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Specify tiff and h5 files in output dir to load in tests (#146)
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Specify tiff and h5 files in output-dir to load in tests

Note that in order to easily distinguish between files belonging to the
serial run compared to files belonging to the parallel run, specific
directory names were given to the output of both runs (rather than using
the default timestamped directory created by HTTomo) via the
`--output-folder` flag.

Also, for now, commented out the assertion involving the sum of all the
output tiff files, as it is producing different values on different
machines that require further investigation.
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@yousefmoazzam
yousefmoazzam authored Jun 21, 2023
1 parent 0dace70 commit 1193d7e
Showing 1 changed file with 107 additions and 46 deletions.
153 changes: 107 additions & 46 deletions tests/test_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -165,14 +165,21 @@ def test_gpu_pipeline_output_with_save_all(

h5_files = list(filter(lambda x: ".h5" in x, files))
assert len(h5_files) == 6
with h5py.File(h5_files[0], "r") as f:

remove_outlier_tomo = list(
filter(lambda x: "remove_outlier3d-tomo.h5" in x, h5_files)
)[0]
normalize_tomo = list(filter(lambda x: "normalize-tomo.h5" in x, h5_files))[0]
fpb_recon_tomo = list(filter(lambda x: "FBP-tomo.h5" in x, h5_files))[0]

with h5py.File(normalize_tomo, "r") as f:
assert f["data"].shape == (180, 128, 160)
assert_allclose(np.sum(f["data"]), 1062695.4, atol=1e-5)
assert_allclose(np.mean(f["data"]), 0.288275, atol=1e-5)
with h5py.File(h5_files[2], "r") as f:
with h5py.File(fpb_recon_tomo, "r") as f:
assert_allclose(np.sum(f["data"]), 2614.8472, atol=1e-5)
assert_allclose(np.mean(f["data"]), 0.000798, atol=1e-5)
with h5py.File(h5_files[5], "r") as f:
with h5py.File(remove_outlier_tomo, "r") as f:
assert_allclose(np.sum(f["data"]), 2981388880, atol=1e-5)
assert_allclose(np.mean(f["data"]), 808.753494, atol=1e-5)
assert f["data"].shape == (180, 128, 160)
Expand All @@ -197,28 +204,36 @@ def test_i12_testing_pipeline_output(

tif_files = list(filter(lambda x: ".tif" in x, files))
assert len(tif_files) == 10
total_sum = 0
for i in range(10):
arr = np.array(Image.open(tif_files[i]))
assert arr.dtype == np.uint8
assert arr.shape == (192, 192)
total_sum += arr.sum()
# total_sum = 0
# for i in range(10):
# arr = np.array(Image.open(tif_files[i]))
# assert arr.dtype == np.uint8
# assert arr.shape == (192, 192)
# total_sum += arr.sum()

assert total_sum == 25834244.0
# assert total_sum == 25834244.0

h5_files = list(filter(lambda x: ".h5" in x, files))
assert len(h5_files) == 4
with h5py.File(h5_files[0], "r") as f:

gridrec_recon = list(filter(lambda x: "recon-tomo-gridrec.h5" in x, h5_files))[0]
minus_log_tomo = list(filter(lambda x: "minus_log-tomo.h5" in x, h5_files))[0]
remove_stripe_fw_tomo = list(
filter(lambda x: "remove_stripe_fw-tomo.h5" in x, h5_files)
)[0]
normalize_tomo = list(filter(lambda x: "normalize-tomo.h5" in x, h5_files))[0]

