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zJK?9`ci?YRcbZGwX~S?!xIa7%o)0gB*T5U$ZSZdRK Date: Tue, 21 Nov 2023 10:36:20 +0100 Subject: [PATCH 02/39] First test for using torch --- lensless/hardware/mask.py | 69 ++++++++++++++++++++++++--------------- 1 file changed, 43 insertions(+), 26 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index f9597bf5..26cd0633 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -321,6 +321,11 @@ def simulate(self, obj, snr_db=20): return meas +class MicroLensArray(Mask): + def __init__( + self, resolution, distance_sensor, size=None, feature_size=None, psf_wavelength=[4.6e-7, 5.5e-7, 6.4e-7], **kwargs + ): + super().__init__(resolution, distance_sensor, size, feature_size, psf_wavelength, **kwargs) class PhaseContour(Mask): """ @@ -354,6 +359,7 @@ def __init__( self.refractive_index = refractive_index self.n_iter = n_iter self.design_wv = design_wv + super().__init__(**kwargs) @@ -361,33 +367,44 @@ def create_mask(self): """ Creating phase contour from edges of Perlin noise. """ + if not (torch_available and isinstance(self.mask, torch.Tensor)): + # Creating Perlin noise + proper_dim_1 = (self.resolution[0] // self.noise_period[0]) * self.noise_period[0] + proper_dim_2 = (self.resolution[1] // self.noise_period[1]) * self.noise_period[1] + noise = generate_perlin_noise_2d((proper_dim_1, proper_dim_2), self.noise_period) + + # Upscaling to correspond to sensor size + if np.any(self.resolution != noise.shape): + noise = resize(noise[:, :, np.newaxis], shape=tuple(self.resolution) + (1,)).squeeze() + + # Edge detection + binary = np.clip(np.round(np.interp(noise, (-1, 1), (0, 1))), a_min=0, a_max=1) + self.target_psf = cv.Canny(np.interp(binary, (-1, 1), (0, 255)).astype(np.uint8), 0, 255) + + # Computing mask and height map + phase_mask, height_map = phase_retrieval( + target_psf=self.target_psf, + wv=self.design_wv, + d1=self.feature_size, + dz=self.distance_sensor, + n=self.refractive_index, + n_iter=self.n_iter, + height_map=True, + ) + self.height_map = height_map + self.phase_pattern = phase_mask + self.mask = np.exp(1j * phase_mask) + + else: + #implement the same thing but using torch + proper_dim_1=(self.resolution[0]//self.noise_period[0])*self.noise_period[0] + proper_dim_2=(self.resolution[1]//self.noise_period[1])*self.noise_period[1] + noise=generate_perlin_noise_2d((proper_dim_1,proper_dim_2),self.noise_period) + + if torch.any(self.resolution != noise.shape): + noise=resize(noise[:,:,:,np.newaxis],shape=tuple(self.resolution)+(1,)).squeeze() + - # Creating Perlin noise - proper_dim_1 = (self.resolution[0] // self.noise_period[0]) * self.noise_period[0] - proper_dim_2 = (self.resolution[1] // self.noise_period[1]) * self.noise_period[1] - noise = generate_perlin_noise_2d((proper_dim_1, proper_dim_2), self.noise_period) - - # Upscaling to correspond to sensor size - if np.any(self.resolution != noise.shape): - noise = resize(noise[:, :, np.newaxis], shape=tuple(self.resolution) + (1,)).squeeze() - - # Edge detection - binary = np.clip(np.round(np.interp(noise, (-1, 1), (0, 1))), a_min=0, a_max=1) - self.target_psf = cv.Canny(np.interp(binary, (-1, 1), (0, 255)).astype(np.uint8), 0, 255) - - # Computing mask and height map - phase_mask, height_map = phase_retrieval( - target_psf=self.target_psf, - wv=self.design_wv, - d1=self.feature_size, - dz=self.distance_sensor, - n=self.refractive_index, - n_iter=self.n_iter, - height_map=True, - ) - self.height_map = height_map - self.phase_pattern = phase_mask - self.mask = np.exp(1j * phase_mask) def phase_retrieval(target_psf, wv, d1, dz, n=1.2, n_iter=10, height_map=False): From c2646a57cee3453015871c948fe0f022cb545e5e Mon Sep 17 00:00:00 2001 From: Ghita2002 Date: Tue, 21 Nov 2023 12:00:34 +0100 Subject: [PATCH 03/39] create_mask into torch --- lensless/hardware/mask.py | 24 +++++++++++++++++++++--- scripts/recon/train_unrolled.py | 1 + 2 files changed, 22 insertions(+), 3 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index 26cd0633..f1982328 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -399,12 +399,30 @@ def create_mask(self): #implement the same thing but using torch proper_dim_1=(self.resolution[0]//self.noise_period[0])*self.noise_period[0] proper_dim_2=(self.resolution[1]//self.noise_period[1])*self.noise_period[1] + #TODO to torch noise=generate_perlin_noise_2d((proper_dim_1,proper_dim_2),self.noise_period) - + n = noise.ndim + # n = noise.dim() + # end of todo if torch.any(self.resolution != noise.shape): - noise=resize(noise[:,:,:,np.newaxis],shape=tuple(self.resolution)+(1,)).squeeze() - + noise=resize(noise.unsqueeze(n-1),shape=tuple(self.resolution)+(1,)).squeeze() + + binary = torch.clip(torch.round(torch.nn.functional.grid_sample(noise, (-1, 1), (0, 1))), a_min=0, a_max=1) + self.target_psf = cv.Canny(torch.nn.functional.grid_sample(binary, (-1, 1), (0, 255)).astype(np.uint8), 0, 255) + # Computing mask and height map + phase_mask, height_map = phase_retrieval( + target_psf=self.target_psf, + wv=self.design_wv, + d1=self.feature_size, + dz=self.distance_sensor, + n=self.refractive_index, + n_iter=self.n_iter, + height_map=True, + ) + self.height_map = height_map + self.phase_pattern = phase_mask + self.mask = torch.exp(1j * phase_mask) def phase_retrieval(target_psf, wv, d1, dz, n=1.2, n_iter=10, height_map=False): diff --git a/scripts/recon/train_unrolled.py b/scripts/recon/train_unrolled.py index feef375a..dc53a505 100644 --- a/scripts/recon/train_unrolled.py +++ b/scripts/recon/train_unrolled.py @@ -615,3 +615,4 @@ def train_unrolled(config): if __name__ == "__main__": train_unrolled() + \ No newline at end of file From 331e32731068329d532e7cc3680e254afee08aab Mon Sep 17 00:00:00 2001 From: Ahmed Date: Tue, 21 Nov 2023 12:52:08 +0100 Subject: [PATCH 04/39] phase_retrieval in torch --- lensless/hardware/mask.py | 73 +++++++++++++++++++++++++++------------ 1 file changed, 50 insertions(+), 23 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index f1982328..12785ac7 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -390,6 +390,7 @@ def create_mask(self): n=self.refractive_index, n_iter=self.n_iter, height_map=True, + can_torch=True, ) self.height_map = height_map self.phase_pattern = phase_mask @@ -419,13 +420,14 @@ def create_mask(self): n=self.refractive_index, n_iter=self.n_iter, height_map=True, + can_torch=False, ) self.height_map = height_map self.phase_pattern = phase_mask self.mask = torch.exp(1j * phase_mask) -def phase_retrieval(target_psf, wv, d1, dz, n=1.2, n_iter=10, height_map=False): +def phase_retrieval(target_psf, wv, d1, dz, n=1.2, n_iter=10, height_map=False, can_torch=False): """ Iterative phase retrieval algorithm similar to `PhlatCam `_, using Fresnel propagation. @@ -445,29 +447,54 @@ def phase_retrieval(target_psf, wv, d1, dz, n=1.2, n_iter=10, height_map=False): n_iter: int Number of iterations. Default value is 10. """ - M_p = np.sqrt(target_psf) - - if hasattr(d1, "__len__"): - if d1[0] != d1[1]: - warnings.warn("Non-square pixel, first dimension taken as feature size.") - d1 = d1[0] - - for _ in range(n_iter): - # back propagate from sensor to mask - M_phi = fresnel_conv(M_p, wv, d1, -dz, dtype=np.float32)[0] - # constrain amplitude at mask to be unity, i.e. phase pattern - M_phi = np.exp(1j * np.angle(M_phi)) - # forward propagate from mask to sensor - M_p = fresnel_conv(M_phi, wv, d1, dz, dtype=np.float32)[0] - # constrain amplitude to be sqrt(PSF) - M_p = np.sqrt(target_psf) * np.exp(1j * np.angle(M_p)) - - phi = (np.angle(M_phi) + 2 * np.pi) % (2 * np.pi) - - if height_map: - return phi, wv * phi / (2 * np.pi * (n - 1)) + if can_torch: + M_p = torch.sqrt(target_psf) + + if hasattr(d1, "__len__"): + if d1[0] != d1[1]: + warnings.warn("Non-square pixel, first dimension taken as feature size.") + d1 = d1[0] + + for _ in range(n_iter): + # back propagate from sensor to mask + M_phi = fresnel_conv(M_p, wv, d1, -dz, dtype=torch.float32)[0] + # constrain amplitude at mask to be unity, i.e. phase pattern + M_phi = torch.exp(1j * torch.angle(M_phi)) + # forward propagate from mask to sensor + M_p = fresnel_conv(M_phi, wv, d1, dz, dtype=torch.float32)[0] + # constrain amplitude to be sqrt(PSF) + M_p = torch.sqrt(target_psf) * torch.exp(1j * torch.angle(M_p)) + + phi = (torch.angle(M_phi) + 2 * torch.pi) % (2 * torch.pi) + + if height_map: + return phi, wv * phi / (2 * torch.pi * (n - 1)) + else: + return phi else: - return phi + M_p = np.sqrt(target_psf) + + if hasattr(d1, "__len__"): + if d1[0] != d1[1]: + warnings.warn("Non-square pixel, first dimension taken as feature size.") + d1 = d1[0] + + for _ in range(n_iter): + # back propagate from sensor to mask + M_phi = fresnel_conv(M_p, wv, d1, -dz, dtype=np.float32)[0] + # constrain amplitude at mask to be unity, i.e. phase pattern + M_phi = np.exp(1j * np.angle(M_phi)) + # forward propagate from mask to sensor + M_p = fresnel_conv(M_phi, wv, d1, dz, dtype=np.float32)[0] + # constrain amplitude to be sqrt(PSF) + M_p = np.sqrt(target_psf) * np.exp(1j * np.angle(M_p)) + + phi = (np.angle(M_phi) + 2 * np.pi) % (2 * np.pi) + + if height_map: + return phi, wv * phi / (2 * np.pi * (n - 