def get2d_deconv_output_size(input_height, input_width, filter_height,
filter_width, row_stride, col_stride, padding_type):
"""Returns the number of rows and columns in a convolution/pooling output."""
input_height = tensor_shape.as_dimension(input_height)
input_width = tensor_shape.as_dimension(input_width)
filter_height = tensor_shape.as_dimension(filter_height)
filter_width = tensor_shape.as_dimension(filter_width)
row_stride = int(row_stride)
col_stride = int(col_stride)
# Compute number of rows in the output, based on the padding.
if input_height.value is None or filter_height.value is None:
out_rows = None
elif padding_type == "VALID":
out_rows = (input_height.value - 1) * row_stride + filter_height.value
elif padding_type == "SAME":
out_rows = input_height.value * row_stride
else:
raise ValueError("Invalid value for padding: %r" % padding_type)
# Compute number of columns in the output, based on the padding.
if input_width.value is None or filter_width.value is None:
out_cols = None
elif padding_type == "VALID":
out_cols = (input_width.value - 1) * col_stride + filter_width.value
elif padding_type == "SAME":
out_cols = input_width.value * col_stride
return out_rows, out_cols
python类as_dimension()的实例源码
def get2d_deconv_output_size(input_height, input_width, filter_height,
filter_width, row_stride, col_stride, padding_type):
"""Returns the number of rows and columns in a convolution/pooling output."""
input_height = tensor_shape.as_dimension(input_height)
input_width = tensor_shape.as_dimension(input_width)
filter_height = tensor_shape.as_dimension(filter_height)
filter_width = tensor_shape.as_dimension(filter_width)
row_stride = int(row_stride)
col_stride = int(col_stride)
# Compute number of rows in the output, based on the padding.
if input_height.value is None or filter_height.value is None:
out_rows = None
elif padding_type == "VALID":
out_rows = (input_height.value - 1) * row_stride + filter_height.value
elif padding_type == "SAME":
out_rows = input_height.value * row_stride
else:
raise ValueError("Invalid value for padding: %r" % padding_type)
# Compute number of columns in the output, based on the padding.
if input_width.value is None or filter_width.value is None:
out_cols = None
elif padding_type == "VALID":
out_cols = (input_width.value - 1) * col_stride + filter_width.value
elif padding_type == "SAME":
out_cols = input_width.value * col_stride
return out_rows, out_cols
def get2d_deconv_output_size(input_height, input_width, filter_height,
filter_width, row_stride, col_stride, padding_type):
"""Returns the number of rows and columns in a convolution/pooling output."""
input_height = tensor_shape.as_dimension(input_height)
input_width = tensor_shape.as_dimension(input_width)
filter_height = tensor_shape.as_dimension(filter_height)
filter_width = tensor_shape.as_dimension(filter_width)
row_stride = int(row_stride)
col_stride = int(col_stride)
# Compute number of rows in the output, based on the padding.
if input_height.value is None or filter_height.value is None:
out_rows = None
elif padding_type == "VALID":
out_rows = (input_height.value - 1) * row_stride + filter_height.value
elif padding_type == "SAME":
out_rows = input_height.value * row_stride
else:
raise ValueError("Invalid value for padding: %r" % padding_type)
# Compute number of columns in the output, based on the padding.
if input_width.value is None or filter_width.value is None:
out_cols = None
elif padding_type == "VALID":
out_cols = (input_width.value - 1) * col_stride + filter_width.value
elif padding_type == "SAME":
out_cols = input_width.value * col_stride
return out_rows, out_cols
def get2d_deconv_output_size(input_height, input_width, filter_height,
filter_width, row_stride, col_stride, padding_type):
"""Returns the number of rows and columns in a convolution/pooling output."""
input_height = tensor_shape.as_dimension(input_height)
input_width = tensor_shape.as_dimension(input_width)
filter_height = tensor_shape.as_dimension(filter_height)
filter_width = tensor_shape.as_dimension(filter_width)
row_stride = int(row_stride)
col_stride = int(col_stride)
# Compute number of rows in the output, based on the padding.
if input_height.value is None or filter_height.value is None:
out_rows = None
elif padding_type == "VALID":
out_rows = (input_height.value - 1) * row_stride + filter_height.value
elif padding_type == "SAME":
out_rows = input_height.value * row_stride
else:
raise ValueError("Invalid value for padding: %r" % padding_type)
