{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Processes an example of our cine MRI dataset.\n", "\n", "Several assumptions are made regarding the acquisition protocol.\n", "\n", "- Cartesian cine dataset.\n", "- GRAPPA acceleration expected.\n", "- Phase Partial Fourier allowed.\n", "\n", "This data is collected using cardiac triggering, which means that there are different numbers time points for each of the lines of k-space\n", "\n", "Takes in raw k-space data (from .dat file)\n", "Fills k-space using GRAPPA \n", "Completes partial Fourier recon\n", "Resizes so all images are the same shape (in x and y)\n", "\n", "Then we take this 'truth' data, and perform synthetic undersampling (gridded using a radial trajectory)\n", "To form multi-coil, complex 2D+t paired training data for deep de-aliasing \n", "\n", "In this tutorial the dat file has been read and the data stored as h5 array, but the code to read dat files is commented out" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-01-21 22:41:32.166912: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2025-01-21 22:41:32.282165: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", "2025-01-21 22:41:32.308835: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" ] } ], "source": [ "\n", "import dataclasses\n", "import functools\n", "import pathlib\n", "\n", "import h5py\n", "import numpy as np\n", "import tensorflow as tf\n", "import tensorflow_mri as tfmri\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.animation\n", "\n", "import operator\n", "\n", "# This is necessary if reading from .dat files\n", "from tensorflow_mri.python.io import twix_io" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# We need to read the raw data from the scanner (In siemens this is stored as a .dat file)\n", "\n", "In this tutorial I have run this on a .dat file and saved the output in a h5 file to ensure confidentiality" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#This cell contains functions that are only necessary if you are reading .dat files\n", "\n", "def _get_loop_counters_as_tuple(header, dimensions=None, ignore_segments=True):\n", " if ignore_segments:\n", " header.loop_counters.seg = 0\n", "\n", " if dimensions is not None:\n", " # If a list of dimensions was specified, get only the requested dimensions,\n", " # after checking that none of the ignored dimensions are non-zero.\n", " for name, value in dataclasses.asdict(header.loop_counters).items():\n", " if value != 0 and name not in dimensions:\n", " raise ValueError(\n", " f\"Unexpected non-zero value for loop counter `{name}`: \"\n", " f\"{header.loop_counters}\")\n", " return operator.attrgetter(*dimensions)(header.loop_counters)\n", "\n", " # Return all loop counters (reversed).\n", " return tuple(reversed(dataclasses.astuple(header.loop_counters)))\n", "\n", "def _get_kspace_shape(meas, dimensions=None, ignore_segments=True):\n", " first_header = None\n", " kspace_shape = _get_loop_counters_as_tuple(\n", " meas.scans[0].header, dimensions=dimensions,\n", " ignore_segments=ignore_segments)\n", "\n", " for scan in meas.scans[1:]:\n", " if scan.header.eval_info_mask.ACQEND:\n", " break\n", " if _should_skip_scan(scan.header):\n", " continue\n", " if first_header is None:\n", " first_header = scan.header\n", "\n", " kspace_shape = np.maximum(\n", " kspace_shape, _get_loop_counters_as_tuple(\n", " scan.header, dimensions=dimensions,\n", " ignore_segments=ignore_segments))\n", "\n", " kspace_shape += 1\n", " kspace_shape = tuple(kspace_shape)\n", " kspace_shape += (first_header.used_channels, first_header.samples_in_scan)\n", " return kspace_shape\n", "\n", "def _should_skip_scan(header):\n", " # Skip scans with these flags.\n", " skip_flags = ['SYNCDATA', 'RETRO_DUMMYSCAN', 'NOISEADJSCAN']\n", " return any(getattr(header.eval_info_mask, flag) for flag in skip_flags)\n", "\n", "def _dataclass_diff_summary(a, b):\n", " # Check that object is a dataclass instance.\n", " # https://docs.python.org/3/library/dataclasses.html#dataclasses.is_dataclass\n", " if not dataclasses.is_dataclass(a) or isinstance(a, type):\n", " raise TypeError(f\"Input must be a dataclass instance, but got: {a}\")\n", "\n", " if a.__class__ != b.__class__:\n", " raise TypeError(\n", " f\"Classes must have the same type: {a.__class__} != {b.__class__}\")\n", "\n", " # Find differences.\n", " # diff = {}\n", " summary = []\n", " for field in dataclasses.fields(a):\n", " value_a = getattr(a, field.name)\n", " value_b = getattr(b, field.name)\n", " if value_a != value_b:\n", " if dataclasses.is_dataclass(field.type):\n", " # Recurse.\n", " temp = _dataclass_diff_summary(value_a, value_b)\n", " # Indent.\n", " temp = '\\n'.join(map(lambda x: ' ' + x, temp.splitlines()))\n", " summary.append(f\"{field.name}:\\n{temp}\")\n", " else:\n", " summary.append(f\"{field.name}: {value_a} != {value_b}\")\n", "\n", " # Create string.