1)) + else: + return phi class FresnelZoneAperture(Mask): From 5e1242580822623477066139bce647a7d021525a Mon Sep 17 00:00:00 2001 From: Seif Hamed Date: Thu, 23 Nov 2023 16:05:22 +0100 Subject: [PATCH 05/39] added the height varying mask --- lensless/hardware/mask.py | 65 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index f9597bf5..416f71b0 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -470,3 +470,68 @@ def create_mask(self): radius_px = self.radius / self.feature_size[0] mask = 0.5 * (1 + np.cos(np.pi * (x**2 + y**2) / radius_px**2)) self.mask = np.round(mask) + + +class HeightVarying(Mask): + + def __init__( + self, + refractive_index = 1.2, + wavelength = 532e-9, + height_map = None, + height_range = (min, max), + seed = 0): + + """ + A class representing a height-varying mask for lensless imaging. + + Parameters + ---------- + refractive_index : float, optional + The refractive index of the material. Default is 1.2. + wavelength : float, optional + The wavelength of the light. Default is 532e-9. + height_map : ndarray or None, optional + An array representing the height map of the mask. If None, a random height map is generated. + height_range : tuple, optional + A tuple (min, max) specifying the range of heights when generating a random height map. + Default is (min, max), where min and max are placeholders for the actual values. + seed : int, optional + Seed for the random number generator when generating a random height map. Default is 0. + + Example + ------- + Creating an instance with a custom height map: + + >>> custom_height_map = np.array([0.1, 0.2, 0.3]) + >>> height_varying_instance = HeightVarying( + ... refractive_index=1.2, + ... wavelength=532e-9, + ... height_map=custom_height_map, + ... height_range=(0.0, 1.0), + ... seed=42 + ... ) + """ + + super().__init__() + self.refractive_index = refractive_index + self.wavelength = wavelength + + if self.height_map is not None: + self.height_map = height_map + else: + np.random.seed(self.seed) + self.height_map = np.random.uniform(self.height_range[0], self.height_range[1], n) + + self.height_range = height_range + self.seed = seed + + def get_phi(self, n): + phi = self.height_map * (2*np.pi*(n-1) / self.wavelength) + phi = phi % (2*np.pi) + return phi + + def create_mask(self, n): + phase_mask = self.get_phi(n) + mask = np.exp(1j * self.get_phi(n)) + return mask \ No newline at end of file From 356d6ba3258eadb18388bb5ff89163756849083b Mon Sep 17 00:00:00 2001 From: Ghita2002 Date: Sat, 25 Nov 2023 12:29:34 +0100 Subject: [PATCH 06/39] MultiLensArray initialization --- lensless/hardware/mask.py | 26 +++++++++++++++++++++++--- 1 file changed, 23 insertions(+), 3 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index 12785ac7..b0daa957 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -321,11 +321,31 @@ def simulate(self, obj, snr_db=20): return meas -class MicroLensArray(Mask): +class MultiLensArray(Mask): def __init__( - self, resolution, distance_sensor, size=None, feature_size=None, psf_wavelength=[4.6e-7, 5.5e-7, 6.4e-7], **kwargs + self, N = None, radius = None, loc = None, refractive_index = 1.2, seed = 0, min_height=1e-3 ): - super().__init__(resolution, distance_sensor, size, feature_size, psf_wavelength, **kwargs) + self.N = N + self.radius = radius + self.loc = loc + self.refractive_index = refractive_index + self.seed = seed + self.min_height = min_height + + + if self.radius is not None: + assert self.loc is not None + assert len(self.radius) == len(self.loc) + self.N = len(self.radius) + else: + assert self.N is not None + radius = np.random.uniform(self.min_height, self.distance_sensor, self.N) + result = np.column_stack((np.random.uniform(0,self.size[0],self.N),np.random.uniform(0,self.size[1],self.N))) + loc = np.array([tuple(row) for row in result]) + # call the does_circle_overlap method + + super().__init__() + class PhaseContour(Mask): """ From 515fd11ef2956075b63dca8c0500fe8c82b79607 Mon Sep 17 00:00:00 2001 From: ghita2002 Date: Sat, 25 Nov 2023 12:32:50 +0100 Subject: [PATCH 07/39] Adding comments to mask.py --- lensless/hardware/mask.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index b0daa957..e74ed4f6 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -322,9 +322,30 @@ def simulate(self, obj, snr_db=20): return meas class MultiLensArray(Mask): + """ + Multi-lens array mask. + """ def __init__( self, N = None, radius = None, loc = None, refractive_index = 1.2, seed = 0, min_height=1e-3 ): + """ + Multi-lens array mask constructor. + + Parameters + ---------- + N: int + Number of lenses + radius: array_like + Radius of the lenses (m) + loc: array_like of tuples + Location of the lenses (m) + refractive_index: float + Refractive index of the mask substrate. Default is 1.2. + seed: int + Seed for the random number generator. Default is 0. + min_height: float + Minimum height of the lenses (m). Default is 1e-3. + """ self.N = N self.radius = radius self.loc = loc @@ -339,6 +360,7 @@ def __init__( self.N = len(self.radius) else: assert self.N is not None + np.random.seed(self.seed) radius = np.random.uniform(self.min_height, self.distance_sensor, self.N) result = np.column_stack((np.random.uniform(0,self.size[0],self.N),np.random.uniform(0,self.size[1],self.N))) loc = np.array([tuple(row) for row in result]) From 0f2d9d12c9a31ca49ca0db92026a31ba226d5da9 Mon Sep 17 00:00:00 2001 From: Ahmed Date: Sat, 25 Nov 2023 14:18:12 +0100 Subject: [PATCH 08/39] first half of the MLA --- lensless/hardware/mask.py | 176 +++++++++++++++++++++++++++----------- 1 file changed, 127 insertions(+), 49 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index f9597bf5..da838715 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -321,6 +321,27 @@ def simulate(self, obj, snr_db=20): return meas +class MultiLensArray(Mask): + def create_mask(self): + height = self.create_height_map(self.radius, self.locs) + phi = (height * (self.refractive_index - 1) * 2 * np.pi / self.wavelength) % (2 * np.pi) + self.mask = np.exp(1j * phi) + + def create_height_map(self, radius, locs): + height = np.full((self.size[0], self.size[1]), self.min_height) + for x in height.shape[0]: + for y in height.shape[1]: + height[x, y] += self.lens_contribution(radius, locs, x, y) + return height + + def lens_contribution(self, radius, locs, x, y): + contribution = 0 + for idx, loc in enumerate(locs): + if (x-loc[0])**2 + (y-loc[1])**2 < radius[idx]**2: + contribution = np.sqrt(radius[idx]**2 - (x-loc[0])**2 - (y-loc[1])**2) + return contribution + return contribution + class PhaseContour(Mask): """ @@ -354,6 +375,7 @@ def __init__( self.refractive_index = refractive_index self.n_iter = n_iter self.design_wv = design_wv + super().__init__(**kwargs) @@ -361,36 +383,67 @@ def create_mask(self): """ Creating phase contour from edges of Perlin noise. """ + if not (torch_available and isinstance(self.mask, torch.Tensor)): + # Creating Perlin noise + proper_dim_1 = (self.resolution[0] // self.noise_period[0]) * self.noise_period[0] + proper_dim_2 = (self.resolution[1] // self.noise_period[1]) * self.noise_period[1] + noise = generate_perlin_noise_2d((proper_dim_1, proper_dim_2), self.noise_period) + + # Upscaling to correspond to sensor size + if np.any(self.resolution != noise.shape): + noise = resize(noise[:, :, np.newaxis], shape=tuple(self.resolution) + (1,)).squeeze() + + # Edge detection + binary = np.clip(np.round(np.interp(noise, (-1, 1), (0, 1))), a_min=0, a_max=1) + self.target_psf = cv.Canny(np.interp(binary, (-1, 1), (0, 255)).astype(np.uint8), 0, 255) + + # Computing mask and height map + phase_mask, height_map = phase_retrieval( + target_psf=self.target_psf, + wv=self.design_wv, + d1=self.feature_size, + dz=self.distance_sensor, + n=self.refractive_index, + n_iter=self.n_iter, + height_map=True, + can_torch=True, + ) + self.height_map = height_map + self.phase_pattern = phase_mask + self.mask = np.exp(1j * phase_mask) - # Creating Perlin noise - proper_dim_1 = (self.resolution[0] // self.noise_period[0]) * self.noise_period[0] - proper_dim_2 = (self.resolution[1] // self.noise_period[1]) * self.noise_period[1] - noise = generate_perlin_noise_2d((proper_dim_1, proper_dim_2), self.noise_period) - - # Upscaling to correspond to sensor size - if np.any(self.resolution != noise.shape): - noise = resize(noise[:, :, np.newaxis], shape=tuple(self.resolution) + (1,)).squeeze() - - # Edge detection - binary = np.clip(np.round(np.interp(noise, (-1, 1), (0, 1))), a_min=0, a_max=1) - self.target_psf = cv.Canny(np.interp(binary, (-1, 1), (0, 255)).astype(np.uint8), 0, 255) - - # Computing mask and height map - phase_mask, height_map = phase_retrieval( - target_psf=self.target_psf, - wv=self.design_wv, - d1=self.feature_size, - dz=self.distance_sensor, - n=self.refractive_index, - n_iter=self.n_iter, - height_map=True, - ) - self.height_map = height_map - self.phase_pattern = phase_mask - self.mask = np.exp(1j * phase_mask) + else: + #implement the same thing but using torch + proper_dim_1=(self.resolution[0]//self.noise_period[0])*self.noise_period[0] + proper_dim_2=(self.resolution[1]//self.noise_period[1])*self.noise_period[1] + #TODO to torch + noise=generate_perlin_noise_2d((proper_dim_1,proper_dim_2),self.noise_period) + n = noise.ndim + # n = noise.dim() + # end of todo + if torch.any(self.resolution != noise.shape): + noise=resize(noise.unsqueeze(n-1),shape=tuple(self.resolution)+(1,)).squeeze() + + binary = torch.clip(torch.round(torch.nn.functional.grid_sample(noise, (-1, 1), (0, 1))), a_min=0, a_max=1) + self.target_psf = cv.Canny(torch.nn.functional.grid_sample(binary, (-1, 1), (0, 255)).astype(np.uint8), 0, 255) + + # Computing mask and height map + phase_mask, height_map = phase_retrieval( + target_psf=self.target_psf, + wv=self.design_wv, + d1=self.feature_size, + dz=self.distance_sensor, + n=self.refractive_index, + n_iter=self.n_iter, + height_map=True, + can_torch=False, + ) + self.height_map = height_map + self.phase_pattern = phase_mask + self.mask = torch.exp(1j * phase_mask) -def phase_retrieval(target_psf, wv, d1, dz, n=1.2, n_iter=10, height_map=False): +def phase_retrieval(target_psf, wv, d1, dz, n=1.2, n_iter=10, height_map=False, can_torch=False): """ Iterative phase retrieval algorithm similar to `PhlatCam `_, using Fresnel propagation. @@ -410,29 +463,54 @@ def phase_retrieval(target_psf, wv, d1, dz, n=1.2, n_iter=10, height_map=False): n_iter: int Number of iterations. Default value is 10. """ - M_p = np.sqrt(target_psf) - - if hasattr(d1, "__len__"): - if d1[0] != d1[1]: - warnings.warn("Non-square pixel, first dimension taken as feature size.") - d1 = d1[0] - - for _ in range(n_iter): - # back propagate from sensor to mask - M_phi = fresnel_conv(M_p, wv, d1, -dz, dtype=np.float32)[0] - # constrain amplitude at mask to be unity, i.e. phase pattern - M_phi = np.exp(1j * np.angle(M_phi)) - # forward propagate from mask to sensor - M_p = fresnel_conv(M_phi, wv, d1, dz, dtype=np.float32)[0] - # constrain amplitude to be sqrt(PSF) - M_p = np.sqrt(target_psf) * np.exp(1j * np.angle(M_p)) - - phi = (np.angle(M_phi) + 2 * np.pi) % (2 * np.pi) - - if height_map: - return phi, wv * phi / (2 * np.pi * (n - 1)) + if can_torch: + M_p = torch.sqrt(target_psf) + + if hasattr(d1, "__len__"): + if d1[0] != d1[1]: + warnings.warn("Non-square pixel, first dimension taken as feature size.") + d1 = d1[0] + + for _ in range(n_iter): + # back propagate from sensor to mask + M_phi = fresnel_conv(M_p, wv, d1, -dz, dtype=torch.float32)[0] + # constrain amplitude at mask to be unity, i.e. phase pattern + M_phi = torch.exp(1j * torch.angle(M_phi)) + # forward propagate from mask to sensor + M_p = fresnel_conv(M_phi, wv, d1, dz, dtype=torch.float32)[0] + # constrain amplitude to be sqrt(PSF) + M_p = torch.sqrt(target_psf) * torch.exp(1j * torch.angle(M_p)) + + phi = (torch.angle(M_phi) + 2 * torch.pi) % (2 * torch.pi) + + if height_map: + return phi, wv * phi / (2 * torch.pi * (n - 1)) + else: + return phi else: - return phi + M_p = np.sqrt(target_psf) + + if hasattr(d1, "__len__"): + if d1[0] != d1[1]: + warnings.warn("Non-square pixel, first dimension taken as feature size.") + d1 = d1[0] + + for _ in range(n_iter): + # back propagate from sensor to mask + M_phi = fresnel_conv(M_p, wv, d1, -dz, dtype=np.float32)[0] + # constrain amplitude at mask to be unity, i.e. phase pattern + M_phi = np.exp(1j * np.angle(M_phi)) + # forward propagate from mask to sensor + M_p = fresnel_conv(M_phi, wv, d1, dz, dtype=np.float32)[0] + # constrain amplitude to be sqrt(PSF) + M_p = np.sqrt(target_psf) * np.exp(1j * np.angle(M_p)) + + phi = (np.angle(M_phi) + 2 * np.pi) % (2 * np.pi) + + if height_map: + return phi, wv * phi / (2 * np.pi * (n - 1)) + else: + return phi class FresnelZoneAperture(Mask): From eeb93993930d5062d8af88bd5bc1092b11116b7a Mon Sep 17 00:00:00 2001 From: Ahmed Date: Sat, 25 Nov 2023 15:05:00 +0100 Subject: [PATCH 09/39] implement overlapping check --- lensless/hardware/mask.py | 50 ++++++++++++++++++++++++++++++++++++--- 1 file changed, 47 insertions(+), 3 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index e74ed4f6..9c6a35ce 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -358,16 +358,60 @@ def __init__( assert self.loc is not None assert len(self.radius) == len(self.loc) self.N = len(self.radius) + circles = np.array([(self.loc[i][0], self.loc[i][1], self.radius[i]) for i in range(self.N)]) + assert MultiLensArray.no_circle_overlap(circles) else: assert self.N is not None np.random.seed(self.seed) - radius = np.random.uniform(self.min_height, self.distance_sensor, self.N) - result = np.column_stack((np.random.uniform(0,self.size[0],self.N),np.random.uniform(0,self.size[1],self.N))) - loc = np.array([tuple(row) for row in result]) + radius = np.random.uniform(self.min_height, self.distance_sensor, self.N) #TODO: check if it is the right way to do it + self.loc, self.radius = MultiLensArray.place_spheres_on_plane(self.size[0], self.size[1], radius) + assert self.N == len(self.radius) # call the does_circle_overlap method super().__init__() + @staticmethod + def no_circle_overlap(circles): + """Check if any circle in the list overlaps with another.""" + for i in range(len(circles)): + if MultiLensArray.does_circle_overlap(circles[i+1:], circles[i][0], circles[i][1], circles[i][2]): + return False + return True + + @staticmethod + def does_circle_overlap(circles, x, y, r): + """Check if a circle overlaps with any in the list.""" + for (cx, cy, cr) in circles: + if np.sqrt((x - cx)**2 + (y - cy)**2) < r/2 + cr/2: + return True + return False + + @staticmethod + def place_spheres_on_plane(width, height, radius, max_attempts=1000): + """Try to place circles on a 2D plane.""" + placed_circles = [] + radius_sorted = sorted(radius, reverse=True) # Place larger circles first + + for r in radius_sorted: + placed = False + for _ in range(max_attempts): + x = np.random.uniform(r, width - r) + y = np.random.uniform(r, height - r) + + if not MultiLensArray.does_circle_overlap(placed_circles, x, y, r): + placed_circles.append((x, y, r)) + placed = True + break + + if not placed: + print(f"Failed to place circle with rad {r}") + continue + + placed_circles = np.array(placed_circles) + circles = placed_circles[:, :2] + radius = placed_circles[:, 2] + return circles, radius + class PhaseContour(Mask): """ From 9ce0426767fd1a5cfec379bab38b9771b59fa41f Mon Sep 17 00:00:00 2001 From: Ahmed Date: Sun, 26 Nov 2023 14:46:43 +0100 Subject: [PATCH 10/39] Multilens kind of done and tested --- lensless/hardware/mask.py | 266 ++++++++++++++++++++++---------------- test/test_masks.py | 17 ++- 2 files changed, 170 insertions(+), 113 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index da838715..f53e8245 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -32,6 +32,7 @@ from waveprop.noise import add_shot_noise from lensless.hardware.sensor import VirtualSensor from lensless.utils.image import resize +from matplotlib import pyplot as plt try: import torch @@ -322,16 +323,114 @@ def simulate(self, obj, snr_db=20): return meas class MultiLensArray(Mask): + """ + Multi-lens array mask. + """ + def __init__( + self, N = None, radius = None, loc = None, refractive_index = 1.2, design_wv=532e-9, seed = 0, min_height=1e-3, **kwargs + ): + """ + Multi-lens array mask constructor. + + Parameters + ---------- + N: int + Number of lenses + radius: array_like + Radius of the lenses (m) + loc: array_like of tuples + Location of the lenses (m) + refractive_index: float + Refractive index of the mask substrate. Default is 1.2. + wavelength: float + seed: int + Seed for the random number generator. Default is 0. + min_height: float + Minimum height of the lenses (m). Default is 1e-3. + """ + self.N = N + self.radius = radius + self.loc = loc + self.refractive_index = refractive_index + self.wavelength = design_wv + self.seed = seed + self.min_height = min_height + + if self.radius is not None: + assert self.loc is not None + assert len(self.radius) == len(self.loc) + self.N = len(self.radius) + circles = np.array([(self.loc[i][0], self.loc[i][1], self.radius[i]) for i in range(self.N)]) + assert MultiLensArray.no_circle_overlap(circles) + else: + assert self.N is not None + np.random.seed(self.seed) + self.radius = np.random.uniform(self.min_height, 20, self.N) #TODO: check if it is the right way to do it + assert self.N == len(self.radius) + super().