minor bugs (3edbbc64) · Commits · disMultiABM / stemdl

stemdl/inputs.py

+3 −3

Original line number	Diff line number	Diff line
		@@ -13,9 +13,9 @@ from tensorflow.python.ops import data_flow_ops
		import horovod.tensorflow as hvd
		import lmdb
		import time
		from nvidia.dali.pipeline import Pipeline
		import nvidia.dali.ops as dali_ops
		import nvidia.dali.plugin.tf as dali_tf
		#from nvidia.dali.pipeline import Pipeline
		#import nvidia.dali.ops as dali_ops
		#import nvidia.dali.plugin.tf as dali_tf

		tf.logging.set_verbosity(tf.logging.ERROR)

stemdl/io_utils.py

+12 −12

Original line number	Diff line number	Diff line
		@@ -5,14 +5,14 @@ email: laanaitn@ornl.gov
		"""
		from collections import OrderedDict
		import json
		import horovod.tensorflow as hvd

		# JSON utility functions

		# import horovod.tensorflow as hvd

		# def print(self, args, *kwargs):
		# if hvd.rank() == 0 :
		# print(args, *kwargs)
		def print_rank(self, args, *kwargs):
		if hvd.rank() == 0 :
		print_rank(args, *kwargs)

		def write_json_network_config(file, layer_keys, layer_params):
		"""
		@@ -26,7 +26,7 @@ def write_json_network_config(file, layer_keys, layer_params):
		network_config = OrderedDict(zip(layer_keys, layer_params))
		with open(file, mode='w') as f:
		json.dump(network_config, f, indent=4)
		print('Wrote %d NN layers to %s' % (len(network_config.keys()), file))
		print_rank('Wrote %d NN layers to %s' % (len(network_config.keys()), file))


		def load_json_network_config(file):
		@@ -46,7 +46,7 @@ def load_json_network_config(file):
		output = json.load(f, object_hook=_as_ordered_dict, object_pairs_hook=_as_ordered_dict)
		network_config = OrderedDict(output)

		print('Read %d NN layers from %s' % (len(network_config.keys()), file))
		print_rank('Read %d NN layers from %s' % (len(network_config.keys()), file))
		return network_config


		@@ -60,7 +60,7 @@ def write_json_hyper_params(file, hyper_params):

		with open(file, mode='w') as f:
		json.dump(hyper_params, f, indent=4)
		print('Wrote %d hyperparameters to %s' % (len(hyper_params.keys()), file))
		print_rank('Wrote %d hyperparameters to %s' % (len(hyper_params.keys()), file))


		def load_json_hyper_params(file):
		@@ -72,7 +72,7 @@ def load_json_hyper_params(file):
		with open(file, mode='r') as f:
		hyper_params = json.load(f)

		print('Read %d hyperparameters from %s' % (len(hyper_params.keys()), file))
		print_rank('Read %d hyperparameters from %s' % (len(hyper_params.keys()), file))
		return hyper_params


		@@ -81,7 +81,7 @@ def load_flags_from_simple_json(file_path, flags, verbose=False):
		for parm_name in image_parms.keys():
		val = image_parms[parm_name]
		if verbose:
		print('\t{}: {}'.format(parm_name, val))
		print_rank('\t{}: {}'.format(parm_name, val))
		if isinstance(val, bool):
		dtype = 'boolean'
		func = flags.DEFINE_boolean
		@@ -97,7 +97,7 @@ def load_flags_from_simple_json(file_path, flags, verbose=False):
		else:
		raise NotImplemented('{} : {} of type that we cannot handle now'.format(parm_name, val))
		if verbose:
		print('{} : {} saved as {}'.format(parm_name, val, dtype))
		print_rank('{} : {} saved as {}'.format(parm_name, val, dtype))
		func(parm_name, val, """""")


		@@ -114,7 +114,7 @@ def load_flags_from_json(file_path, flags, verbose=False):
		image_parms = load_json_hyper_params(file_path)
		for parm_name, parm_values in list(image_parms.items()):
		if verbose:
		print('\t{}: {}'.format(parm_name, parm_values))
		print_rank('\t{}: {}'.format(parm_name, parm_values))
		if parm_values['type'] == 'bool':
		func = flags.DEFINE_boolean
		elif parm_values['type'] == 'int':
		@@ -126,6 +126,6 @@ def load_flags_from_json(file_path, flags, verbose=False):
		else:
		raise NotImplemented('Cannot handle type: {} for parameter: {}'.format(parm_values['type'], parm_name))
		if verbose:
		print('{} : {} saved as {} with description: {}'.format(parm_name, parm_values['value'],
		print_rank('{} : {} saved as {} with description: {}'.format(parm_name, parm_values['value'],
		parm_values['type'], parm_values['desc']))
		func(parm_name, parm_values['value'], parm_values['desc'])

