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- if loss_fn == 0: self.gen_loss = tf.reduce_mean ( msa.tf.mmath.ce_loss(msa.tf.mmath.lmap(self.x, input_range, (0,1)), msa.tf.mmath.lmap(self.y, output_range, (0,1))) )# / data_size
- elif loss_fn == 1: self.gen_loss = self.l1_loss
- elif loss_fn == 2: self.gen_loss = self.l2_loss
- elif loss_fn == 3: self.gen_loss = 1.0 - tf.reduce_mean( msa.tf.ssim.ms_ssim(self.x, self.y) ) # see end of post for ms_ssim
- self.kl_loss = tf.reduce_mean ( msa.tf.mmath.kl_loss(self.z_mu, self.z_log_sigma_sq) )
- self.loss = kl_weight * self.kl_loss + self.gen_loss
- i:1 e:0.006 | gen_loss:0.9598 kl_loss:0.0000 l1_loss:0.5096 l2_loss:0.3638 loss:0.9598
- i:2 e:0.011 | gen_loss:0.9582 kl_loss:0.0000 l1_loss:0.5017 l2_loss:0.3555 loss:0.9582
- i:3 e:0.017 | gen_loss:0.9633 kl_loss:0.0000 l1_loss:0.5131 l2_loss:0.3693 loss:0.9633
- i:4 e:0.022 | gen_loss:0.9583 kl_loss:0.0000 l1_loss:0.4945 l2_loss:0.3424 loss:0.9583
- i:5 e:0.028 | gen_loss:0.9490 kl_loss:0.0000 l1_loss:0.4848 l2_loss:0.3359 loss:0.9490
- i:6 e:0.033 | gen_loss:0.9493 kl_loss:0.0000 l1_loss:0.5229 l2_loss:0.3786 loss:0.9493
- i:7 e:0.039 | gen_loss:0.9400 kl_loss:0.0000 l1_loss:0.4548 l2_loss:0.2993 loss:0.9400
- i:8 e:0.044 | gen_loss:0.9497 kl_loss:0.0000 l1_loss:0.4968 l2_loss:0.3501 loss:0.9498
- i:9 e:0.050 | gen_loss:nan kl_loss:0.0000 l1_loss:0.4854 l2_loss:0.3355 loss:nan
- InvalidArgumentError: Nan in summary histogram for: z [[Node: z = HistogramSummary[T=DT_FLOAT,
- _device="/job:localhost/replica:0/task:0/device:CPU:0"](z/tag, vae/add/_41)]]
- Caused by op u'z', defined at: File "/home/memo/anaconda2/lib/python2.7/site-packages/spyder/utils/ipython/start_kernel.py", line 227, in <module>
- main() File "/home/memo/anaconda2/lib/python2.7/site-packages/spyder/utils/ipython/start_kernel.py", line 223, in main
- kernel.start() File "/home/memo/anaconda2/lib/python2.7/site-packages/ipykernel/kernelapp.py", line 474, in start
- ioloop.IOLoop.instance().start() File "/home/memo/anaconda2/lib/python2.7/site-packages/zmq/eventloop/ioloop.py", line 177, in start
- super(ZMQIOLoop, self).start() File "/home/memo/anaconda2/lib/python2.7/site-packages/tornado/ioloop.py", line 888, in start
- handler_func(fd_obj, events) File "/home/memo/anaconda2/lib/python2.7/site-packages/tornado/stack_context.py", line 277, in null_wrapper
- return fn(*args, **kwargs) File "/home/memo/anaconda2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
- self._handle_recv() File "/home/memo/anaconda2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
- self._run_callback(callback, msg) File "/home/memo/anaconda2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
- callback(*args, **kwargs) File "/home/memo/anaconda2/lib/python2.7/site-packages/tornado/stack_context.py", line 277, in null_wrapper
- return fn(*args, **kwargs) File "/home/memo/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 276, in dispatcher
- return self.dispatch_shell(stream, msg) File "/home/memo/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell
- handler(stream, idents, msg) File "/home/memo/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 390, in execute_request
- user_expressions, allow_stdin) File "/home/memo/anaconda2/lib/python2.7/site-packages/ipykernel/ipkernel.py", line 196, in do_execute
- res = shell.run_cell(code, store_history=store_history, silent=silent) File "/home/memo/anaconda2/lib/python2.7/site-packages/ipykernel/zmqshell.py", line 501, in run_cell
- return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs) File "/home/memo/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2717, in run_cell
- interactivity=interactivity, compiler=compiler, result=result) File "/home/memo/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2827, in run_ast_nodes
- if self.run_code(code, result): File "/home/memo/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code
- exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-1-1c98e289993a>", line 1, in <module>
- runfile('/home/memo/Dropbox/research/pypackages/msa/__tests/train_autovae.py', wdir='/home/memo/Dropbox/research/pypackages/msa/__tests') File "/home/memo/anaconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 866, in runfile
- execfile(filename, namespace) File "/home/memo/anaconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 94, in execfile
- builtins.execfile(filename, *where) File "/home/memo/Dropbox/research/pypackages/msa/__tests/train_autovae.py", line 88, in <module>
- adam_beta1 = a.adam_beta1, File "/mnt/data/Dropbox/research/pypackages/msa/tf/models/autovae.py", line 385, in __init__