__init__(**kwargs) + + + + @staticmethod + def no_circle_overlap(circles): + """Check if any circle in the list overlaps with another.""" + for i in range(len(circles)): + if MultiLensArray.does_circle_overlap(circles[i+1:], circles[i][0], circles[i][1], circles[i][2]): + return False + return True + + @staticmethod + def does_circle_overlap(circles, x, y, r): + """Check if a circle overlaps with any in the list.""" + for (cx, cy, cr) in circles: + if np.sqrt((x - cx)**2 + (y - cy)**2) < r/2 + cr/2: + return True + return False + + @staticmethod + def place_spheres_on_plane(width, height, radius, max_attempts=1000): + """Try to place circles on a 2D plane.""" + placed_circles = [] + radius_sorted = sorted(radius, reverse=True) # Place larger circles first + + for r in radius_sorted: + placed = False + for _ in range(max_attempts): + x = np.random.uniform(r, width - r) + y = np.random.uniform(r, height - r) + + if not MultiLensArray.does_circle_overlap(placed_circles, x, y, r): + placed_circles.append((x, y, r)) + placed = True + print(f"Placed circle with rad {r}, and center ({x}, {y})") + break + + if not placed: + print(f"Failed to place circle with rad {r}") + continue + + placed_circles = np.array(placed_circles) + circles = placed_circles[:, :2] + radius = placed_circles[:, 2] + return circles, radius + def create_mask(self): - height = self.create_height_map(self.radius, self.locs) - phi = (height * (self.refractive_index - 1) * 2 * np.pi / self.wavelength) % (2 * np.pi) + self.loc, self.radius = MultiLensArray.place_spheres_on_plane(self.resolution[0], self.resolution[1], self.radius) + height = self.create_height_map(self.radius, self.loc) + phi = (height * (self.refractive_index - 1) * 2 * np.pi / self.wavelength) #% (2 * np.pi) ? Makes it have some noisy values instead of a continuous sphere + fig, ax = plt.subplots() + im = ax.imshow(phi, cmap="gray") + fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) + plt.show() self.mask = np.exp(1j * phi) + print(np.angle(self.mask[51, 321]), " ", phi[51, 321] - 2*np.pi) def create_height_map(self, radius, locs): - height = np.full((self.size[0], self.size[1]), self.min_height) - for x in height.shape[0]: - for y in height.shape[1]: - height[x, y] += self.lens_contribution(radius, locs, x, y) + height = np.full((self.resolution[0], self.resolution[1]), self.min_height) + for x in range(height.shape[0]): + for y in range(height.shape[1]): + height[x, y] += self.lens_contribution(radius, locs, x + 0.5, y + 0.5) + assert np.all(height >= self.min_height) return height def lens_contribution(self, radius, locs, x, y): @@ -383,67 +482,36 @@ def create_mask(self): """ Creating phase contour from edges of Perlin noise. """ - if not (torch_available and isinstance(self.mask, torch.Tensor)): - # Creating Perlin noise - proper_dim_1 = (self.resolution[0] // self.noise_period[0]) * self.noise_period[0] - proper_dim_2 = (self.resolution[1] // self.noise_period[1]) * self.noise_period[1] - noise = generate_perlin_noise_2d((proper_dim_1, proper_dim_2), self.noise_period) - - # Upscaling to correspond to sensor size - if np.any(self.resolution != noise.shape): - noise = resize(noise[:, :, np.newaxis], shape=tuple(self.resolution) + (1,)).squeeze() - - # Edge detection - binary = np.clip(np.round(np.interp(noise, (-1, 1), (0, 1))), a_min=0, a_max=1) - self.target_psf = cv.Canny(np.interp(binary, (-1, 1), (0, 255)).astype(np.uint8), 0, 255) - - # Computing mask and height map - phase_mask, height_map = phase_retrieval( - target_psf=self.target_psf, - wv=self.design_wv, - d1=self.feature_size, - dz=self.distance_sensor, - n=self.refractive_index, - n_iter=self.n_iter, - height_map=True, - can_torch=True, - ) - self.height_map = height_map - self.phase_pattern = phase_mask - self.mask = np.exp(1j * phase_mask) - else: - #implement the same thing but using torch - proper_dim_1=(self.resolution[0]//self.noise_period[0])*self.noise_period[0] - proper_dim_2=(self.resolution[1]//self.noise_period[1])*self.noise_period[1] - #TODO to torch - noise=generate_perlin_noise_2d((proper_dim_1,proper_dim_2),self.noise_period) - n = noise.ndim - # n = noise.dim() - # end of todo - if torch.any(self.resolution != noise.shape): - noise=resize(noise.unsqueeze(n-1),shape=tuple(self.resolution)+(1,)).squeeze() - - binary = torch.clip(torch.round(torch.nn.functional.grid_sample(noise, (-1, 1), (0, 1))), a_min=0, a_max=1) - self.target_psf = cv.Canny(torch.nn.functional.grid_sample(binary, (-1, 1), (0, 255)).astype(np.uint8), 0, 255) - - # Computing mask and height map - phase_mask, height_map = phase_retrieval( - target_psf=self.target_psf, - wv=self.design_wv, - d1=self.feature_size, - dz=self.distance_sensor, - n=self.refractive_index, - n_iter=self.n_iter, - height_map=True, - can_torch=False, - ) - self.height_map = height_map - self.phase_pattern = phase_mask - self.mask = torch.exp(1j * phase_mask) + # Creating Perlin noise + proper_dim_1 = (self.resolution[0] // self.noise_period[0]) * self.noise_period[0] + proper_dim_2 = (self.resolution[1] // self.noise_period[1]) * self.noise_period[1] + noise = generate_perlin_noise_2d((proper_dim_1, proper_dim_2), self.noise_period) + + # Upscaling to correspond to sensor size + if np.any(self.resolution != noise.shape): + noise = resize(noise[:, :, np.newaxis], shape=tuple(self.resolution) + (1,)).squeeze() + + # Edge detection + binary = np.clip(np.round(np.interp(noise, (-1, 1), (0, 1))), a_min=0, a_max=1) + self.target_psf = cv.Canny(np.interp(binary, (-1, 1), (0, 255)).astype(np.uint8), 0, 255) + + # Computing mask and height map + phase_mask, height_map = phase_retrieval( + target_psf=self.target_psf, + wv=self.design_wv, + d1=self.feature_size, + dz=self.distance_sensor, + n=self.refractive_index, + n_iter=self.n_iter, + height_map=True, + ) + self.height_map = height_map + self.phase_pattern = phase_mask + self.mask = np.exp(1j * phase_mask) -def phase_retrieval(target_psf, wv, d1, dz, n=1.2, n_iter=10, height_map=False, can_torch=False): +def phase_retrieval(target_psf, wv, d1, dz, n=1.2, n_iter=10, height_map=False): """ Iterative phase retrieval algorithm similar to `PhlatCam `_, using Fresnel propagation. @@ -463,54 +531,30 @@ def phase_retrieval(target_psf, wv, d1, dz, n=1.2, n_iter=10, height_map=False, n_iter: int Number of iterations. Default value is 10. """ - if can_torch: - M_p = torch.sqrt(target_psf) - - if hasattr(d1, "__len__"): - if d1[0] != d1[1]: - warnings.warn("Non-square pixel, first dimension taken as feature size.") - d1 = d1[0] - - for _ in range(n_iter): - # back propagate from sensor to mask - M_phi = fresnel_conv(M_p, wv, d1, -dz, dtype=torch.float32)[0] - # constrain amplitude at mask to be unity, i.e. phase pattern - M_phi = torch.exp(1j * torch.angle(M_phi)) - # forward propagate from mask to sensor - M_p = fresnel_conv(M_phi, wv, d1, dz, dtype=torch.float32)[0] - # constrain amplitude to be sqrt(PSF) - M_p = torch.sqrt(target_psf) * torch.exp(1j * torch.angle(M_p)) - - phi = (torch.angle(M_phi) + 2 * torch.pi) % (2 * torch.pi) - - if height_map: - return phi, wv * phi / (2 * torch.pi * (n - 1)) - else: - return phi + + M_p = np.sqrt(target_psf) + + if hasattr(d1, "__len__"): + if d1[0] != d1[1]: + warnings.warn("Non-square pixel, first dimension taken as feature size.") + d1 = d1[0] + + for _ in range(n_iter): + # back propagate from sensor to mask + M_phi = fresnel_conv(M_p, wv, d1, -dz, dtype=np.float32)[0] + # constrain amplitude at mask to be unity, i.e. phase pattern + M_phi = np.exp(1j * np.angle(M_phi)) + # forward propagate from mask to sensor + M_p = fresnel_conv(M_phi, wv, d1, dz, dtype=np.float32)[0] + # constrain amplitude to be sqrt(PSF) + M_p = np.sqrt(target_psf) * np.exp(1j * np.angle(M_p)) + + phi = (np.angle(M_phi) + 2 * np.pi) % (2 * np.pi) + + if height_map: + return phi, wv * phi / (2 * np.pi * (n - 1)) else: - M_p = np.sqrt(target_psf) - - if hasattr(d1, "__len__"): - if d1[0] != d1[1]: - warnings.warn("Non-square pixel, first dimension taken as feature size.") - d1 = d1[0] - - for _ in range(n_iter): - # back propagate from sensor to mask - M_phi = fresnel_conv(M_p, wv, d1, -dz, dtype=np.float32)[0] - # constrain amplitude at mask to be unity, i.e. phase pattern - M_phi = np.exp(1j * np.angle(M_phi)) - # forward propagate from mask to sensor - M_p = fresnel_conv(M_phi, wv, d1, dz, dtype=np.float32)[0] - # constrain amplitude to be sqrt(PSF) - M_p = np.sqrt(target_psf) * np.exp(1j * np.angle(M_p)) - - phi = (np.angle(M_phi) + 2 * np.pi) % (2 * np.pi) - - if height_map: - return phi, wv * phi / (2 * np.pi * (n - 1)) - else: - return phi + return phi class FresnelZoneAperture(Mask): diff --git a/test/test_masks.py b/test/test_masks.py index a16659d6..894a61f7 100644 --- a/test/test_masks.py +++ b/test/test_masks.py @@ -1,7 +1,8 @@ import numpy as np -from lensless.hardware.mask import CodedAperture, PhaseContour, FresnelZoneAperture +from lensless.hardware.mask import CodedAperture, PhaseContour, FresnelZoneAperture, MultiLensArray from lensless.eval.metric import mse, psnr, ssim from waveprop.fresnel import fresnel_conv +from matplotlib import pyplot as plt resolution = np.array([380, 507]) @@ -75,7 +76,6 @@ def test_classmethod(): assert np.all(mask1.mask.shape == resolution) desired_psf_shape = np.array(tuple(resolution) + (len(mask1.psf_wavelength),)) assert np.all(mask1.psf.shape == desired_psf_shape) - mask2 = PhaseContour.from_sensor( sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz ) @@ -90,6 +90,19 @@ def test_classmethod(): desired_psf_shape = np.array(tuple(resolution) + (len(mask3.psf_wavelength),)) assert np.all(mask3.psf.shape == desired_psf_shape) + mask4 = MultiLensArray.from_sensor( + sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, N=10#radius=np.array([10, 25]), loc=np.array([[10.1, 11.3], [56.5, 89.2]]) + ) + assert np.all(mask4.mask.shape == resolution) + desired_psf_shape = np.array(tuple(resolution) + (len(mask4.psf_wavelength),)) + assert np.all(mask3.psf.shape == desired_psf_shape) + + z = np.abs(np.angle(mask4.mask)) + assert np.all(z > 0) + fig, ax = plt.subplots() + #im = ax.imshow(z, cmap="gray") + #fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) + #plt.show() if __name__ == "__main__": test_flatcam() From 12c63f266aa6c7f6837293b8c4130045a87de1c9 Mon Sep 17 00:00:00 2001 From: Ahmed Date: Mon, 27 Nov 2023 09:46:15 +0100 Subject: [PATCH 11/39] height varying tested --- lensless/hardware/mask.py | 52 ++++++++++++++++++++------------------- test/test_masks.py | 14 ++++++++++- 2 files changed, 40 insertions(+), 26 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index 416f71b0..4bbeef09 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -401,7 +401,7 @@ def phase_retrieval(target_psf, wv, d1, dz, n=1.2, n_iter=10, height_map=False): Target PSF to optimize the phase mask for. wv: float Wavelength (m). - d1: float + d1: float= Sample period on the sensor i.e. pixel size (m). dz: float Propagation distance between the mask and the sensor. @@ -473,16 +473,7 @@ def create_mask(self): class HeightVarying(Mask): - - def __init__( - self, - refractive_index = 1.2, - wavelength = 532e-9, - height_map = None, - height_range = (min, max), - seed = 0): - - """ + """ A class representing a height-varying mask for lensless imaging. Parameters @@ -512,26 +503,37 @@ def __init__( ... seed=42 ... ) """ - - super().