def testBatch(self):
# Build an arbitrary RGB image
np.random.seed(7)
batch_size = 5
shape = (batch_size, 2, 7, 3)
for nptype in self.float_types:
inp = GenerateNumpyRandomRGB(shape).astype(nptype)
# Convert to HSV and back, as a batch and individually
with self.test_session() as sess:
batch0 = array_ops.placeholder(nptype, shape=shape)
with self.test_scope():
batch1 = image_ops.rgb_to_hsv(batch0)
batch2 = image_ops.hsv_to_rgb(batch1)
split0 = array_ops.unstack(batch0)
with self.test_scope():
split1 = list(map(image_ops.rgb_to_hsv, split0))
split2 = list(map(image_ops.hsv_to_rgb, split1))
join1 = array_ops.stack(split1)
join2 = array_ops.stack(split2)
batch1, batch2, join1, join2 = sess.run([batch1, batch2, join1, join2],
{batch0: inp})
# Verify that processing batch elements together is the same as separate
self.assertAllClose(batch1, join1)
self.assertAllClose(batch2, join2)
self.assertAllCloseAccordingToType(
batch2, inp, bfloat16_atol=0.03, half_rtol=0.02)
def testBatch(self):
# Build an arbitrary RGB image
np.random.seed(7)
batch_size = 5
shape = (batch_size, 2, 7, 3)
for nptype in self.float_types:
inp = np.random.rand(*shape).astype(nptype)
# Convert to HSV and back, as a batch and individually
with self.test_session() as sess:
batch0 = array_ops.placeholder(nptype, shape=shape)
with self.test_scope():
batch1 = image_ops.rgb_to_hsv(batch0)
batch2 = image_ops.hsv_to_rgb(batch1)
split0 = array_ops.unstack(batch0)
with self.test_scope():
split1 = list(map(image_ops.rgb_to_hsv, split0))
split2 = list(map(image_ops.hsv_to_rgb, split1))
join1 = array_ops.stack(split1)
join2 = array_ops.stack(split2)
batch1, batch2, join1, join2 = sess.run(
[batch1, batch2, join1, join2], {batch0: inp})
# Verify that processing batch elements together is the same as separate
self.assertAllClose(batch1, join1)
self.assertAllClose(batch2, join2)
self.assertAllClose(batch2, inp)
def testRGBToHSVRoundTrip(self):
data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
rgb_np = np.array(data, dtype=np.float32).reshape([2, 2, 3]) / 255.
for use_gpu in [True, False]:
with self.test_session(use_gpu=use_gpu):
hsv = image_ops.rgb_to_hsv(rgb_np)
rgb = image_ops.hsv_to_rgb(hsv)
rgb_tf = rgb.eval()
self.assertAllClose(rgb_tf, rgb_np)
def testRGBToHSVRoundTrip(self):
data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
rgb_np = np.array(data, dtype=np.float32).reshape([2, 2, 3]) / 255.
for use_gpu in [True, False]:
with self.test_session(use_gpu=use_gpu):
hsv = image_ops.rgb_to_hsv(rgb_np)
rgb = image_ops.hsv_to_rgb(hsv)
rgb_tf = rgb.eval()
self.assertAllClose(rgb_tf, rgb_np)
def testRGBToHSVRoundTrip(self):
data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
for nptype in self.float_types:
rgb_np = np.array(data, dtype=nptype).reshape([2, 2, 3]) / 255.
with self.test_session():
placeholder = array_ops.placeholder(nptype)
with self.test_scope():
hsv = image_ops.rgb_to_hsv(placeholder)
rgb = image_ops.hsv_to_rgb(hsv)
rgb_tf = rgb.eval(feed_dict={placeholder: rgb_np})
self.assertAllCloseAccordingToType(rgb_tf, rgb_np, bfloat16_atol=0.03)
def testRGBToHSVRoundTrip(self):
data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
for nptype in self.float_types:
rgb_np = np.array(data, dtype=nptype).reshape([2, 2, 3]) / 255.
with self.test_session():
placeholder = array_ops.placeholder(nptype)
with self.test_scope():
hsv = image_ops.rgb_to_hsv(placeholder)
rgb = image_ops.hsv_to_rgb(hsv)
rgb_tf = rgb.eval(feed_dict={placeholder: rgb_np})
self.assertAllClose(rgb_tf, rgb_np)
def testBatch(self):
# Build an arbitrary RGB image
np.random.seed(7)
batch_size = 5
shape = (batch_size, 2, 7, 3)
inp = np.random.rand(*shape).astype(np.float32)
# Convert to HSV and back, as a batch and individually
with self.test_session() as sess:
batch0 = constant_op.constant(inp)
batch1 = image_ops.rgb_to_hsv(batch0)
batch2 = image_ops.hsv_to_rgb(batch1)
split0 = array_ops.unpack(batch0)
split1 = map(image_ops.rgb_to_hsv, split0)
split2 = map(image_ops.hsv_to_rgb, split1)
join1 = array_ops.pack(split1)
join2 = array_ops.pack(split2)
batch1, batch2, join1, join2 = sess.run([batch1, batch2, join1, join2])
# Verify that processing batch elements together is the same as separate
self.assertAllClose(batch1, join1)
self.assertAllClose(batch2, join2)
self.assertAllClose(batch2, inp)
def testBatch(self):
# Build an arbitrary RGB image
np.random.seed(7)
batch_size = 5
shape = (batch_size, 2, 7, 3)
inp = np.random.rand(*shape).astype(np.float32)
# Convert to HSV and back, as a batch and individually
with self.test_session() as sess:
batch0 = constant_op.constant(inp)
batch1 = image_ops.rgb_to_hsv(batch0)
batch2 = image_ops.hsv_to_rgb(batch1)
split0 = array_ops.unpack(batch0)
split1 = list(map(image_ops.rgb_to_hsv, split0))
split2 = list(map(image_ops.hsv_to_rgb, split1))
join1 = array_ops.pack(split1)
join2 = array_ops.pack(split2)
batch1, batch2, join1, join2 = sess.run([batch1, batch2, join1, join2])
# Verify that processing batch elements together is the same as separate
self.assertAllClose(batch1, join1)
self.assertAllClose(batch2, join2)
self.assertAllClose(batch2, inp)