\n", " return '\\n'.join(summary)\n", "\n", "def get_kspace_array_from_twix_measurement(meas,\n", " output_dimensions=None,\n", " ignore_segments=True,\n", " create_mask=False,\n", " keep_scan_fn=None):\n", "\n", " kspace_shape = _get_kspace_shape(meas, dimensions=output_dimensions,\n", " ignore_segments=ignore_segments)\n", " kspace = np.zeros(kspace_shape, dtype=np.complex64)\n", " if create_mask:\n", " mask = np.zeros(kspace_shape[:-2], dtype=np.bool_)\n", "\n", " # We use this to keep track of what indices we've already filled.\n", " accu_counters = {}\n", "\n", " # Scans with one of these flags will be ignored.\n", " for scan in meas.scans:\n", " if scan.header.eval_info_mask.ACQEND:\n", " break\n", " if _should_skip_scan(scan.header):\n", " continue\n", " if keep_scan_fn is not None and not keep_scan_fn(scan.header):\n", " continue\n", "\n", " counters = _get_loop_counters_as_tuple(\n", " scan.header, dimensions=output_dimensions,\n", " ignore_segments=ignore_segments)\n", "\n", " if counters in accu_counters:\n", " raise ValueError(\n", " f\"Duplicate index counters: {counters}. Do you need another dimension?\\n\\n\"\n", " f\"Current scan header:\\n{scan.header}\\n\\n\"\n", " f\"Previous scan header:\\n{accu_counters[counters]}\\n\\n\"\n", " f\"Diff (current, previous):\\n{_dataclass_diff_summary(scan.header, accu_counters[counters])}\")\n", " accu_counters[counters] = scan.header\n", "\n", " for channel_idx, channel in enumerate(scan.channels):\n", " kspace[counters + (channel_idx,)] = channel.data.numpy()\n", " if create_mask:\n", " mask[counters] = True\n", "\n", " if create_mask:\n", " return kspace, mask\n", " return kspace\n", "\n", "def read_raw_data_file(input_file):\n", " # Read TWIX file.\n", " contents = tf.io.read_file(str(input_file))\n", " twix = tfmri.io.parse_twix(contents)\n", "\n", " meas = twix.measurements[-1]\n", " meas_prot = meas.protocol['Meas']\n", "\n", " PARTIAL_FOURIER_FACTOR = {\n", " 0x01: 0.5,\n", " 0x02: 5.0 / 8.0,\n", " 0x04: 6.0 / 8.0,\n", " 0x08: 7.0 / 8.0,\n", " 0x10: 1.0\n", " }\n", " # The following are actually used in the computation.\n", " phase_pf = PARTIAL_FOURIER_FACTOR[meas_prot.MEAS.sKSpace.ucPhasePartialFourier.value]\n", "\n", " for scan in meas.scans:\n", " if _should_skip_scan(scan.header):\n", " continue\n", " kspace_centre_line = scan.header.kspace_centre_line_no\n", " break\n", "\n", " # Loop k-space data.\n", " OUTPUT_DIMENSIONS = ['slice', 'phase', 'line']\n", " kspace, kspace_mask = get_kspace_array_from_twix_measurement(\n", " meas, output_dimensions=OUTPUT_DIMENSIONS,\n", " ignore_segments=True, create_mask=True)\n", " # kspace: [slices, phases, lines, coils, columns]\n", " # kspace_mask: [slices, phases, lines]\n", "\n", " # Loop calibration data.\n", " def keep_scan_fn(header):\n", " flags = header.eval_info_mask\n", " return (flags.PATREFSCAN or flags.PATREFANDIMASCAN)\n", " calib, calib_mask = get_kspace_array_from_twix_measurement(\n", " meas, output_dimensions=OUTPUT_DIMENSIONS,\n", " ignore_segments=True, create_mask=True,\n", " keep_scan_fn=keep_scan_fn)\n", " # calib: [slices, phases, lines, coils, columns]\n", " # calib_mask: [slices, phases, lines] \n", "\n", "\n", " return phase_pf, kspace_centre_line, kspace, kspace_mask, calib, calib_mask" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The below cell is only needed if you are reading from a .dat file\n", "In this tutorial the .dat file has been read using these functions and the necessary outputs have been stored in a h5 file" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#This cell is only needed if reading from a .dat file\n", "\n", "# input_file =pathlib.Path('/workspaces/Tutorials/DataFile.dat')\n", "# input_file = pathlib.Path(input_file)\n", "# phase_pf, kspace_centre_line, kspace, kspace_mask, calib, calib_mask = read_raw_data_file(input_file) \n", "\n", "# # write these results to h5 for the purposes of the tutorial\n", "# f = h5py.File('/workspaces/Tutorials/exampleRawDataCine.hdf5', 'w')\n", "# f.create_dataset('phase_pf', data = phase_pf)\n", "# f.create_dataset('kspace_centre_line', data = kspace_centre_line)\n", "# f.create_dataset('kspace', data = kspace)\n", "# f.create_dataset('kspace_mask', data = kspace_mask)\n", "# f.create_dataset('calib', data = calib)\n", "# f.create_dataset('calib_mask', data = calib_mask)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Use this cell if reading from the pre-processed h5 file\n", "\n", "import gdown\n", "\n", "url = 'https://drive.google.com/uc?id=1GAM91x54VsKmepImKkza0Lh_kUjQ-oOj'\n", "output = '/tools/docs/tutorials/recon/exampleRawDataCine.hdf5.zip'\n", "gdown.download(url, output, quiet=False)\n", "\n", "import zipfile\n", "with zipfile.ZipFile(\"/tools/docs/tutorials/recon/exampleRawDataCine.hdf5.zip\",\"r\") as zip_ref:\n", " zip_ref.extractall(\"/tools/docs/tutorials/recon/\")\n", "\n", "input_file =pathlib.Path('/tools/docs/tutorials/recon/exampleRawDataCine.hdf5')\n", "\n", "input_file = pathlib.Path(input_file)\n", "f = h5py.File(input_file, 'r')\n", "list(f.keys())\n", "\n", "phase_pf = f['phase_pf'][()] # This is the parial fourier factor in the phase direction\n", "kspace_centre_line = f['kspace_centre_line'][()] # This is the line index which represents the centre of kspace\n", "kspace = f['kspace'] # This is the kspace data fourier factor in the phase direction in the format # [slices, cardiac_phases, y, coils, x]\n", "kspace_mask = f['kspace_mask'] # This is a 1's and 0's mask which determines which lines of k-space were acquired\n", "calib = f['calib'] # This is the calibration (ACS) lines from the GRAPPA acquisition\n", "calib_mask = f['calib_mask'] # This is a 1's and 0's mask which determines which lines of k-space the ACS lines represent\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1, 45, 190, 30, 544)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kspace.shape\n", "# [slices, cardiac_phases, y, coils, x]\n", "# (1, 45, 190, 30, 544)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cine data which we often use for training is from a \n", "Cartesian bSSFP sequence\n", "It has GRAPPA x2, with 24 ACS lines\n", "And partial fourier in the phase direction of ~75%\n", "\n", "The data is acquired in a breath-hold, and is segmented\n", "This means that the data is collected over a number of RR intervals, where a different set of lines of k=space are collected in each R-wave\n", "\n", "However variations in heart-rate mean that some R-waves are longer than others, and when combining the line data together for form multiple cardiac phases, there are lines which have more cardiac phases than others" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# This functions is necessary to deal with the varying number of cardiac_phases for each line of kspace\n", "\n", "def _fill_missing_lines_in_last_phases(kspace, mask, threshold):\n", " # The last few phases may have some empty lines. If the proportion of missing\n", " # lines is more than `threshold`, just drop the entire phase. Otherwise, fill\n", " # the missing lines with data from the previous phase.\n", " expected_lines = np.count_nonzero(mask[0, 0])\n", " expected_lines_mask = mask[0, 0]\n", " # Count number of acquired lines for each slice and phase.\n", " acquired_lines = np.count_nonzero(mask, axis=-1)\n", " # Find which phases are to be kept (1D boolean array, true if phase has\n", " # enough lines).\n", " phases_to_keep = np.all(\n", " acquired_lines >= expected_lines * (1.0 - threshold), axis=0)\n", " # Find the number of phases as the first False entry in the phases_to_keep\n", " # array.\n", " phases_to_discard_indices = np.nonzero(~phases_to_keep)[0]\n", " if len(phases_to_discard_indices) == 0:\n", " # There are no incomplete phases.\n", " first_discarded_phase = None\n", " num_phases = len(phases_to_keep)\n", " else:\n", " first_discarded_phase = phases_to_discard_indices[0]\n", " num_phases = first_discarded_phase\n", " kspace = kspace[:, :num_phases, ...]\n", " mask = mask[:, :num_phases, ...]\n", " # Now fill the missing lines with data from the previous phase.\n", " for slice_index, phase_index in np.ndindex(mask.shape[:2]):\n", " # Lines mask for this slice and phase.\n", " lines_mask = mask[slice_index, phase_index]\n", "\n", " # Fill missing lines with data from the previous phase.\n", " missing_lines_mask = np.logical_and(\n", " expected_lines_mask, np.logical_not(lines_mask))\n", " mask[slice_index, phase_index][missing_lines_mask] = True\n", " kspace[slice_index, phase_index][missing_lines_mask] = \\\n", " kspace[slice_index, phase_index - 1][missing_lines_mask]\n", "\n", " return kspace, mask\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "THRESHOLD_MISSING_LINES_KSPACE = 0.3\n", "THRESHOLD_MISSING_LINES_CALIB = 0.1\n", " \n", "# Fill in missing lines.\n", "kspace, kspace_mask = _fill_missing_lines_in_last_phases(\n", " kspace, kspace_mask, threshold=THRESHOLD_MISSING_LINES_KSPACE)\n", "\n", "calib, calib_mask = _fill_missing_lines_in_last_phases(\n", " calib, calib_mask, threshold=THRESHOLD_MISSING_LINES_CALIB)\n", "\n", "# Keep the lowest number of phases between `kspace` and `calib`.\n", "phases = np.minimum(kspace.shape[1], calib.shape[1]) \n", "kspace = kspace[:, :phases, ...]\n", "kspace_mask = kspace_mask[:, :phases, ...]\n", "calib = calib[:, :phases, ...]\n", "calib_mask = calib_mask[:, :phases, ...]\n", "max_line = kspace_mask.shape[-1]\n", "\n", "if phase_pf == 1.0:\n", " pe_lines = max_line\n", " assert kspace_centre_line == (max_line + 1) // 2\n", "else:\n", " pe_lines = kspace_centre_line * 2 # Assuming even number of lines.\n", " phase_pf = max_line / pe_lines\n", "\n", " phase_pf = float(phase_pf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The raw data is collected with both GRAPPA and PArtial Fourer acquistion. \n", "To use this data as our 'truth' we want to have fully sampled data.