__init__() + def __init__( + self, + refractive_index = 1.2, + wavelength = 532e-9, + height_map = None, + height_range = (1e-3, 1e-2), + seed = 0, + **kwargs): + + self.refractive_index = refractive_index self.wavelength = wavelength + self.height_range = height_range + self.seed = seed - if self.height_map is not None: + if height_map is not None: self.height_map = height_map else: + self.height_map = None np.random.seed(self.seed) - self.height_map = np.random.uniform(self.height_range[0], self.height_range[1], n) - - self.height_range = height_range - self.seed = seed - - def get_phi(self, n): - phi = self.height_map * (2*np.pi*(n-1) / self.wavelength) + + super().__init__(**kwargs) + + def get_phi(self): + phi = self.height_map * (2*np.pi*(self.refractive_index-1) / self.wavelength) phi = phi % (2*np.pi) return phi - def create_mask(self, n): - phase_mask = self.get_phi(n) - mask = np.exp(1j * self.get_phi(n)) - return mask \ No newline at end of file + def create_mask(self): + if self.height_map is None: + self.height_map = np.random.uniform(self.height_range[0], self.height_range[1], self.resolution) + assert self.height_map.shape == tuple(self.resolution) + phase_mask = self.get_phi() + self.mask = np.exp(1j * phase_mask) \ No newline at end of file diff --git a/test/test_masks.py b/test/test_masks.py index a16659d6..7c52bb46 100644 --- a/test/test_masks.py +++ b/test/test_masks.py @@ -1,7 +1,8 @@ import numpy as np -from lensless.hardware.mask import CodedAperture, PhaseContour, FresnelZoneAperture +from lensless.hardware.mask import CodedAperture, PhaseContour, FresnelZoneAperture, HeightVarying from lensless.eval.metric import mse, psnr, ssim from waveprop.fresnel import fresnel_conv +from matplotlib import pyplot as plt resolution = np.array([380, 507]) @@ -90,6 +91,17 @@ def test_classmethod(): desired_psf_shape = np.array(tuple(resolution) + (len(mask3.psf_wavelength),)) assert np.all(mask3.psf.shape == desired_psf_shape) + mask5 = HeightVarying.from_sensor( + sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz + ) + assert np.all(mask5.mask.shape == resolution) + desired_psf_shape = np.array(tuple(resolution) + (len(mask5.psf_wavelength),)) + assert np.all(mask5.psf.shape == desired_psf_shape) + fig, ax = plt.subplots() + im = ax.imshow(np.angle(mask5.mask), cmap="gray") + fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) + plt.show() + if __name__ == "__main__": test_flatcam() From 7b2a1a45029348f452ad62753f7cec747811dc79 Mon Sep 17 00:00:00 2001 From: Ahmed Elalamy <91589855+aelalamy42@users.noreply.github.com> Date: Mon, 27 Nov 2023 09:50:14 +0100 Subject: [PATCH 12/39] Update mask.py --- lensless/hardware/mask.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index f53e8245..e2512505 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -415,15 +415,11 @@ def place_spheres_on_plane(width, height, radius, max_attempts=1000): return circles, radius def create_mask(self): - self.loc, self.radius = MultiLensArray.place_spheres_on_plane(self.resolution[0], self.resolution[1], self.radius) + if self.loc is None: + self.loc, self.radius = MultiLensArray.place_spheres_on_plane(self.resolution[0], self.resolution[1], self.radius) height = self.create_height_map(self.radius, self.loc) phi = (height * (self.refractive_index - 1) * 2 * np.pi / self.wavelength) #% (2 * np.pi) ? Makes it have some noisy values instead of a continuous sphere - fig, ax = plt.subplots() - im = ax.imshow(phi, cmap="gray") - fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) - plt.show() self.mask = np.exp(1j * phi) - print(np.angle(self.mask[51, 321]), " ", phi[51, 321] - 2*np.pi) def create_height_map(self, radius, locs): height = np.full((self.resolution[0], self.resolution[1]), self.min_height) From 1dc82fc411693f998e5f8662846e4061febbbdfc Mon Sep 17 00:00:00 2001 From: Ahmed Date: Mon, 27 Nov 2023 09:51:44 +0100 Subject: [PATCH 13/39] Multilens patched --- test/test_masks.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/test/test_masks.py b/test/test_masks.py index 894a61f7..99449c89 100644 --- a/test/test_masks.py +++ b/test/test_masks.py @@ -97,12 +97,10 @@ def test_classmethod(): desired_psf_shape = np.array(tuple(resolution) + (len(mask4.psf_wavelength),)) assert np.all(mask3.psf.shape == desired_psf_shape) - z = np.abs(np.angle(mask4.mask)) - assert np.all(z > 0) fig, ax = plt.subplots() - #im = ax.imshow(z, cmap="gray") - #fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) - #plt.show() + im = ax.imshow(np.angle(mask4.mask), cmap="gray") + fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) + plt.show() if __name__ == "__main__": test_flatcam() From 0951bd95d701f18aa3940e620f4a2a1e5561d943 Mon Sep 17 00:00:00 2001 From: Seif Hamed Date: Mon, 27 Nov 2023 17:39:15 +0100 Subject: [PATCH 14/39] added the torch implementation (as a 2nd option) to the HeightVarying mask --- lensless/hardware/mask.py | 34 +++++++++++++++++++++++++--------- 1 file changed, 25 insertions(+), 9 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index fd2380e4..23539a11 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -623,6 +623,7 @@ class HeightVarying(Mask): """ def __init__( self, + is_torch=False, refractive_index = 1.2, wavelength = 532e-9, height_map = None, @@ -630,7 +631,7 @@ def __init__( seed = 0, **kwargs): - + self.is_torch = is_torch self.refractive_index = refractive_index self.wavelength = wavelength self.height_range = height_range @@ -645,13 +646,28 @@ def __init__( super().__init__(**kwargs) def get_phi(self): - phi = self.height_map * (2*np.pi*(self.refractive_index-1) / self.wavelength) - phi = phi % (2*np.pi) - return phi + + if self.is_torch == False: + phi = self.height_map * (2*np.pi*(self.refractive_index-1) / self.wavelength) + phi = phi % (2*np.pi) + return phi + + else: + return torch.from_numpy((self.height_map * (2*np.pi*(self.refractive_index-1) / self.wavelength)) % (2*np.pi)) + def create_mask(self): - if self.height_map is None: - self.height_map = np.random.uniform(self.height_range[0], self.height_range[1], self.resolution) - assert self.height_map.shape == tuple(self.resolution) - phase_mask = self.get_phi() - self.mask = np.exp(1j * phase_mask) \ No newline at end of file + + if self.is_torch == False: + if self.height_map is None: + self.height_map = np.random.uniform(self.height_range[0], self.height_range[1], self.resolution) + assert self.height_map.shape == tuple(self.resolution) + phase_mask = self.get_phi() + self.mask = np.exp(1j * phase_mask) + + else: + if self.height_map is None: + self.height_map = torch.from_numpy(np.random.uniform(self.height_range[0], self.height_range[1], self.resolution)) + assert self.height_map.shape == tuple(self.resolution) + phase_mask = self.get_phi() + self.mask = torch.exp(1j * phase_mask) From 228ca0d7d641841bf49df97b922faf0ac9d07bcd Mon Sep 17 00:00:00 2001 From: Seif Hamed Date: Tue, 28 Nov 2023 11:31:54 +0100 Subject: [PATCH 15/39] set is_torch to TRUE for the height_varying mask --- lensless/hardware/mask.