diff options
Diffstat (limited to 'tensorflow/python/ops/image_ops_test.py')
| -rw-r--r-- | tensorflow/python/ops/image_ops_test.py | 62 |
1 files changed, 30 insertions, 32 deletions
diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py index 35fdee4fad..ff86df6346 100644 --- a/tensorflow/python/ops/image_ops_test.py +++ b/tensorflow/python/ops/image_ops_test.py @@ -602,20 +602,19 @@ class AdjustHueBenchmark(test.Benchmark): if cpu_count is not None: config.inter_op_parallelism_threads = 1 config.intra_op_parallelism_threads = cpu_count - with session.Session("", graph=ops.Graph(), config=config) as sess: - with ops.device(device): - inputs = variables.Variable( - random_ops.random_uniform(image_shape, dtype=dtypes.float32) * 255, - trainable=False, - dtype=dtypes.float32) - delta = constant_op.constant(0.1, dtype=dtypes.float32) - outputs = image_ops.adjust_hue(inputs, delta) - run_op = control_flow_ops.group(outputs) - sess.run(variables.global_variables_initializer()) - for i in xrange(warmup_rounds + benchmark_rounds): - if i == warmup_rounds: - start = time.time() - sess.run(run_op) + with self.benchmark_session(config=config, device=device) as sess: + inputs = variables.Variable( + random_ops.random_uniform(image_shape, dtype=dtypes.float32) * 255, + trainable=False, + dtype=dtypes.float32) + delta = constant_op.constant(0.1, dtype=dtypes.float32) + outputs = image_ops.adjust_hue(inputs, delta) + run_op = control_flow_ops.group(outputs) + sess.run(variables.global_variables_initializer()) + for i in xrange(warmup_rounds + benchmark_rounds): + if i == warmup_rounds: + start = time.time() + sess.run(run_op) end = time.time() step_time = (end - start) / benchmark_rounds tag = device + "_%s" % (cpu_count if cpu_count is not None else "_all") @@ -646,21 +645,20 @@ class AdjustSaturationBenchmark(test.Benchmark): if cpu_count is not None: config.inter_op_parallelism_threads = 1 config.intra_op_parallelism_threads = cpu_count - with session.Session("", graph=ops.Graph(), config=config) as sess: - with ops.device(device): - inputs = variables.Variable( - random_ops.random_uniform(image_shape, dtype=dtypes.float32) * 255, - trainable=False, - dtype=dtypes.float32) - delta = constant_op.constant(0.1, dtype=dtypes.float32) - outputs = image_ops.adjust_saturation(inputs, delta) - run_op = control_flow_ops.group(outputs) - sess.run(variables.global_variables_initializer()) - for _ in xrange(warmup_rounds): - sess.run(run_op) - start = time.time() - for _ in xrange(benchmark_rounds): - sess.run(run_op) + with self.benchmark_session(config=config, device=device) as sess: + inputs = variables.Variable( + random_ops.random_uniform(image_shape, dtype=dtypes.float32) * 255, + trainable=False, + dtype=dtypes.float32) + delta = constant_op.constant(0.1, dtype=dtypes.float32) + outputs = image_ops.adjust_saturation(inputs, delta) + run_op = control_flow_ops.group(outputs) + sess.run(variables.global_variables_initializer()) + for _ in xrange(warmup_rounds): + sess.run(run_op) + start = time.time() + for _ in xrange(benchmark_rounds): + sess.run(run_op) end = time.time() step_time = (end - start) / benchmark_rounds tag = device + "_%s" % (cpu_count if cpu_count is not None else "_all") @@ -699,7 +697,7 @@ class ResizeBilinearBenchmark(test.Benchmark): deps = [resize_op] benchmark_op = control_flow_ops.group(*deps) - with session.Session() as sess: + with self.benchmark_session() as sess: sess.run(variables.global_variables_initializer()) results = self.run_op_benchmark( sess, @@ -747,7 +745,7 @@ class ResizeBicubicBenchmark(test.Benchmark): deps = [resize_op] benchmark_op = control_flow_ops.group(*deps) - with session.Session() as sess: + with self.benchmark_session() as sess: sess.run(variables.global_variables_initializer()) results = self.run_op_benchmark( sess, @@ -804,7 +802,7 @@ class ResizeAreaBenchmark(test.Benchmark): deps = [resize_op] benchmark_op = control_flow_ops.group(*deps) - with session.Session() as sess: + with self.benchmark_session() as sess: sess.run(variables.global_variables_initializer()) results = self.run_op_benchmark( sess, |