\n", "Therefore we need to perform both GRAPPA and partial fourier reconstruction to fill in the missing lines of k-space" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# This cell contains all of the functionality to convert the k-space (with PF and GRAPPA) to a fully sampled dataset \n", "\n", "def _reconstruct_slice(kspace, kspace_mask, calib, calib_mask, phase_pf=1.0):\n", "\n", " # Create masked kspace and calib.\n", " kspace_lines_mask = kspace_mask[0]\n", " masked_kspace = kspace[:, kspace_lines_mask]\n", " calib_lines_mask = calib_mask[0]\n", " masked_calib = calib[:, calib_lines_mask]\n", "\n", " # Transpose.\n", " masked_kspace = np.transpose(masked_kspace, (0, 2, 1, 3)) # [phases, coils, lines, columns]\n", " masked_calib = np.transpose(masked_calib, (0, 2, 1, 3)) # [phases, coils, lines, columns]\n", "\n", " # Finally, make 2D mask (lines, columns).\n", " mask = np.transpose(np.tile(kspace_mask[0, ...], (masked_kspace.shape[-1], 1)))\n", "\n", " # Although tfmri.recon.grappa supports batches, it is quite likely to run out\n", " # of memory. Therefore we perform GRAPPA one phase at a time.\n", " num_coils = masked_kspace.shape[-3]\n", " def _single_phase_grappa(inputs):\n", " single_phase_kspace, single_phase_calib = inputs\n", "\n", " #print('single_phase_kspace: ', single_phase_kspace.shape, ' , mask:', mask.shape, ' , single_phase_calib: ',single_phase_calib.shape)\n", " #print('single_phase_kspace: ', single_phase_kspace.dtype, ' , mask:', mask.dtype, ' , single_phase_calib: ',single_phase_calib.dtype)\n", " # \n", " # single_phase_kspace: (30, 117, 544) , mask: (190, 544) , single_phase_calib: (30, 44, 544)\n", " # single_phase_kspace: , mask: bool , single_phase_calib: \n", "\n", " return tfmri.recon.grappa(single_phase_kspace, mask, single_phase_calib,\n", " return_kspace=True)\n", " kspace = tf.map_fn(_single_phase_grappa, [masked_kspace, masked_calib],\n", " fn_output_signature=tf.TensorSpec(\n", " shape=tf.TensorShape([num_coils]).concatenate(mask.shape),\n", " dtype=tf.complex64))\n", "\n", " if phase_pf != 1.0:\n", " # kspace = tf.pad(kspace, [[0, 0], [0, 0], [0, 44], [0, 0]])\n", " def _single_phase_pf(inputs):\n", " return tfmri.recon.partial_fourier(\n", " inputs, [phase_pf, 1.0], method='homodyne',\n", " preserve_phase=True, return_kspace=True)\n", "\n", " _single_phase_pf(kspace)\n", " kspace = tf.map_fn(_single_phase_pf, kspace)\n", "\n", " return kspace" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we call the function that performs the GRAPPA and PF recon for our dataset" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-01-21 22:41:41.705654: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2025-01-21 22:41:42.621475: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 436 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:65:00.0, compute capability: 8.6\n", "2025-01-21 22:41:42.622945: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 21746 MB memory: -> device: 1, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:b3:00.0, compute capability: 8.6\n", "2025-01-21 22:41:52.943571: W tensorflow/core/common_runtime/bfc_allocator.cc:479] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1B (rounded to 256)requested by op SameWorkerRecvDone\n", "If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. \n", "Current allocation summary follows.\n", "Current allocation summary follows.\n", "2025-01-21 22:41:52.943644: I 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tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (4096): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", "2025-01-21 22:41:52.943686: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (8192): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", "2025-01-21 22:41:52.943690: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (16384): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", "2025-01-21 22:41:52.943694: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (32768): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", "2025-01-21 22:41:52.943698: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (65536): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", "2025-01-21 22:41:52.943702: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (131072): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", "2025-01-21 22:41:52.943707: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (262144): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", "2025-01-21 22:41:52.943711: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (524288): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", "2025-01-21 22:41:52.943715: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (1048576): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", "2025-01-21 22:41:52.943719: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (2097152): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", "2025-01-21 22:41:52.943723: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (4194304): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", "2025-01-21 22:41:52.943727: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (8388608): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", "2025-01-21 22:41:52.943731: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (16777216): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", "2025-01-21 22:41:52.943741: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (33554432): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", "2025-01-21 22:41:52.943745: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (67108864): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", "2025-01-21 22:41:52.943752: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (134217728): \tTotal Chunks: 1, Chunks in use: 1. 