# Compute number of columns in the output, based on the padding.
if input_width.value is None or filter_width.value is None:
out_cols = None
elif padding_type == "VALID":
out_cols = (input_width.value - 1) * col_stride + filter_width.value
elif padding_type == "SAME":
out_cols = input_width.value * col_stride
return out_rows, out_cols
def __encoder(self, scope, input_tensor, bn_is_training, keep_prob, in_nch=1, reuse=False):
lf = self.layer_factory
input_tensor2d = tf.reshape(input_tensor, [self.flags.batch_size, \
self.flags.img_height, self.flags.img_width, in_nch])
if(self.nch == 1 and reuse==False):
tf.image_summary('summ_input_tensor2d', input_tensor2d, max_images=10)
nch = tensor_shape.as_dimension(input_tensor2d.get_shape()[3]).value
if(reuse==False):
W_conv1 = lf.weight_variable(name='W_conv1', shape=[5, 5, nch, 128])
W_conv2 = lf.weight_variable(name='W_conv2', shape=[5, 5, 128, 256])
W_conv3 = lf.weight_variable(name='W_conv3', shape=[5, 5, 256, 512])
W_conv4 = lf.weight_variable(name='W_conv4', shape=[4, 4, 512, 1024])
W_fc1 = lf.weight_variable(name='W_fc1', shape=[4*4*1024, self.flags.hidden_size * 2])
b_conv1 = lf.bias_variable(name='b_conv1', shape=[128])
b_conv2 = lf.bias_variable(name='b_conv2', shape=[256])
b_conv3 = lf.bias_variable(name='b_conv3', shape=[512])
b_conv4 = lf.bias_variable(name='b_conv4', shape=[1024])
b_fc1 = lf.bias_variable(name='b_fc1', shape=[self.flags.hidden_size * 2])
else:
W_conv1 = lf.weight_variable(name='W_conv1')
W_conv2 = lf.weight_variable(name='W_conv2')
W_conv3 = lf.weight_variable(name='W_conv3')
W_conv4 = lf.weight_variable(name='W_conv4')
W_fc1 = lf.weight_variable(name='W_fc1')
b_conv1 = lf.bias_variable(name='b_conv1')
b_conv2 = lf.bias_variable(name='b_conv2')
b_conv3 = lf.bias_variable(name='b_conv3')
b_conv4 = lf.bias_variable(name='b_conv4')
b_fc1 = lf.bias_variable(name='b_fc1')
conv1 = tf.nn.relu(lf.conv2d(input_tensor2d, W_conv1, stride=2) + b_conv1)
conv1_norm = lf.batch_norm_aiuiuc_wrapper(conv1, bn_is_training, \
'BN1', reuse_vars=reuse)
conv2 = tf.nn.relu(lf.conv2d(conv1_norm, W_conv2, stride=2) + b_conv2)
conv2_norm = lf.batch_norm_aiuiuc_wrapper(conv2, bn_is_training, \
'BN2', reuse_vars=reuse)
conv3 = tf.nn.relu(lf.conv2d(conv2_norm, W_conv3, stride=2) + b_conv3)
conv3_norm = lf.batch_norm_aiuiuc_wrapper(conv3, bn_is_training, \
'BN3', reuse_vars=reuse)
conv4 = tf.nn.relu(lf.conv2d(conv3_norm, W_conv4, stride=2) + b_conv4)
conv4_norm = lf.batch_norm_aiuiuc_wrapper(conv4, bn_is_training, \
'BN4', reuse_vars=reuse)
dropout1 = tf.nn.dropout(conv4_norm, keep_prob)
flatten1 = tf.reshape(dropout1, [-1, 4*4*1024])
fc1 = tf.matmul(flatten1, W_fc1)+b_fc1
return fc1
def __cond_encoder(self, scope, input_tensor, bn_is_training, keep_prob, in_nch=1, reuse=False):
lf = self.layer_factory
input_tensor2d = tf.reshape(input_tensor, [self.flags.batch_size, \
self.flags.img_height, self.flags.img_width, 1])
nch = tensor_shape.as_dimension(input_tensor2d.get_shape()[3]).value
nout = self.flags.hidden_size
if(reuse == False):
W_conv1 = lf.weight_variable(name='W_conv1_cond', shape=[5, 5, nch, 128])
W_conv2 = lf.weight_variable(name='W_conv2_cond', shape=[5, 5, 128, 256])
W_conv3 = lf.weight_variable(name='W_conv3_cond', shape=[5, 5, 256, 512])
W_conv4 = lf.weight_variable(name='W_conv4_cond', shape=[4, 4, 512, self.flags.hidden_size])
b_conv1 = lf.bias_variable(name='b_conv1_cond', shape=[128])
b_conv2 = lf.bias_variable(name='b_conv2_cond', shape=[256])