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index 23539a11..935cdf60 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -623,7 +623,7 @@ class HeightVarying(Mask): """ def __init__( self, - is_torch=False, + is_torch=True, refractive_index = 1.2, wavelength = 532e-9, height_map = None, From af1818c52de56815d9e382d3bd3352356e3e9a0d Mon Sep 17 00:00:00 2001 From: Seif Hamed Date: Tue, 28 Nov 2023 11:46:01 +0100 Subject: [PATCH 16/39] added the test for the torch case in test_masks.py --- test/test_masks.py | 26 +++++++++++++++++++------- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/test/test_masks.py b/test/test_masks.py index f128f7b5..db2bd414 100644 --- a/test/test_masks.py +++ b/test/test_masks.py @@ -101,16 +101,28 @@ def test_classmethod(): im = ax.imshow(np.angle(mask4.mask), cmap="gray") fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) plt.show() + mask5 = HeightVarying.from_sensor( sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz ) - assert np.all(mask5.mask.shape == resolution) - desired_psf_shape = np.array(tuple(resolution) + (len(mask5.psf_wavelength),)) - assert np.all(mask5.psf.shape == desired_psf_shape) - fig, ax = plt.subplots() - im = ax.imshow(np.angle(mask5.mask), cmap="gray") - fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) - plt.show() + if mask5.is_Torch == False: + assert np.all(mask5.mask.shape == resolution) + desired_psf_shape = np.array(tuple(resolution) + (len(mask5.psf_wavelength),)) + assert np.all(mask5.psf.shape == desired_psf_shape) + fig, ax = plt.subplots() + im = ax.imshow(np.angle(mask5.mask), cmap="gray") + fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) + plt.show() + else: + assert torch.all(mask5.mask.shape == torch.tensor(resolution)) + desired_psf_shape = torch.tensor(resolution + (len(mask5.psf_wavelength),)) + assert torch.all(mask5.psf.shape == desired_psf_shape) + fig, ax = plt.subplots() + im = ax.imshow(torch.angle(mask5.mask), cmap="gray") + fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) + plt.show() + + if __name__ == "__main__": test_flatcam() From 2bd322c4f0ce4610b485576e039a75f99a5a77a2 Mon Sep 17 00:00:00 2001 From: Ghita2002 Date: Wed, 29 Nov 2023 11:36:13 +0100 Subject: [PATCH 17/39] from numpy to torch --- lensless/hardware/mask.py | 54 +++++++++++++++++++++++++++------------ 1 file changed, 38 insertions(+), 16 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index bbdc8193..7abf1bfa 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -327,7 +327,7 @@ class MultiLensArray(Mask): Multi-lens array mask. """ def __init__( - self, N = None, radius = None, loc = None, refractive_index = 1.2, design_wv=532e-9, seed = 0, min_height=1e-3, **kwargs + self, N = None, radius = None, loc = None, refractive_index = 1.2, design_wv=532e-9, seed = 0, min_height=1e-3, istorch=False, **kwargs ): """ Multi-lens array mask constructor. @@ -355,37 +355,59 @@ def __init__( self.wavelength = design_wv self.seed = seed self.min_height = min_height + self.istorch=istorch - if self.radius is not None: - assert self.loc is not None - assert len(self.radius) == len(self.loc) - self.N = len(self.radius) - circles = np.array([(self.loc[i][0], self.loc[i][1], self.radius[i]) for i in range(self.N)]) - assert MultiLensArray.no_circle_overlap(circles) + if not istorch : + if self.radius is not None: + assert self.loc is not None + assert len(self.radius) == len(self.loc) + self.N = len(self.radius) + circles = np.array([(self.loc[i][0], self.loc[i][1], self.radius[i]) for i in range(self.N)]) + assert MultiLensArray.no_circle_overlap(circles, self.istorch) + else: + assert self.N is not None + np.random.seed(self.seed) + self.radius = np.random.uniform(self.min_height, 20, self.N) #TODO: check if it is the right way to do it + assert self.N == len(self.radius) + elif self.radius is not None: + assert self.loc is not None + assert len(self.radius) == len(self.loc) + self.N = len(self.radius) + circles = torch.tensor([(self.loc[i][0], self.loc[i][1], self.radius[i]) for i in range(self.N)]) + assert MultiLensArray.no_circle_overlap(circles, self.istorch) else: assert self.N is not None - np.random.seed(self.seed) - self.radius = np.random.uniform(self.min_height, 20, self.N) #TODO: check if it is the right way to do it + torch.manual_seed(self.seed) + self.radius = torch.rand(self.N) * (20 - self.min_height) + self.min_height #TODO: check if it is the right way to do it assert self.N == len(self.radius) + super().__init__(**kwargs) @staticmethod - def no_circle_overlap(circles): + def no_circle_overlap(circles, istorch): """Check if any circle in the list overlaps with another.""" for i in range(len(circles)): - if MultiLensArray.does_circle_overlap(circles[i+1:], circles[i][0], circles[i][1], circles[i][2]): + if MultiLensArray.does_circle_overlap(circles[i+1:], circles[i][0], circles[i][1], circles[i][2], istorch): return False return True @staticmethod - def does_circle_overlap(circles, x, y, r): + def does_circle_overlap(circles, x, y, r, istorch): """Check if a circle overlaps with any in the list.""" - for (cx, cy, cr) in circles: - if np.sqrt((x - cx)**2 + (y - cy)**2) < r/2 + cr/2: - return True - return False + if not istorch: + for (cx, cy, cr) in circles: + if np.sqrt((x - cx)**2 + (y - cy)**2) < r/2 + cr/2: + return True + return False + else: + for circle in circles: + cx, cy, cr = circle.tolist() # Convert the PyTorch tensor to a list + if torch.sqrt((x - cx)**2 + (y - cy)**2) < r/2 + cr/2: + return True + return False + @staticmethod def place_spheres_on_plane(width, height, radius, max_attempts=1000): From 51e6966d572b967e766442dfb7a302afa65546ba Mon Sep 17 00:00:00 2001 From: Seif-Wessam Date: Thu, 30 Nov 2023 14:21:33 +0100 Subject: [PATCH 18/39] edits to the pytorch implementation --- lensless/hardware/mask.py | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index 935cdf60..001066cc 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -624,6 +624,7 @@ class HeightVarying(Mask): def __init__( self, is_torch=True, + device = "cpu", refractive_index = 1.2, wavelength = 532e-9, height_map = None, @@ -636,6 +637,7 @@ def __init__( self.wavelength = wavelength self.height_range = height_range self.seed = seed + self.device = torch.device(device) if height_map is not None: self.height_map = height_map @@ -646,18 +648,14 @@ def __init__( super().__init__(**kwargs) def get_phi(self): - + phi = self.height_map * (2*np.pi*(self.refractive_index-1) / self.wavelength) + phi = phi % (2*np.pi) if self.is_torch == False: - phi = self.height_map * (2*np.pi*(self.refractive_index-1) / self.wavelength) - phi = phi % (2*np.pi) return phi - else: - return torch.from_numpy((self.height_map * (2*np.pi*(self.refractive_index-1) / self.wavelength)) % (2*np.pi)) + return torch.tensor(phi) - def create_mask(self): - if self.is_torch == False: if self.height_map is None: self.height_map = np.random.uniform(self.height_range[0], self.height_range[1], self.resolution) @@ -667,7 +665,14 @@ def create_mask(self): else: if self.height_map is None: - self.height_map = torch.from_numpy(np.random.uniform(self.height_range[0], self.height_range[1], self.resolution)) + height_range_tensor = torch.tensor(self.height_range) + # Generate a random height map using PyTorch + resolution = torch.tensor(self.resolution) + self.height_map = torch.rand((resolution[0], resolution[1])) * (height_range_tensor[1] - height_range_tensor[0]) + height_range_tensor[0] + #print("self.height_map.shape:", self.height_map.shape) + #print("tuple(self.resolution):", tuple(self.resolution)) assert self.height_map.shape == tuple(self.resolution) phase_mask = self.get_phi() self.mask = torch.exp(1j * phase_mask) + + From 9254864af39b1f4ca058ea208d9e2c5cd1b837f5 Mon Sep 17 00:00:00 2001 From: Seif-Wessam Date: Thu, 30 Nov 2023 14:46:26 +0100 Subject: [PATCH 19/39] added the device to HeightVarying --- lensless/hardware/mask.py | 1 - 1 file changed, 1 deletion(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index 001066cc..43f6568d 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -624,7 +624,6 @@ class HeightVarying(Mask): def __init__( self, is_torch=True, - device = "cpu", refractive_index = 1.2, wavelength = 532e-9, height_map = None, From 2a92bbbf106ed646d6ac18e6706f52bad0d5c3f2 Mon Sep 17 00:00:00 2001 From: Seif-Wessam Date: Thu, 30 Nov 2023 14:48:39 +0100 Subject: [PATCH 20/39] added the device --- lensless/hardware/mask.py | 1 + 1 file changed, 1 insertion(+) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index 43f6568d..001066cc 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -624,6 +624,7 @@ class HeightVarying(Mask): def __init__( self, is_torch=True, + device = "cpu", refractive_index = 1.2, wavelength = 532e-9, height_map = None, From 7209fee96d56f782a18ebc5e7e88495510169aa9 Mon Sep 17 00:00:00 2001 From: Ahmed Date: Thu, 30 Nov 2023 15:07:12 +0100 Subject: [PATCH 21/39] fix phase of multilens --- lensless/hardware/mask.py | 34 +++++++++++++++++++++++----------- test/test_masks.py | 7 ++++--- 2 files changed, 27 insertions(+), 14 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index fd2380e4..adfd3950 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -365,7 +365,7 @@ def __init__( else: assert self.N is not None np.random.seed(self.seed) - self.radius = np.random.uniform(self.min_height, 20, self.N) #TODO: check if it is the right way to do it + self.radius = np.random.uniform(self.min_height, 1e-5, self.N) #TODO: check if it is the right way to do it assert self.N == len(self.radius) super().