stemdl/losses.py

+9 −1

Original line number	Diff line number	Diff line
		@@ -191,7 +191,7 @@ def calculate_loss_regressor(net_output, labels, params, hyper_params, weight=No
		global_step = 1
		loss_params = hyper_params['loss_function']
		assert loss_params['type'] == 'Huber' or loss_params['type'] == 'MSE' \
		or loss_params['type'] == 'LOG' or loss_params['type'] == 'MSE_PAIR' or loss_params['type'] == 'ABS_DIFF' or loss_params['type'] == 'ABS_DIFF_SCALED', "Type of regression loss function must be 'Huber' or 'MSE'"
		or loss_params['type'] == 'LOG' or loss_params['type'] == 'MSE_PAIR' or loss_params['type'] == 'ABS_DIFF' or loss_params['type'] == 'ABS_DIFF_SCALED' or loss_params['type'] == 'rMSE', "Type of regression loss function must be 'Huber' or 'MSE'"
		if loss_params['type'] == 'Huber':
		# decay the residual cutoff exponentially
		decay_steps = int(params['NUM_EXAMPLES_PER_EPOCH'] / params['batch_size'] \
		@@ -220,6 +220,14 @@ def calculate_loss_regressor(net_output, labels, params, hyper_params, weight=No
		#reduction=tf.losses.Reduction.SUM)
		if loss_params['type'] == 'MSE_PAIR':
		cost = tf.losses.mean_pairwise_squared_error(labels, net_output, weights=weight)
		if loss_params['type'] == 'rMSE':
		labels = tf.cast(labels, tf.float32)
		l2_true = tf.sqrt(tf.reduce_sum(labels ** 2, axis=[1,2,3]))
		l2_output = tf.sqrt(tf.reduce_sum(net_output **2, axis = [1,2,3]))
		cost = tf.reduce_mean(tf.abs(l2_true - l2_output)/l2_true)
		#cost = tf.reduce_mean(tf.sqrt(tf.reduce_sum((labels - net_output)2, axis=[1,2,3]))/tf.sqrt(tf.reduce_sum(labels2, axis=[1,2,3])))
		cost *= 100
		tf.add_to_collection(tf.GraphKeys.LOSSES, cost)
		if loss_params['type'] == 'LOG':
		cost = tf.losses.log_loss(labels, weights=weight, predictions=net_output, reduction=tf.losses.Reduction.MEAN)
		return cost

stemdl/network.py

+2 −1

Original line number	Diff line number	Diff line
		@@ -2233,7 +2233,8 @@ class YNet(FCDenseNet, FCNet):
		self.network = dict([(key, itm) for key,itm in self.network.items()])

		def _batch_norm(self, input=None):
		out = tf.keras.layers.BatchNormalization(axis=1)(inputs=input, training= self.operation == 'train')
		#out = tf.keras.layers.BatchNormalization(axis=1)(inputs=input, training= self.operation == 'train')
		out = super(YNet, self)._batch_norm(input=input)
		return out

		def get_all_ops(self, subnet=None):

stemdl/runtime.py

+42 −10

Original line number	Diff line number	Diff line
		@@ -19,7 +19,7 @@ import tensorflow as tf
		from collections import OrderedDict
		import horovod.tensorflow as hvd
		from tensorflow.python.client import timeline
		from tensorflow.contrib.compiler import xla
		#from tensorflow.contrib.compiler import xla

		# stemdl
		from . import network
		@@ -152,8 +152,8 @@ def train(network_config, hyper_params, params):
		config.gpu_options.force_gpu_compatible = True
		config.intra_op_parallelism_threads = 6
		config.inter_op_parallelism_threads = max(1, cpu_count()//6)
		config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
		jit_scope = tf.contrib.compiler.jit.experimental_jit_scope
		#config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
		#jit_scope = tf.contrib.compiler.jit.experimental_jit_scope
		# JIT causes gcc errors on dgx-dl and is built without on Summit.
		sess = tf.Session(config=config)

