [3, 3], stride=2, padding='VALID', scope='conv7') self.assertEqual(output.op.name, 'conv7/Relu') with self.test_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertListEqual(list(output.eval().shape), expected_size)
def testDeconvWithoutBiasesProducesConv2dTranspose(self): num_filters = 32 input_size = [5, 9, 11, 3] expected_size = [5, 19, 23, num_filters] stride = 2 padding = 'VALID' with self.test_session() as sess: images = random_ops.random_uniform(input_size, seed=1) output_deconv = layers_lib.conv2d_transpose( images, num_filters, [3, 3], stride=stride, padding=padding, activation_fn=None, scope='conv7') weights = variables.get_variables_by_name('conv7/weights')[0] output_conv2d_transpose = nn_ops.conv2d_transpose( images, weights, expected_size, [1, stride, stride, 1], padding=padding) sess.run(variables_lib.global_variables_initializer()) output_deconv, output_conv2d_transpose = sess.run( [output_deconv, output_conv2d_transpose]) self.assertTrue( np.isclose(output_deconv, output_conv2d_transpose, 1e-5, 1e-5).all())