130.64MiB allocated for chunks. 130.64MiB in use in bin. 115.05MiB client-requested in use in bin.\n", "2025-01-21 22:41:52.943757: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Bin (268435456): \tTotal Chunks: 1, Chunks in use: 1. 305.92MiB allocated for chunks. 305.92MiB in use in bin. 305.92MiB client-requested in use in bin.\n", "2025-01-21 22:41:52.943762: I tensorflow/core/common_runtime/bfc_allocator.cc:1056] Bin for 256B was 256B, Chunk State: \n", "2025-01-21 22:41:52.943766: I tensorflow/core/common_runtime/bfc_allocator.cc:1069] Next region of size 457768960\n", "2025-01-21 22:41:52.943779: I tensorflow/core/common_runtime/bfc_allocator.cc:1089] InUse at 7f64d8000000 of size 320785920 next 1\n", "2025-01-21 22:41:52.943783: I tensorflow/core/common_runtime/bfc_allocator.cc:1089] InUse at 7f64eb1ece00 of size 1280 next 2\n", "2025-01-21 22:41:52.943787: I tensorflow/core/common_runtime/bfc_allocator.cc:1089] InUse at 7f64eb1ed300 of size 136981760 next 18446744073709551615\n", "2025-01-21 22:41:52.943791: I tensorflow/core/common_runtime/bfc_allocator.cc:1094] Summary of in-use Chunks by size: \n", "2025-01-21 22:41:52.943797: I tensorflow/core/common_runtime/bfc_allocator.cc:1097] 1 Chunks of size 1280 totalling 1.2KiB\n", "2025-01-21 22:41:52.943802: I tensorflow/core/common_runtime/bfc_allocator.cc:1097] 1 Chunks of size 136981760 totalling 130.64MiB\n", "2025-01-21 22:41:52.943806: I tensorflow/core/common_runtime/bfc_allocator.cc:1097] 1 Chunks of size 320785920 totalling 305.92MiB\n", "2025-01-21 22:41:52.943810: I tensorflow/core/common_runtime/bfc_allocator.cc:1101] Sum Total of in-use chunks: 436.56MiB\n", "2025-01-21 22:41:52.943814: I tensorflow/core/common_runtime/bfc_allocator.cc:1103] total_region_allocated_bytes_: 457768960 memory_limit_: 457768960 available bytes: 0 curr_region_allocation_bytes_: 915537920\n", "2025-01-21 22:41:52.943823: I tensorflow/core/common_runtime/bfc_allocator.cc:1109] Stats: \n", "Limit: 457768960\n", "InUse: 457768960\n", "MaxInUse: 457768960\n", "NumAllocs: 3\n", "MaxAllocSize: 320785920\n", "Reserved: 0\n", "PeakReserved: 0\n", "LargestFreeBlock: 0\n", "\n", "2025-01-21 22:41:52.943828: W tensorflow/core/common_runtime/bfc_allocator.cc:491] *************************************************************************************************xxx\n" ] }, { "ename": "ResourceExhaustedError", "evalue": "{{function_node __wrapped__Less_device_/job:localhost/replica:0/task:0/device:GPU:0}} SameWorkerRecvDone unable to allocate output tensor. Key: /job:localhost/replica:0/task:0/device:GPU:0;43649fa394554138;/job:localhost/replica:0/task:0/device:GPU:0;edge_4_Less;0:0\n\t [[{{node Less/_6}}]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.\n [Op:Less]", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mResourceExhaustedError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[9], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Reconstruct all slices and phases.\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m fully_sampled_kspace \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvectorize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunctools\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpartial\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_reconstruct_slice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mphase_pf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mphase_pf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43motypes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcomplex64\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m(phs, lin, cha, col),(phs, lin),(phs, lin, cha, col),(phs, lin)->(phs, cha, out_lin, col)\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mkspace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkspace_mask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalib\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalib_mask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mResulting fully-sampled fully_sampled_kspace.shape:\u001b[39m\u001b[38;5;124m'\u001b[39m, fully_sampled_kspace\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# and now save fully_sampled_kspace as fully sampled dataset which can be used for training\u001b[39;00m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# output_file = input_file.with_suffix('.h5')\u001b[39;00m\n", "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/numpy/lib/function_base.py:2328\u001b[0m, in \u001b[0;36mvectorize.