		@@ -786,6 +786,24 @@ def validate(network_config, hyper_params, params, sess, dset, num_batches=10):
		elif hyper_params['network_type'] == 'hybrid':
		#TODO: implement evaluation call for hybrid network
		print('not implemented')
		elif hyper_params['network_type'] == 'YNet':
		loss_params = hyper_params['loss_function']
		model_output = tf.concat([n_net.model_output[subnet] for subnet in ['inverter', 'decoder_RE', 'decoder_IM']], axis=1)
		if loss_params['type'] == 'MSE_PAIR':
		errors = tf.losses.mean_pairwise_squared_error(tf.cast(labels, tf.float32), tf.cast(model_output, tf.float32))
		loss_label= loss_params['type']
		else:
		loss_label= 'ABS_DIFF'
		errors = tf.losses.absolute_difference(tf.cast(labels, tf.float32), tf.cast(model_output, tf.float32), reduction=tf.losses.Reduction.MEAN)
		errors = tf.expand_dims(errors,axis=0)
		error_averaging = hvd.allreduce(errors)
		if num_batches is not None:
		num_samples = num_batches
		else:
		num_samples = dset.num_samples
		#error = np.array([sess.run([IO_ops,error_averaging])[-1] for i in range(4)])
		error = np.array([sess.run([IO_ops,error_averaging])[-1] for i in range(num_samples//params['batch_size'])])
		print_rank('Validation Reconstruction Error %s: %3.3e' % (loss_label, error.mean()))
		elif hyper_params['network_type'] == 'inverter':
		loss_params = hyper_params['loss_function']
		if labels.shape.as_list()[1] > 1:
		@@ -793,6 +811,13 @@ def validate(network_config, hyper_params, params, sess, dset, num_batches=10):
		if loss_params['type'] == 'MSE_PAIR':
		errors = tf.losses.mean_pairwise_squared_error(tf.cast(labels, tf.float32), tf.cast(n_net.model_output, tf.float32))
		loss_label= loss_params['type']
		elif loss_params['type'] == 'rMSE':
		labels = tf.cast(labels, tf.float32)
		l2_true = tf.sqrt(tf.reduce_sum(labels ** 2, axis=[1,2,3]))
		l2_output = tf.sqrt(tf.reduce_sum(n_net.model_output **2, axis = [1,2,3]))
		errors = tf.reduce_mean(tf.abs(l2_true - l2_output)/l2_true)
		errors *= 100
		loss_label= loss_params['type']
		else:
		loss_label= 'ABS_DIFF'
		errors = tf.losses.absolute_difference(tf.cast(labels, tf.float32), tf.cast(n_net.model_output, tf.float32), reduction=tf.losses.Reduction.MEAN)
		@@ -803,7 +828,7 @@ def validate(network_config, hyper_params, params, sess, dset, num_batches=10):
		num_samples = num_batches
		else:
		num_samples = dset.num_samples
		errors = np.array([sess.run([IO_ops,error_averaging])[-1] for i in range(num_samples)])
		errors = np.array([sess.run([IO_ops,error_averaging])[-1] for i in range(num_samples//params['batch_size'])])
		# errors = np.array([sess.run([IO_ops,errors])[-1] for i in range(dset.num_samples)])
		# errors = tf.reduce_mean(errors)
		# avg_errors = hvd.allreduce(tf.expand_dims(errors, axis=0))
		@@ -917,6 +942,12 @@ def validate_ckpt(network_config, hyper_params, params, num_batches=None,
		ckpt_paths = [ckpt_paths[-1]]
		model_steps = [model_steps[-1]]

		if params['output']:
		output_dir = os.path.join(os.getcwd(), 'outputs_%s' % params['checkpt_dir'].split('/')[-1])
		if not os.path.exists(output_dir):
		tf.gfile.MakeDirs(output_dir)


		# Validate Models
		for ckpt, last_step in zip(ckpt_paths, model_steps):
		#
		@@ -950,7 +981,6 @@ def validate_ckpt(network_config, hyper_params, params, num_batches=None,
		if params['output']:
		output = tf.cast(n_net.model_output, tf.float32)
		print('output shape',output.get_shape().as_list())
		output_dir = os.path.join(os.getcwd(),'outputs')
		if num_batches is not None:
		num_samples = num_batches
		else:
		@@ -977,11 +1007,10 @@ def validate_ckpt(network_config, hyper_params, params, num_batches=None,
		print_rank('Validation Reconstruction Error %s: %3.3e' % (loss_label, error.mean()))
		elif hyper_params['network_type'] == 'YNet':
		loss_params = hyper_params['loss_function']
		model_output = tf.stack([n_net.model_output[subnet] for subnet in ['inverter', 'decoder_RE', 'decoder_IM']], axis=0)
		model_output = tf.concat([n_net.model_output[subnet] for subnet in ['inverter', 'decoder_RE', 'decoder_IM']], axis=1)
		if params['output']:
		output = tf.cast(model_output, tf.float32)
		print('output shape',output.get_shape().as_list())
		output_dir = os.path.join(os.getcwd(),'outputs')
		if num_batches is not None:
		num_samples = num_batches
		else:
		@@ -998,14 +1027,17 @@ def validate_ckpt(network_config, hyper_params, params, num_batches=None,
		else:
		loss_label= 'ABS_DIFF'
		errors = tf.losses.absolute_difference(tf.cast(labels, tf.float32), tf.cast(model_output, tf.float32), reduction=tf.losses.Reduction.MEAN)
		errors = tf.expand_dims(errors,axis=0)
		error_averaging = hvd.allreduce(errors)
		#errors = tf.expand_dims(errors,axis=0)
		#error_averaging = hvd.allreduce(errors)
		error_averaging = errors
		if num_batches is not None:
		num_samples = num_batches
		else:
		num_samples = dset.num_samples
		#error = np.array([sess.run([IO_ops,error_averaging])[-1] for i in range(4)])
		error = np.array([sess.run([IO_ops,error_averaging])[-1] for i in range(num_samples)])
		print_rank('Validation Reconstruction Error %s: %3.3e' % (loss_label, error.mean()))
		print('Rank=%d, Validation Reconstruction Error %s: %3.3e' % (hvd.rank(),loss_label, error.mean()))
		#print_rank('Validation Reconstruction Error %s: %3.3e' % (loss_label, error.mean()))
		if sleep < 0:
		break
		else:

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