def test_summary_image(func_dir):
img = np.zeros(10 * 3 * 5 * 5, dtype=np.float32).reshape((10, 3, 5, 5))
summary.image(img, out=func_dir, epoch=10)
meta_filepath = os.path.join(func_dir,
summary.CHAINERUI_ASSETS_METAFILE_NAME)
assert os.path.exists(meta_filepath)
with open(meta_filepath, 'r') as f:
metas = json.load(f)
assert len(metas) == 1
assert 'timestamp' in metas[0]
assert 'epoch' in metas[0]
assert metas[0]['epoch'] == 10
assert 'images' in metas[0]
assert 'image' in metas[0]['images']
saved_filename = metas[0]['images']['image']
assert saved_filename.startswith('image_')
assert saved_filename.endswith('.png')
img2 = np.zeros(10 * 3 * 5 * 5, dtype=np.float32).reshape((10, 3, 5, 5))
summary.image(img2, 'test', out=func_dir, subdir='image', epoch=20)
with open(meta_filepath, 'r') as f:
metas2 = json.load(f)
assert len(metas2) == 2
assert 'timestamp' in metas2[1]
assert 'epoch' in metas2[1]
assert metas2[1]['epoch'] == 20
assert 'images' in metas2[1]
assert 'test' in metas2[1]['images']
saved_filename2 = metas2[1]['images']['test']
assert saved_filename != saved_filename2
assert saved_filename2.startswith(os.path.join('image', 'test_'))
assert saved_filename2.endswith('.png')
def test_summary_image_unavailable(func_dir):
mock_checker = MagicMock(return_value=False)
with patch('chainerui.report.image_report.check_available', mock_checker):
summary.image(np.zeros(10), out=func_dir)
assert not os.path.exists(
os.path.join(func_dir, summary.CHAINERUI_ASSETS_METAFILE_NAME))
def generate_images(trainer):
gen = trainer.updater.gen
xp = gen.xp
z = xp.asarray(gen.make_hidden(n_samples))
with chainer.using_config('train', False):
with chainer.using_config('enable_backprop', False):
x = gen(z)
summary.image(x, name=image_name_format.format(trainer), row=n_rows)
def make_image(trainer):
np.random.seed(seed)
n_images = rows * cols
xp = gen.xp
z = Variable(xp.asarray(gen.make_hidden(n_images)))
with chainer.using_config('train', False):
x = gen(z)
x = chainer.cuda.to_cpu(x.data)
summary.image(x, row=rows)
np.random.seed()
def test_image_error(self):
img = np.zeros(10)
with self.assertRaises(ValueError) as e:
summary.image(img, 'test', batched=False)
assert 'must be 2 or 3' in str(e.exception)
img = np.zeros(10).reshape(2, 5)
with self.assertRaises(ValueError) as e:
summary.image(img, 'test')
assert 'must be 3 or 4' in str(e.exception)
def test_image_hw(self):
observer = summary.chainerui_image_observer
img = np.zeros(5*10).reshape((5, 10))
img = chainer.Variable(img)
summary.image(img, 'test', batched=False)
assert len(observer.observation) == 1
key = summary.CHAINERUI_IMAGE_PREFIX+'/test'
assert key in observer.observation
assert np.allclose(observer.observation[key]['image'], img.data)
def test_image_chw(self):
observer = summary.chainerui_image_observer
img = np.zeros(3*5*5).reshape((3, 5, 5))
img = chainer.Variable(img)
summary.image(img, 'test', ch_axis=0, batched=False)
assert len(observer.observation) == 1
key = summary.CHAINERUI_IMAGE_PREFIX+'/test'
assert key in observer.observation
expected_img = np.zeros(5*5*3).reshape((5, 5, 3))
assert np.allclose(observer.observation[key]['image'], expected_img)
assert 'mode' not in observer.observation[key]
def test_image_hwc_hsv(self):
observer = summary.chainerui_image_observer
img = np.zeros(5*5*3).reshape((5, 5, 3))
img = chainer.Variable(img)
summary.image(img, 'test', batched=False, ch_axis=-1, mode='HSV')
assert len(observer.observation) == 1
key = summary.CHAINERUI_IMAGE_PREFIX+'/test'
assert key in observer.observation
assert np.allclose(observer.observation[key]['image'], img.data)
assert 'mode' in observer.observation[key]
assert observer.observation[key]['mode'] == 'hsv'
def test_summary_image_without_output_path(func_dir):
summary._chainerui_asset_observer.default_output_path = func_dir
meta_filepath = os.path.join(func_dir,
summary.CHAINERUI_ASSETS_METAFILE_NAME)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
img = np.zeros(10 * 3 * 5 * 5, dtype=np.float32).reshape((10, 3, 5, 5))
summary.image(img)
assert len(w) == 1
assert os.path.exists(meta_filepath)
summary.set_out(func_dir)
summary.image(img)
assert len(w) == 1
def test_image(self):
img = np.zeros(750).reshape((10, 5, 5, 3))