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2325\u001b[0m vargs \u001b[38;5;241m=\u001b[39m [args[_i] \u001b[38;5;28;01mfor\u001b[39;00m _i \u001b[38;5;129;01min\u001b[39;00m inds]\n\u001b[1;32m 2326\u001b[0m vargs\u001b[38;5;241m.\u001b[39mextend([kwargs[_n] \u001b[38;5;28;01mfor\u001b[39;00m _n \u001b[38;5;129;01min\u001b[39;00m names])\n\u001b[0;32m-> 2328\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_vectorize_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/numpy/lib/function_base.py:2402\u001b[0m, in \u001b[0;36mvectorize._vectorize_call\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m 2400\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Vectorized call to `func` over positional `args`.\"\"\"\u001b[39;00m\n\u001b[1;32m 2401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 2402\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_vectorize_call_with_signature\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2403\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[1;32m 2404\u001b[0m res \u001b[38;5;241m=\u001b[39m func()\n", "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/numpy/lib/function_base.py:2442\u001b[0m, in \u001b[0;36mvectorize._vectorize_call_with_signature\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m 2439\u001b[0m nout \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(output_core_dims)\n\u001b[1;32m 2441\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m index \u001b[38;5;129;01min\u001b[39;00m np\u001b[38;5;241m.\u001b[39mndindex(\u001b[38;5;241m*\u001b[39mbroadcast_shape):\n\u001b[0;32m-> 2442\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m[\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2444\u001b[0m n_results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(results) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 2446\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nout \u001b[38;5;241m!=\u001b[39m n_results:\n", "Cell \u001b[0;32mIn[8], line 32\u001b[0m, in \u001b[0;36m_reconstruct_slice\u001b[0;34m(kspace, kspace_mask, calib, calib_mask, phase_pf)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;66;03m#print('single_phase_kspace: ', single_phase_kspace.shape, ' , mask:', mask.shape, ' , single_phase_calib: ',single_phase_calib.shape)\u001b[39;00m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;66;03m#print('single_phase_kspace: ', single_phase_kspace.dtype, ' , mask:', mask.dtype, ' , single_phase_calib: ',single_phase_calib.dtype)\u001b[39;00m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;66;03m# \u001b[39;00m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;66;03m# single_phase_kspace: (30, 117, 544) , mask: (190, 544) , single_phase_calib: (30, 44, 544)\u001b[39;00m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;66;03m# single_phase_kspace: , mask: bool , single_phase_calib: \u001b[39;00m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tfmri\u001b[38;5;241m.\u001b[39mrecon\u001b[38;5;241m.\u001b[39mgrappa(single_phase_kspace, mask, single_phase_calib,\n\u001b[1;32m 31\u001b[0m return_kspace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m---> 32\u001b[0m kspace \u001b[38;5;241m=\u001b[39m \u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_single_phase_grappa\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mmasked_kspace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmasked_calib\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 33\u001b[0m \u001b[43m \u001b[49m\u001b[43mfn_output_signature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTensorSpec\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 34\u001b[0m \u001b[43m \u001b[49m\u001b[43mshape\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTensorShape\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnum_coils\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 35\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcomplex64\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m phase_pf \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1.0\u001b[39m:\n\u001b[1;32m 38\u001b[0m \u001b[38;5;66;03m# kspace = tf.pad(kspace, [[0, 0], [0, 0], [0, 44], [0, 0]])\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_single_phase_pf\u001b[39m(inputs):\n", "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/deprecation.py:629\u001b[0m, in \u001b[0;36mdeprecated_arg_values..deprecated_wrapper..new_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 622\u001b[0m _PRINTED_WARNING[(func, arg_name)] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 623\u001b[0m logging\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m 624\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFrom \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m: calling \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m (from \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m) with \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m is deprecated and \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 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\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_ops.py:2761\u001b[0m, in \u001b[0;36mwhile_loop\u001b[0;34m(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name, maximum_iterations, return_same_structure)\u001b[0m\n\u001b[1;32m 2757\u001b[0m packed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;66;03m# whether the body result was packed into a 1-item tuple\u001b[39;00m\n\u001b[1;32m 2759\u001b[0m loop_var_structure \u001b[38;5;241m=\u001b[39m nest\u001b[38;5;241m.