b_conv3 = lf.bias_variable(name='b_conv3_cond', shape=[512])
b_conv4 = lf.bias_variable(name='b_conv4_cond', shape=[self.flags.hidden_size])
else:
W_conv1 = lf.weight_variable(name='W_conv1_cond')
W_conv2 = lf.weight_variable(name='W_conv2_cond')
W_conv3 = lf.weight_variable(name='W_conv3_cond')
W_conv4 = lf.weight_variable(name='W_conv4_cond')
b_conv1 = lf.bias_variable(name='b_conv1_cond')
b_conv2 = lf.bias_variable(name='b_conv2_cond')
b_conv3 = lf.bias_variable(name='b_conv3_cond')
b_conv4 = lf.bias_variable(name='b_conv4_cond')
conv1 = tf.nn.relu(lf.conv2d(input_tensor2d, W_conv1, stride=2) + b_conv1)
conv1_norm = lf.batch_norm_aiuiuc_wrapper(conv1, bn_is_training, \
'BN1_cond', reuse_vars=reuse)
conv2 = tf.nn.relu(lf.conv2d(conv1_norm, W_conv2, stride=2) + b_conv2)
conv2_norm = lf.batch_norm_aiuiuc_wrapper(conv2, bn_is_training, \
'BN2_cond', reuse_vars=reuse)
conv3 = tf.nn.relu(lf.conv2d(conv2_norm, W_conv3, stride=2) + b_conv3)
conv3_norm = lf.batch_norm_aiuiuc_wrapper(conv3, bn_is_training, \
'BN3_cond', reuse_vars=reuse)
conv4 = tf.nn.relu(lf.conv2d(conv3_norm, W_conv4, stride=2) + b_conv4)
conv4_norm = lf.batch_norm_aiuiuc_wrapper(conv4, bn_is_training, \
'BN4_cond', reuse_vars=reuse)
return conv1_norm, conv2_norm, conv3_norm, conv4_norm
def __encoder(self, scope, input_tensor, bn_is_training, keep_prob, in_nch=2, reuse=False):
lf = self.layer_factory
input_tensor2d = tf.reshape(input_tensor, [self.flags.batch_size, \
self.flags.img_height, self.flags.img_width, in_nch])
nch = tensor_shape.as_dimension(input_tensor2d.get_shape()[3]).value
if(reuse==False):
W_conv1 = lf.weight_variable(name='W_conv1', shape=[5, 5, nch, 128])
W_conv2 = lf.weight_variable(name='W_conv2', shape=[5, 5, 128, 256])
W_conv3 = lf.weight_variable(name='W_conv3', shape=[5, 5, 256, 512])
W_conv4 = lf.weight_variable(name='W_conv4', shape=[4, 4, 512, 1024])
W_fc1 = lf.weight_variable(name='W_fc1', shape=[4*4*1024, self.flags.hidden_size * 2])
b_conv1 = lf.bias_variable(name='b_conv1', shape=[128])
b_conv2 = lf.bias_variable(name='b_conv2', shape=[256])
b_conv3 = lf.bias_variable(name='b_conv3', shape=[512])
b_conv4 = lf.bias_variable(name='b_conv4', shape=[1024])
b_fc1 = lf.bias_variable(name='b_fc1', shape=[self.flags.hidden_size * 2])
else:
W_conv1 = lf.weight_variable(name='W_conv1')
W_conv2 = lf.weight_variable(name='W_conv2')
W_conv3 = lf.weight_variable(name='W_conv3')
W_conv4 = lf.weight_variable(name='W_conv4')
W_fc1 = lf.weight_variable(name='W_fc1')
b_conv1 = lf.bias_variable(name='b_conv1')
b_conv2 = lf.bias_variable(name='b_conv2')
b_conv3 = lf.bias_variable(name='b_conv3')
b_conv4 = lf.bias_variable(name='b_conv4')
b_fc1 = lf.bias_variable(name='b_fc1')
conv1 = tf.nn.relu(lf.conv2d(input_tensor2d, W_conv1, stride=2) + b_conv1)
conv1_norm = lf.batch_norm_aiuiuc_wrapper(conv1, bn_is_training, \
'BN1', reuse_vars=reuse)
conv2 = tf.nn.relu(lf.conv2d(conv1_norm, W_conv2, stride=2) + b_conv2)
conv2_norm = lf.batch_norm_aiuiuc_wrapper(conv2, bn_is_training, \
'BN2', reuse_vars=reuse)
conv3 = tf.nn.relu(lf.conv2d(conv2_norm, W_conv3, stride=2) + b_conv3)
conv3_norm = lf.batch_norm_aiuiuc_wrapper(conv3, bn_is_training, \
'BN3', reuse_vars=reuse)
conv4 = tf.nn.relu(lf.conv2d(conv3_norm, W_conv4, stride=2) + b_conv4)
conv4_norm = lf.batch_norm_aiuiuc_wrapper(conv4, bn_is_training, \
'BN4', reuse_vars=reuse)
dropout1 = tf.nn.dropout(conv4_norm, keep_prob)
flatten1 = tf.reshape(dropout1, [-1, 4*4*1024])
fc1 = tf.matmul(flatten1, W_fc1)+b_fc1
return fc1