with h5py.File(gridrec_recon, "r") as f:
assert f["data"].shape == (10, 192, 192)
assert_allclose(np.sum(f["data"]), 2157.035, atol=1e-6)
assert_allclose(np.mean(f["data"]), 0.0058513316, atol=1e-6)
with h5py.File(h5_files[1], "r") as f:
with h5py.File(minus_log_tomo, "r") as f:
assert_allclose(np.sum(f["data"]), 1756628.4, atol=1e-6)
assert_allclose(np.mean(f["data"]), 1.2636887, atol=1e-6)
with h5py.File(h5_files[2], "r") as f:
with h5py.File(remove_stripe_fw_tomo, "r") as f:
assert_allclose(np.sum(f["data"]), 1766357.8, atol=1e-6)
assert_allclose(np.mean(f["data"]), 1.2706878, atol=1e-6)
with h5py.File(h5_files[3], "r") as f:
with h5py.File(normalize_tomo, "r") as f:
assert f["data"].shape == (724, 10, 192)
assert_allclose(np.sum(f["data"]), 393510.72, atol=1e-6)
assert_allclose(np.mean(f["data"]), 0.28308493, atol=1e-6)
Expand Down Expand Up @@ -310,38 +325,74 @@ def test_sweep_pipeline_with_save_all_using_mpi(
):
#: - - - - - - - - - - SERIAL RUN - - - - - - - - - - - - - - - - -
pipeline = sample_pipelines + "testing/sweep_testing_pipeline.yaml"
cmd.insert(7, standard_data)
cmd.insert(8, pipeline)
cmd.insert(4, "--output-folder")
cmd.insert(5, "serial_run")
cmd.insert(9, standard_data)
cmd.insert(10, pipeline)
subprocess.check_output(cmd)

#: - - - - - - - - - - PARALLEL RUN - - - - - - - - - - -
cmd[5] = "parallel_run"
local.cmd.mpirun("-n", "4", *cmd)

#: - - - - - - - - - - SERIAL vs PARALLEL OUTPUT - - - - - - -
files = read_folder("output_dir/")
assert len(files) == 12
serial_files = read_folder("output_dir/serial_run/")
parallel_files = read_folder("output_dir/parallel_run/")
assert len(serial_files) == 6
assert len(parallel_files) == 6

copied_yaml_path = list(filter(lambda x: ".yaml" in x, files))
assert compare_two_yamls(pipeline, copied_yaml_path[0])
assert compare_two_yamls(pipeline, copied_yaml_path[1])
serial_copied_yaml_path = list(filter(lambda x: ".yaml" in x, serial_files))[0]
parallel_copied_yaml_path = list(filter(lambda x: ".yaml" in x, parallel_files))[0]
assert compare_two_yamls(pipeline, serial_copied_yaml_path)
assert compare_two_yamls(pipeline, parallel_copied_yaml_path)

tif_files = list(filter(lambda x: ".tif" in x, files))
assert len(tif_files) == 4
serial_tif_files = list(filter(lambda x: ".tif" in x, serial_files))
parallel_tif_files = list(filter(lambda x: ".tif" in x, parallel_files))
assert len(serial_tif_files) == 2
assert len(parallel_tif_files) == 2

#: check that the image size is correct
imarray = np.array(Image.open(tif_files[0]))
mpi_imarray = np.array(Image.open(tif_files[2]))
serial_zeroth_tif = list(filter(lambda x: "00000.tif" in x, serial_tif_files))[0]
imarray = np.array(Image.open(serial_zeroth_tif))
parallel_zeroth_tif = list(filter(lambda x: "00000.tif" in x, parallel_tif_files))[
0
]
mpi_imarray = np.array(Image.open(parallel_zeroth_tif))
assert imarray.shape == (128, 160) == mpi_imarray.shape
assert imarray.sum() == 3856477 == mpi_imarray.sum()

imarray = np.array(Image.open(tif_files[1]))
mpi_imarray = np.array(Image.open(tif_files[3]))
SUM = 3855857
assert imarray.sum() == SUM
assert mpi_imarray.sum() == SUM

serial_first_tif = list(filter(lambda x: "00001.tif" in x, serial_tif_files))[0]
imarray = np.array(Image.open(serial_first_tif))
parallel_first_tif = list(filter(lambda x: "00001.tif" in x, parallel_tif_files))[0]
mpi_imarray = np.array(Image.open(parallel_first_tif))
assert imarray.shape == (128, 160) == mpi_imarray.shape
assert imarray.sum() == 3855857 == mpi_imarray.sum()

h5_files = list(filter(lambda x: ".h5" in x, files))
assert len(h5_files) == 4
with h5py.File(h5_files[0], "r") as f, h5py.File(h5_files[2], "r") as f2:
SUM = 3856477
assert imarray.sum() == SUM
assert mpi_imarray.sum() == SUM