- if log_z: tf.summary.histogram('z', self.z) File "/home/memo/anaconda2/lib/python2.7/site-packages/tensorflow/python/summary/summary.py", line 203, in histogram
- tag=tag, values=values, name=scope) File "/home/memo/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_logging_ops.py", line 283, in histogram_summary
- "HistogramSummary", tag=tag, values=values, name=name) File "/home/memo/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
- op_def=op_def) File "/home/memo/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3392, in create_op
- op_def=op_def) File "/home/memo/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1718, in __init__
- self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
- InvalidArgumentError (see above for traceback): Nan in summary histogram for: z [[Node: z = HistogramSummary[T=DT_FLOAT,
- _device="/job:localhost/replica:0/task:0/device:CPU:0"](z/tag, vae/add/_41)]]
- """
- Structural Similarity index for images in tensorflow
- adapted from
- https://stackoverflow.com/questions/39051451/ssim-ms-ssim-for-tensorflow
- """
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
- import tensorflow as tf
- import numpy as np
- import math
- EPS = 1e-6
- def _fspecial_gauss(size, sigma):
- """Function to mimic the 'fspecial' gaussian MATLAB function
- """
- if type(size)==int: size=(size,size)
- y_data, x_data = np.mgrid[-size[0]//2 + 1:size[0]//2 + 1, -size[1]//2 + 1:size[1]//2 + 1]
- x_data = np.expand_dims(x_data, axis=-1)
- x_data = np.expand_dims(x_data, axis=-1)
- y_data = np.expand_dims(y_data, axis=-1)
- y_data = np.expand_dims(y_data, axis=-1)
- x = tf.constant(x_data, dtype=tf.float32)
- y = tf.constant(y_data, dtype=tf.float32)
- g = tf.exp(-((x**2 + y**2)/(2.0*sigma**2)))
- return g / tf.reduce_sum(g)
- def ssim(img1, img2, cs_map=False, mean_metric=True, size=11, sigma=1.5):
- # convert multichannel (e.g. RGB) images to batch
- img1 = tf.expand_dims(tf.concat(tf.unstack(img1, axis=-1), axis=0), axis=-1)
- img2 = tf.expand_dims(tf.concat(tf.unstack(img2, axis=-1), axis=0), axis=-1)
- window = _fspecial_gauss(size, sigma) # window shape [size, size, 1, 1]
- K1 = 0.01
- K2 = 0.03
- L = 1 # depth of image (255 in case the image has a differnt scale)
- C1 = (K1*L)**2
- C2 = (K2*L)**2
- mu1 = tf.nn.conv2d(img1, window, strides=[1,1,1,1], padding='VALID')
- mu2 = tf.nn.conv2d(img2, window, strides=[1,1,1,1], padding='VALID')
- mu1_sq = mu1*mu1
- mu2_sq = mu2*mu2
- mu1_mu2 = mu1*mu2
- sigma1_sq = tf.nn.conv2d(img1*img1, window, strides=[1,1,1,1], padding='VALID') - mu1_sq
- sigma2_sq = tf.nn.conv2d(img2*img2, window, strides=[1,1,1,1], padding='VALID') - mu2_sq
- sigma12 = tf.nn.conv2d(img1*img2, window, strides=[1,1,1,1], padding='VALID') - mu1_mu2
- if cs_map:
- value = (((2*mu1_mu2 + C1)*(2*sigma12 + C2))/(EPS + (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)),
- (2.0*sigma12 + C2)/(EPS + sigma1_sq + sigma2_sq + C2))
- else:
- value = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/(EPS + (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
- if mean_metric:
- value = tf.reduce_mean(value)
- return value
- def ms_ssim(img1, img2, mean_metric=True, level=None, max_level=4, size=11, sigma=1.5):
- if type(size)==int: size=(size,size)
- if level is None:
- img_shape = np.int32(img1.shape.as_list()[1:3])
- size = np.int32(size)
- size_log2 = np.log2(size)
- levels = np.int32(np.log2(img_shape) - size_log2)+1 # find levels for each
- level = min(levels[0], levels[1])
- if max_level: level = min(level, max_level)
- print('ms_ssim | levels:', levels, ', using:', level, ', smallest dims:', np.array(img_shape)//(2**(level-1)))
- # weight = tf.constant([0.0448, 0.2856, 0.3001, 0.2363, 0.1333], dtype=tf.float32)
- weight = tf.constant([1.0/level]*level, dtype=tf.float32)
- mssim = []
- mcs = []
- for l in range(level):
- ssim_map, cs_map = ssim(img1, img2, cs_map=True, mean_metric=False, size=size, sigma=sigma)
- mssim.append(tf.reduce_mean(ssim_map))
- mcs.append(tf.reduce_mean(cs_map))
- filtered_im1 = tf.nn.avg_pool(img1, [1,2,2,1], [1,2,2,1], padding='SAME')
- filtered_im2 = tf.nn.avg_pool(img2, [1,2,2,1], [1,2,2,1], padding='SAME')
- img1 = filtered_im1
- img2 = filtered_im2
- # list to tensor of dim D+1
- mssim = tf.stack(mssim, axis=0)
- mcs = tf.stack(mcs, axis=0)
- value = (tf.reduce_prod(mcs[0:level-1]**weight[0:level-1])*
- (mssim[level-1]**weight[level-1]))
- if mean_metric:
- value = tf.reduce_mean(value)
- return value
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