\u001b[39mmap_structure(type_spec\u001b[38;5;241m.\u001b[39mtype_spec_from_value,\n\u001b[1;32m 2760\u001b[0m \u001b[38;5;28mlist\u001b[39m(loop_vars))\n\u001b[0;32m-> 2761\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[43mcond\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mloop_vars\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 2762\u001b[0m loop_vars \u001b[38;5;241m=\u001b[39m body(\u001b[38;5;241m*\u001b[39mloop_vars)\n\u001b[1;32m 2763\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m try_to_pack \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(loop_vars, (\u001b[38;5;28mlist\u001b[39m, _basetuple)):\n", "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_ops.py:2752\u001b[0m, in \u001b[0;36mwhile_loop..\u001b[0;34m(i, lv)\u001b[0m\n\u001b[1;32m 2749\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2750\u001b[0m loop_vars \u001b[38;5;241m=\u001b[39m (counter, loop_vars)\n\u001b[1;32m 2751\u001b[0m cond \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m i, lv: ( \u001b[38;5;66;03m# pylint: disable=g-long-lambda\u001b[39;00m\n\u001b[0;32m-> 2752\u001b[0m math_ops\u001b[38;5;241m.\u001b[39mlogical_and(i \u001b[38;5;241m<\u001b[39m maximum_iterations, \u001b[43morig_cond\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mlv\u001b[49m\u001b[43m)\u001b[49m))\n\u001b[1;32m 2753\u001b[0m body \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m i, lv: (i \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, orig_body(\u001b[38;5;241m*\u001b[39mlv))\n\u001b[1;32m 2754\u001b[0m try_to_pack \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/map_fn.py:499\u001b[0m, in \u001b[0;36mmap_fn..\u001b[0;34m(i, _)\u001b[0m\n\u001b[1;32m 493\u001b[0m tas \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 494\u001b[0m ta\u001b[38;5;241m.\u001b[39mwrite(i, value) \u001b[38;5;28;01mfor\u001b[39;00m (ta, value) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(tas, result_value_batchable)\n\u001b[1;32m 495\u001b[0m ]\n\u001b[1;32m 496\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (i \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, tas)\n\u001b[1;32m 498\u001b[0m _, r_a \u001b[38;5;241m=\u001b[39m control_flow_ops\u001b[38;5;241m.\u001b[39mwhile_loop(\n\u001b[0;32m--> 499\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m i, _: \u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m<\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m,\n\u001b[1;32m 500\u001b[0m compute, (i, result_batchable_ta),\n\u001b[1;32m 501\u001b[0m parallel_iterations\u001b[38;5;241m=\u001b[39mparallel_iterations,\n\u001b[1;32m 502\u001b[0m back_prop\u001b[38;5;241m=\u001b[39mback_prop,\n\u001b[1;32m 503\u001b[0m swap_memory\u001b[38;5;241m=\u001b[39mswap_memory,\n\u001b[1;32m 504\u001b[0m maximum_iterations\u001b[38;5;241m=\u001b[39mn)\n\u001b[1;32m 505\u001b[0m result_batchable \u001b[38;5;241m=\u001b[39m [r\u001b[38;5;241m.\u001b[39mstack() \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m r_a]\n\u001b[1;32m 507\u001b[0m \u001b[38;5;66;03m# Update each output tensor w/ static shape info about the outer dimension.\u001b[39;00m\n", "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/math_ops.py:1875\u001b[0m, in \u001b[0;36m_promote_dtypes_decorator..wrapper\u001b[0;34m(x, y, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1873\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mwrapper\u001b[39m(x, y, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1874\u001b[0m x, y \u001b[38;5;241m=\u001b[39m maybe_promote_tensors(x, y)\n\u001b[0;32m-> 1875\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_math_ops.py:5113\u001b[0m, in \u001b[0;36mless\u001b[0;34m(x, y, name)\u001b[0m\n\u001b[1;32m 5111\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _result\n\u001b[1;32m 5112\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 5113\u001b[0m \u001b[43m_ops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_from_not_ok_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5114\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _core\u001b[38;5;241m.\u001b[39m_FallbackException:\n\u001b[1;32m 5115\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py:7209\u001b[0m, in \u001b[0;36mraise_from_not_ok_status\u001b[0;34m(e, name)\u001b[0m\n\u001b[1;32m 7207\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mraise_from_not_ok_status\u001b[39m(e, name):\n\u001b[1;32m 7208\u001b[0m e\u001b[38;5;241m.