with self.assertRaises(IndexError) as e:
summary.image(img, 'test', ch_axis=-1)
assert 'out of range' in str(e.exception)
reporter = chainer.Reporter()
observer = summary.chainerui_image_observer
reporter.add_observer(summary.CHAINERUI_IMAGE_PREFIX, observer)
with reporter.scope(observer.observation):
summary.image(img, 'test', ch_axis=-1)
assert len(observer.observation) == 1
key = summary.CHAINERUI_IMAGE_PREFIX+'/test'
assert key in observer.observation
img2 = np.zeros(750).reshape((10, 5, 5, 3))
img2[0, 0, 0, 1] = 1
with reporter.scope(observer.observation):
summary.image(img2, 'test', ch_axis=-1)
assert len(observer.observation) == 1
assert key in observer.observation
assert np.allclose(observer.observation[key]['array'], img2)
assert 'row' not in observer.observation[key]
assert 'mode' not in observer.observation[key]
img3 = np.zeros(750).reshape((10, 5, 5, 3))
img3[0, 0, 0, 2] = 1
img3 = chainer.Variable(img3)
with reporter.scope(observer.observation):
summary.image(img3, row=5, ch_axis=-1, mode='hsv')
assert len(observer.observation) == 2
none_key = summary.CHAINERUI_IMAGE_PREFIX+'/0'
assert none_key in observer.observation
assert np.allclose(observer.observation[none_key]['array'], img3.data)
assert observer.observation[none_key]['row'] == 5
assert observer.observation[none_key]['mode'] == 'hsv'
img4 = np.zeros(750).reshape((10, 3, 5, 5))
img4[0, 0, 1, 0] = 1
with reporter.scope(observer.observation):
summary.image(img4, 'test')
assert len(observer.observation) == 2
expected_img4 = img4.transpose(0, 2, 3, 1)
assert np.allclose(observer.observation[key]['array'], expected_img4)
assert 'row' not in observer.observation[key]
assert 'mode' not in observer.observation[key]
def test_image_bhw_no_name(self):
observer = summary.chainerui_image_observer
# unstuck
img = np.zeros(10*5*5).reshape((10, 5, 5))
summary.image(img)
assert len(observer.observation) == 1
key = summary.CHAINERUI_IMAGE_PREFIX+'/0'
assert key in observer.observation
expected_img = np.zeros(10*5*5).reshape((50, 5))
assert np.allclose(observer.observation[key]['image'], expected_img)
# stuck
img2 = np.zeros(10*5*5).reshape((10, 5, 5))
img2[0, 0, 0] = 10
summary.image(img2, row=2)
assert len(observer.observation) == 1
expected_img2 = np.zeros(10*5*5).reshape((10, 25))
expected_img2[0, 0] = 10
assert np.allclose(observer.observation[key]['image'], expected_img2)
def test_image_bhwc(self):
observer = summary.chainerui_image_observer
# unstuck
img = np.zeros(10*5*5*3).reshape((10, 5, 5, 3))
summary.image(img, 'test', ch_axis=-1)
assert len(observer.observation) == 1
key = summary.CHAINERUI_IMAGE_PREFIX+'/test'
assert key in observer.observation
expected_img = np.zeros(10*3*5*5).reshape((50, 5, 3))
assert np.allclose(observer.observation[key]['image'], expected_img)
# stuck
img2 = np.zeros(10*5*5*3).reshape((10, 5, 5, 3))
img2[0, 0, 0, 0] = 10
summary.image(img2, 'test2', ch_axis=-1, row=2)
assert len(observer.observation) == 2
key2 = summary.CHAINERUI_IMAGE_PREFIX+'/test2'
assert key2 in observer.observation
expected_img2 = np.zeros(10*5*5*3).reshape((10, 25, 3))
expected_img2[0, 0, 0] = 10
assert np.allclose(observer.observation[key2]['image'], expected_img2)
def make_image(trainer):
np.random.seed(seed)
xp = enc.xp
w_in = 256
w_out = 256
in_ch = 12
out_ch = 3
in_all = np.zeros((n_images, in_ch, w_in, w_in)).astype("i")
gt_all = np.zeros((n_images, out_ch, w_out, w_out)).astype("f")
gen_all = np.zeros((n_images, out_ch, w_out, w_out)).astype("f")
for it in range(n_images):
batch = updater.get_iterator('test').next()
batchsize = len(batch)
x_in = xp.zeros((batchsize, in_ch, w_in, w_in)).astype("f")
t_out = xp.zeros((batchsize, out_ch, w_out, w_out)).astype("f")
for i in range(batchsize):
x_in[i,:] = xp.asarray(batch[i][0])
t_out[i,:] = xp.asarray(batch[i][1])
x_in = Variable(x_in)
z = enc(x_in)
x_out = dec(z)
in_all[it,:] = x_in.data.get()[0,:]
gt_all[it,:] = t_out.get()[0,:]
gen_all[it,:] = x_out.data.get()[0,:]
summary.image(gen_all, name='gen', row=rows)
x = np.ones((n_images, 3, w_in, w_in)).astype(np.uint8)*255
x[:,0,:,:] = 0
for i in range(12):
x[:,0,:,:] += np.uint8(15*i*in_all[:,i,:,:])
summary.image(x, name='in', row=rows, mode='HSV')
summary.image(gt_all, name='gt', row=rows)