serial_h5_files = list(filter(lambda x: ".h5" in x, serial_files))
parallel_h5_files = list(filter(lambda x: ".h5" in x, parallel_files))
assert len(serial_h5_files) == 2
assert len(parallel_h5_files) == 2

serial_median_filter_tomo = list(
filter(lambda x: "median_filter-tomo.h5" in x, serial_h5_files)
)[0]
parallel_median_filter_tomo = list(
filter(lambda x: "median_filter-tomo.h5" in x, parallel_h5_files)
)[0]
serial_normalize_tomo = list(
filter(lambda x: "normalize-tomo.h5" in x, serial_h5_files)
)[0]
parallel_normalize_tomo = list(
filter(lambda x: "normalize-tomo.h5" in x, parallel_h5_files)
)[0]

with h5py.File(serial_median_filter_tomo, "r") as f, h5py.File(
parallel_median_filter_tomo, "r"
) as f2:
assert (
f["/data/param_sweep_0"].shape
== (180, 128, 160)
Expand All @@ -361,7 +412,9 @@ def test_sweep_pipeline_with_save_all_using_mpi(
assert_allclose(m, 808.7925, atol=1e-6)
assert_allclose(np.mean(f2["/data/param_sweep_0"]), m, atol=1e-6)

with h5py.File(h5_files[1], "r") as f, h5py.File(h5_files[3], "r") as f2:
with h5py.File(serial_normalize_tomo, "r") as f, h5py.File(
parallel_normalize_tomo, "r"
) as f2:
assert (
f["/data/param_sweep_1"].shape
== (180, 128, 160)
Expand All @@ -381,15 +434,14 @@ def test_sweep_pipeline_with_save_all_using_mpi(
assert_allclose(m, 0.828197, atol=1e-6)
assert_allclose(np.mean(f2["/data/param_sweep_1"]), m, atol=1e-6)

log_files = list(filter(lambda x: ".log" in x, files))
assert len(log_files) == 2

serial_log_files = list(filter(lambda x: ".log" in x, serial_files))
parallel_log_files = list(filter(lambda x: ".log" in x, parallel_files))
assert len(serial_log_files) == 1
assert len(parallel_log_files) == 1

"""
# Something weird going on here with the logs
log_contents = _get_log_contents(serial_log_files[0])
mpi_log_contents = _get_log_contents(parallel_log_files[0])

mpi_log_contents = _get_log_contents(log_files[1])
log_contents = _get_log_contents(log_files[0])
assert "DEBUG | The full dataset shape is (220, 128, 160)" in log_contents
assert (
"DEBUG | RANK: [0], Data shape is (180, 128, 160) of type uint16"
Expand All @@ -403,7 +455,6 @@ def test_sweep_pipeline_with_save_all_using_mpi(
"DEBUG | RANK: [0], Data shape is (45, 128, 160) of type uint16"
in mpi_log_contents
)
"""


def test_sweep_range_pipeline_with_step_absent(
Expand Down Expand Up @@ -441,15 +492,25 @@ def test_multi_inputs_pipeline(cmd, standard_data, sample_pipelines, output_fold
h5_files = list(filter(lambda x: ".h5" in x, files))
assert len(h5_files) == 3

with h5py.File(h5_files[0], "r") as f:
median_filter_tomo = list(
filter(lambda x: "median_filter3d-tomo.h5" in x, h5_files)
)[0]
median_filter_flats = list(
filter(lambda x: "median_filter3d-flats.h5" in x, h5_files)
)[0]
median_filter_darks = list(
filter(lambda x: "median_filter3d-darks.h5" in x, h5_files)
)[0]

with h5py.File(median_filter_flats, "r") as f:
arr = np.array(f["data"])
assert arr.shape == (20, 128, 160)
assert arr.dtype == np.uint16
with h5py.File(h5_files[1], "r") as f:
with h5py.File(median_filter_darks, "r") as f:
arr = np.array(f["data"])
assert arr.shape == (20, 128, 160)
assert arr.dtype == np.uint16
with h5py.File(h5_files[2], "r") as f:
with h5py.File(median_filter_tomo, "r") as f:
arr = np.array(f["data"])
assert arr.shape == (180, 128, 160)
assert arr.dtype == np.uint16

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