\u001b[39mmessage \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m name: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m name \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 7209\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_status_to_exception(e) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n", "\u001b[0;31mResourceExhaustedError\u001b[0m: {{function_node __wrapped__Less_device_/job:localhost/replica:0/task:0/device:GPU:0}} SameWorkerRecvDone unable to allocate output tensor. Key: /job:localhost/replica:0/task:0/device:GPU:0;43649fa394554138;/job:localhost/replica:0/task:0/device:GPU:0;edge_4_Less;0:0\n\t [[{{node Less/_6}}]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.\n [Op:Less]" ] } ], "source": [ "# Reconstruct all slices and phases.\n", "fully_sampled_kspace = np.vectorize(functools.partial(_reconstruct_slice,\n", " phase_pf=phase_pf),\n", " otypes=['complex64'],\n", " signature='(phs, lin, cha, col),(phs, lin),(phs, lin, cha, col),(phs, lin)->(phs, cha, out_lin, col)')(\n", " kspace, kspace_mask, calib, calib_mask)\n", "\n", "print('Resulting fully-sampled fully_sampled_kspace.shape:', fully_sampled_kspace.shape)\n", "\n", "# and now save fully_sampled_kspace as fully sampled dataset which can be used for training\n", "# output_file = input_file.with_suffix('.h5')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have our fully sampled k-space, however each subjust mau have a different size image/kspace \n", "So we reize (crop or pad) the data so that all trainig data sets are the same size" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(21, 30, 256, 256)\n" ] } ], "source": [ "# In training we often want to make all the data the same size\n", "\n", "new_base_resolution = 256\n", "image_shape = tf.constant([new_base_resolution, new_base_resolution], dtype=tf.int32)\n", "\n", "fully_sampled_kspace = tf.cast(np.squeeze(fully_sampled_kspace), dtype = tf.complex64)\n", " \n", "# Crop to fixed image size.\n", "ground_truth_image = tfmri.signal.ifft(fully_sampled_kspace, axes=[-2, -1], shift=True)\n", "ground_truth_image = tfmri.resize_with_crop_or_pad(ground_truth_image, image_shape)\n", "\n", "# This is the FINAL 'truth; image\n", "# complex multicoil image data\n", "print(ground_truth_image.shape)\n", "#(21, 30, 256, 256)\n", "\n", "# Get number of phases in this image.\n", "num_phases = ground_truth_image.shape[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# And lets visualise\n", "plt.rcParams[\"animation.html\"] = \"jshtml\"\n", "plt.rcParams['figure.dpi'] = 150 \n", "plt.ioff()\n", "fig, ax = plt.subplots()\n", "\n", "cCoil = 0\n", "t= np.linspace(0,num_phases)\n", "def animate(t):\n", " plt.imshow(np.squeeze(tf.math.abs(ground_truth_image[t,cCoil,:,:])), cmap = 'gray')\n", " plt.title('Ground truth images (shown for one coil only)')\n", "\n", "matplotlib.animation.FuncAnimation(fig, animate, frames=num_phases)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point we have the ground truth k-space and image data\n", "\n", "However, often we want to create paired undersampled data when performing ML.\n", "\n", "In this example we will create radially undersampled cine data (multi-coil, complex)\n", "Here we use the parameters from the cmplex_multicoilUnet tutorial (radial)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(30, 21, 256, 256)\n", "(21, 6656, 2)\n" ] } ], "source": [ "# There is more informtion about calculating trajectories and gridding in the nufftTutorial\n", "radial_views = 13 \n", " \n", "# Compute trajectory.\n", "trajectory = tfmri.sampling.radial_trajectory(base_resolution=new_base_resolution,\n", " views=radial_views,\n", " phases=phases,\n", " ordering='sorted_half')\n", "# Compute density.\n", "density = tfmri.sampling.estimate_radial_density(trajectory)\n", "\n", "# Flatten trajectory and density.\n", "trajectory = tfmri.sampling.flatten_trajectory(trajectory)\n", "density = tfmri.sampling.flatten_density(density)\n", "\n", "Final_image = tf.einsum('...tchw->...cthw', ground_truth_image) #check dims\n", "\n", "print(Final_image.shape)\n", "print(trajectory.shape)\n", "#(30, 21, 256, 256)\n", "#(21, 6656, 2)\n", "\n", "# Final_image should be [time, coil, x, y]] \n", "# traj should # [time, nPtsPerRadial*nRadials, 2]]\n", "\n", "radial_kspace = tfmri.signal.nufft(Final_image, trajectory,\n", " transform_type='type_2',\n", " fft_direction='forward')\n", "radial_kspace /= tf.math.sqrt(\n", " tf.cast(tf.math.reduce_prod(image_shape), tf.complex64))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have created out undersampled multi-coil complex radial k-space data, we need to grid it to get the paired images for training" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "multicoil_time_recon.shape: (30, 21, 256, 256)\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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