__init__(**kwargs) @@ -383,14 +383,14 @@ def no_circle_overlap(circles): def does_circle_overlap(circles, x, y, r): """Check if a circle overlaps with any in the list.""" for (cx, cy, cr) in circles: - if np.sqrt((x - cx)**2 + (y - cy)**2) < r/2 + cr/2: + if np.sqrt((x - cx)**2 + (y - cy)**2) < r + cr: return True return False - @staticmethod - def place_spheres_on_plane(width, height, radius, max_attempts=1000): + def place_spheres_on_plane(self, width, height, radius, max_attempts=1000): """Try to place circles on a 2D plane.""" placed_circles = [] + placed_circles_res = [] radius_sorted = sorted(radius, reverse=True) # Place larger circles first for r in radius_sorted: @@ -399,8 +399,9 @@ def place_spheres_on_plane(width, height, radius, max_attempts=1000): x = np.random.uniform(r, width - r) y = np.random.uniform(r, height - r) - if not MultiLensArray.does_circle_overlap(placed_circles, x, y, r): + if not MultiLensArray.does_circle_overlap(placed_circles, x , y , r): placed_circles.append((x, y, r)) + placed_circles_res.append((x / self.feature_size[0], y / self.feature_size[1], r / self.feature_size[0])) placed = True print(f"Placed circle with rad {r}, and center ({x}, {y})") break @@ -416,16 +417,27 @@ def place_spheres_on_plane(width, height, radius, max_attempts=1000): def create_mask(self): if self.loc is None: - self.loc, self.radius = MultiLensArray.place_spheres_on_plane(self.resolution[0], self.resolution[1], self.radius) - height = self.create_height_map(self.radius, self.loc) - phi = (height * (self.refractive_index - 1) * 2 * np.pi / self.wavelength) #% (2 * np.pi) ? Makes it have some noisy values instead of a continuous sphere + self.loc, self.radius = self.place_spheres_on_plane(self.size[0], self.size[1], self.radius) + locs_res = self.loc * (1/self.feature_size) + radius_res = self.radius * (1/self.feature_size[0]) + height = self.create_height_map(radius_res, locs_res) + + phi = (height * (self.refractive_index - 1) * 2 * np.pi / self.wavelength) #? Makes it have some noisy values instead of a continuous sphere + max_val = np.max(phi) + + phi = phi * (2 * np.pi / max_val) + min_val = np.min(phi) + fig, ax = plt.subplots() + im = ax.imshow(phi, cmap="gray") + fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) + plt.show() self.mask = np.exp(1j * phi) def create_height_map(self, radius, locs): height = np.full((self.resolution[0], self.resolution[1]), self.min_height) for x in range(height.shape[0]): for y in range(height.shape[1]): - height[x, y] += self.lens_contribution(radius, locs, x + 0.5, y + 0.5) + height[x, y] += self.lens_contribution(radius, locs, (x + 0.5), (y + 0.5)) * self.feature_size[0] assert np.all(height >= self.min_height) return height @@ -433,7 +445,7 @@ def lens_contribution(self, radius, locs, x, y): contribution = 0 for idx, loc in enumerate(locs): if (x-loc[0])**2 + (y-loc[1])**2 < radius[idx]**2: - contribution = np.sqrt(radius[idx]**2 - (x-loc[0])**2 - (y-loc[1])**2) + contribution = np.sqrt((radius[idx])**2 - ((x-loc[0]))**2 - ((y -loc[1]))**2) return contribution return contribution @@ -626,7 +638,7 @@ def __init__( refractive_index = 1.2, wavelength = 532e-9, height_map = None, - height_range = (1e-3, 1e-2), + height_range = (1e-5, 1e-3), seed = 0, **kwargs): diff --git a/test/test_masks.py b/test/test_masks.py index f128f7b5..a46a1a12 100644 --- a/test/test_masks.py +++ b/test/test_masks.py @@ -91,18 +91,19 @@ def test_classmethod(): assert np.all(mask3.psf.shape == desired_psf_shape) mask4 = MultiLensArray.from_sensor( - sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, N=10#radius=np.array([10, 25]), loc=np.array([[10.1, 11.3], [56.5, 89.2]]) + sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, N=10, seed=569#radius=np.array([10, 25]), loc=np.array([[10.1, 11.3], [56.5, 89.2]]) ) assert np.all(mask4.mask.shape == resolution) desired_psf_shape = np.array(tuple(resolution) + (len(mask4.psf_wavelength),)) - assert np.all(mask3.psf.shape == desired_psf_shape) + assert np.all(mask4.psf.shape == desired_psf_shape) fig, ax = plt.subplots() im = ax.imshow(np.angle(mask4.mask), cmap="gray") fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) + plt.title("Rescaling") plt.show() mask5 = HeightVarying.from_sensor( - sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz + sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, ) assert np.all(mask5.mask.shape == resolution) desired_psf_shape = np.array(tuple(resolution) + (len(mask5.psf_wavelength),)) From 613dc07b1e81e710b96455d6201ba9524bd5d586 Mon Sep 17 00:00:00 2001 From: Seif-Wessam Date: Thu, 30 Nov 2023 15:10:21 +0100 Subject: [PATCH 22/39] added is_Torch to the constructor and to compute_psf --- lensless/hardware/mask.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index 001066cc..cb6dac5b 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -54,6 +54,8 @@ def __init__( size=None, feature_size=None, psf_wavelength=[460e-9, 550e-9, 640e-9], + is_Torch=False, + device="cpu", **kwargs ): """ @@ -103,6 +105,8 @@ def __init__( else: self.feature_size = feature_size self.distance_sensor = distance_sensor + self.is_Torch = is_Torch + self.device = device # create mask self.mask = None @@ -170,6 +174,8 @@ def compute_psf(self): # intensity PSF self.psf = np.abs(psf) ** 2 + if self.is_Torch == True: + self.psf = torch.Tensor(self.psf).to(self.device) class CodedAperture(Mask): @@ -653,7 +659,7 @@ def get_phi(self): if self.is_torch == False: return phi else: - return torch.tensor(phi) + return torch.tensor(phi).to(self.device) def create_mask(self): if self.is_torch == False: @@ -669,10 +675,11 @@ def create_mask(self): # Generate a random height map using PyTorch resolution = torch.tensor(self.resolution) self.height_map = torch.rand((resolution[0], resolution[1])) * (height_range_tensor[1] - height_range_tensor[0]) + height_range_tensor[0] + self.height_map.to(self.device) #print("self.height_map.shape:", self.height_map.shape) #print("tuple(self.resolution):", tuple(self.resolution)) assert self.height_map.shape == tuple(self.resolution) phase_mask = self.get_phi() - self.mask = torch.exp(1j * phase_mask) + self.mask = torch.exp(1j * phase_mask).to(self.device) From af267084920c4274dc25d552336b29d91a10b83d Mon Sep 17 00:00:00 2001 From: Seif-Wessam Date: Thu, 30 Nov 2023 15:20:46 +0100 Subject: [PATCH 23/39] changed the compute_psf function to include is_torch --- lensless/hardware/mask.py | 48 +++++++++++++++++++++++++-------------- 1 file changed, 31 insertions(+), 17 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index cb6dac5b..51d8d250 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -161,22 +161,37 @@ def compute_psf(self): Compute the intensity PSF with bandlimited angular spectrum (BLAS) for each wavelength. Common to all types of masks. """ - psf = np.zeros(tuple(self.resolution) + (len(self.psf_wavelength),), dtype=np.complex64) - for i, wv in enumerate(self.psf_wavelength): - psf[:, :, i] = angular_spectrum( - u_in=self.mask, - wv=wv, - d1=self.feature_size, - dz=self.distance_sensor, - dtype=np.float32, - bandlimit=True, - )[0] - - # intensity PSF - self.psf = np.abs(psf) ** 2 - if self.is_Torch == True: + if self.is_Torch == False: + psf = np.zeros(tuple(self.resolution) + (len(self.psf_wavelength),), dtype=np.complex64) + for i, wv in enumerate(self.psf_wavelength): + psf[:, :, i] = angular_spectrum( + u_in=self.mask, + wv=wv, + d1=self.feature_size, + dz=self.distance_sensor, + dtype=np.float32, + bandlimit=True, + )[0] + + # intensity PSF + self.psf = np.abs(psf) ** 2 + else: + psf = np.zeros(tuple(self.resolution) + (len(self.psf_wavelength),), dtype=np.complex64) + for i, wv in enumerate(self.psf_wavelength): + psf[:, :, i] = angular_spectrum( + u_in=self.mask, + wv=wv, + d1=self.feature_size, + dz=self.distance_sensor, + dtype=np.float32, + bandlimit=True, + device = self.device + )[0] + + # intensity PSF + self.psf = np.abs(psf) ** 2 self.psf = torch.Tensor(self.psf).to(self.device) - + class CodedAperture(Mask): """ @@ -674,8 +689,7 @@ def create_mask(self): height_range_tensor = torch.tensor(self.height_range) # Generate a random height map using PyTorch resolution = torch.tensor(self.resolution) - self.height_map = torch.rand((resolution[0], resolution[1])) * (height_range_tensor[1] - height_range_tensor[0]) + height_range_tensor[0] - self.height_map.to(self.device) + self.height_map = torch.rand((resolution[0], resolution[1])).to(self.device) * (height_range_tensor[1] - height_range_tensor[0]) + height_range_tensor[0] #print("self.height_map.shape:", self.height_map.shape) #print("tuple(self.resolution):", tuple(self.resolution)) assert self.height_map.shape == tuple(self.resolution) From def924c6de94d8372d98d3f3bec506fa6b7070d3 Mon Sep 17 00:00:00 2001 From: Ghita2002 Date: Thu, 30 Nov 2023 15:28:47 +0100 Subject: [PATCH 24/39] some tests --- lensless/hardware/mask.py | 36 +++-------------------------- lensless/hardware/trainable_mask.py | 5 ++++ test/test_masks.py | 18 +++++++++------ 3 files changed, 19 insertions(+), 40 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index 7abf1bfa..64c2a12c 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -115,7 +115,7 @@ def __init__( self.compute_psf() @classmethod - def from_sensor(cls, sensor_name, downsample=None, **kwargs): + def from_sensor(cls, sensor_name, downsample=None, istorch=False,**kwargs): """ Constructor from an existing virtual sensor that copies over the sensor parameters (sensor resolution, sensor size, feature size). @@ -522,44 +522,13 @@ def create_mask(self): n=self.refractive_index, n_iter=self.n_iter, height_map=True, - can_torch=True, ) self.height_map = height_map self.phase_pattern = phase_mask self.mask = np.exp(1j * phase_mask) - else: - #implement the same thing but using torch - proper_dim_1=(self.resolution[0]//self.noise_period[0])*self.noise_period[0] - proper_dim_2=(self.resolution[1]//self.noise_period[1])*self.noise_period[1] - #TODO to torch - noise=generate_perlin_noise_2d((proper_dim_1,proper_dim_2),self.noise_period) - n = noise.ndim - # n = noise.dim() - # end of todo - if torch.any(self.resolution != noise.shape): - noise=resize(noise.unsqueeze(n-1),shape=tuple(self.resolution)+(1,)).squeeze() - - binary = torch.clip(torch.round(torch.nn.functional.grid_sample(noise, (-1, 1), (0, 1))), a_min=0, a_max=1) - self.target_psf = cv.Canny(torch.nn.functional.grid_sample(binary, (-1, 1), (0, 255)).astype(np.uint8), 0, 255) - # Computing mask and height map - phase_mask, height_map = phase_retrieval( - target_psf=self.target_psf, - wv=self.design_wv, - d1=self.feature_size, - dz=self.distance_sensor, - n=self.refractive_index, - n_iter=self.n_iter, - height_map=True, - can_torch=False, - ) - self.height_map = height_map - self.phase_pattern = phase_mask - self.mask = torch.exp(1j * phase_mask) - - -def phase_retrieval(target_psf, wv, d1, dz, n=1.2, n_iter=10, height_map=False, can_torch=False): +def phase_retrieval(target_psf, wv, d1, dz, n=1.2, n_iter=10, height_map=False): """ Iterative phase retrieval algorithm similar to `PhlatCam `_, using Fresnel propagation. @@ -582,6 +551,7 @@ def phase_retrieval(target_psf, wv, d1, dz, n=1.2, n_iter=10, height_map=False, M_p = np.sqrt(target_psf) + if hasattr(d1, "__len__"): if d1[0] != d1[1]: warnings.warn("Non-square pixel, first dimension taken as feature size.") diff --git a/lensless/hardware/trainable_mask.py b/lensless/hardware/trainable_mask.py index f0d258ba..e1ed68b7 100644 --- a/lensless/hardware/trainable_mask.py +++ b/lensless/hardware/trainable_mask.py @@ -71,6 +71,11 @@ def get_vals(self): def project(self): """Abstract method for projecting the mask parameters to a valid space (should be a subspace of [0,1]).""" raise NotImplementedError + + @classmethod + def from_mask(cls, mask, **kwargs): + return cls(initial_mask=mask) + class TrainablePSF(TrainableMask): diff --git a/test/test_masks.py b/test/test_masks.py index f128f7b5..b1dea015 100644 --- a/test/test_masks.py +++ b/test/test_masks.py @@ -3,6 +3,7 @@ from lensless.eval.metric import mse, psnr, ssim from waveprop.fresnel import fresnel_conv from matplotlib import pyplot as plt +from lensless.hardware.trainable_mask import TrainableMask resolution = np.array([380, 507]) @@ -35,7 +36,7 @@ def test_flatcam(): desired_psf_shape = np.array(tuple(resolution) + (len(mask2.psf_wavelength),)) assert np.all(mask2.psf.shape == desired_psf_shape) - +""" def test_phlatcam(): mask = PhaseContour( @@ -54,7 +55,7 @@ def test_phlatcam(): assert mse(abs(Mp), np.sqrt(mask.target_psf)) < 0.1 assert psnr(abs(Mp), np.sqrt(mask.target_psf)) > 30 assert abs(1 - ssim(abs(Mp), np.sqrt(mask.target_psf), channel_axis=None)) < 0.1 - +""" def test_fza(): @@ -76,13 +77,13 @@ def test_classmethod(): assert np.all(mask1.mask.shape == resolution) desired_psf_shape = np.array(tuple(resolution) + (len(mask1.psf_wavelength),)) assert np.all(mask1.psf.shape == desired_psf_shape) - mask2 = PhaseContour.from_sensor( + """mask2 = PhaseContour.from_sensor( sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz ) assert np.all(mask2.mask.shape == resolution) desired_psf_shape = np.array(tuple(resolution) + (len(mask2.psf_wavelength),)) assert np.all(mask2.psf.shape == desired_psf_shape) - +""" mask3 = FresnelZoneAperture.from_sensor( sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz ) @@ -91,11 +92,12 @@ def test_classmethod(): assert np.all(mask3.psf.shape == desired_psf_shape) mask4 = MultiLensArray.from_sensor( - sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, N=10#radius=np.array([10, 25]), loc=np.array([[10.1, 11.3], [56.5, 89.2]]) + sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, N=10, istorch=True#radius=np.array([10, 25]), loc=np.array([[10.1, 11.3], [56.5, 89.2]]) ) + train1 = TrainableMask.from_mask(mask4) assert np.all(mask4.mask.shape == resolution) desired_psf_shape = np.array(tuple(resolution) + (len(mask4.psf_wavelength),)) - assert np.all(mask3.psf.shape == desired_psf_shape) + assert np.all(mask4.psf.shape == desired_psf_shape) fig, ax = plt.subplots() im = ax.imshow(np.angle(mask4.mask), cmap="gray") @@ -112,8 +114,10 @@ def test_classmethod(): fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) plt.show() + + if __name__ == "__main__": test_flatcam() - test_phlatcam() +## test_phlatcam() test_fza() test_classmethod() From d2684bc046d8fe16ccfb78f3beceddc3a79ab25e Mon Sep 17 00:00:00 2001 From: Seif-Wessam Date: Thu, 30 Nov 2023 15:38:00 +0100 Subject: [PATCH 25/39] fixed the heightVarying with torch --- lensless/hardware/mask.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index 51d8d250..51fdd8e9 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -176,6 +176,7 @@ def compute_psf(self): # intensity PSF self.psf = np.abs(psf) ** 2 else: + print(self.device) psf = np.zeros(tuple(self.resolution) + (len(self.psf_wavelength),), dtype=np.complex64) for i, wv in enumerate(self.psf_wavelength): psf[:, :, i] = angular_spectrum( @@ -644,8 +645,7 @@ class HeightVarying(Mask): """ def __init__( self, - is_torch=True, - device = "cpu", + refractive_index = 1.2, wavelength = 532e-9, height_map = None, @@ -653,12 +653,12 @@ def __init__( seed = 0, **kwargs): - self.is_torch = is_torch + self.refractive_index = refractive_index self.wavelength = wavelength self.height_range = height_range self.seed = seed - self.device = torch.device(device) + if height_map is not None: self.height_map = height_map @@ -671,13 +671,13 @@ def __init__( def get_phi(self): phi = self.height_map * (2*np.pi*(self.refractive_index-1) / self.wavelength) phi = phi % (2*np.pi) - if self.is_torch == False: + if self.is_Torch == False: return phi else: return torch.tensor(phi).to(self.device) def create_mask(self): - if self.is_torch == False: + if self.is_Torch == False: if self.height_map is None: self.height_map = np.random.uniform(self.height_range[0], self.height_range[1], self.resolution) assert self.height_map.shape == tuple(self.resolution) From 26acdd19ee5ebad8c023c959584d7d9111ca4a0e Mon Sep 17 00:00:00 2001 From: Seif-Wessam Date: Thu, 30 Nov 2023 15:43:28 +0100 Subject: [PATCH 26/39] changed the testing code to have is_Torch=True --- test/test_masks.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_masks.py b/test/test_masks.py index db2bd414..372613be 100644 --- a/test/test_masks.py +++ b/test/test_masks.py @@ -3,7 +3,7 @@ from lensless.eval.metric import mse, psnr, ssim from waveprop.fresnel import fresnel_conv from matplotlib import pyplot as plt - +import torch resolution = np.array([380, 507]) d1 = 3e-6 @@ -103,7 +103,7 @@ def test_classmethod(): plt.show() mask5 = HeightVarying.from_sensor( - sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz + sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, is_Torch=True ) if mask5.is_Torch == False: assert np.all(mask5.mask.shape == resolution) From de51f5cab12a86f6943e163e9363ebd86f8b0d6b Mon Sep 17 00:00:00 2001 From: Seif Hamed Date: Fri, 1 Dec 2023 10:13:58 +0100 Subject: [PATCH 27/39] corrected the code for testing the torch part of HeightVaryingMask --- lensless/hardware/mask.py | 11 +++++++++-- test/test_masks.py | 11 ++++++++--- 2 files changed, 17 insertions(+), 5 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index 51fdd8e9..f355bebe 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -690,10 +690,17 @@ def create_mask(self): # Generate a random height map using PyTorch resolution = torch.tensor(self.resolution) self.height_map = torch.rand((resolution[0], resolution[1])).to(self.device) * (height_range_tensor[1] - height_range_tensor[0]) + height_range_tensor[0] - #print("self.height_map.shape:", self.height_map.shape) - #print("tuple(self.resolution):", tuple(self.resolution)) assert self.height_map.shape == tuple(self.resolution) phase_mask = self.get_phi() self.mask = torch.exp(1j * phase_mask).to(self.device) + + """if self.height_map is None: + self.height_map = np.random.uniform(self.height_range[0], self.height_range[1], self.resolution) + assert self.height_map.shape == tuple(self.resolution) + phase_mask = self.get_phi() + self.mask = np.exp(1j * phase_mask) + if self.is_Torch == True: + self.mask = torch.tensor(self.mask).to(self.device)""" + diff --git a/test/test_masks.py b/test/test_masks.py index 372613be..1d9d4b74 100644 --- a/test/test_masks.py +++ b/test/test_masks.py @@ -105,7 +105,8 @@ def test_classmethod(): mask5 = HeightVarying.from_sensor( sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, is_Torch=True ) - if mask5.is_Torch == False: + if not mask5.is_Torch: + # NumPy operations assert np.all(mask5.mask.shape == resolution) desired_psf_shape = np.array(tuple(resolution) + (len(mask5.psf_wavelength),)) assert np.all(mask5.psf.shape == desired_psf_shape) @@ -114,9 +115,10 @@ def test_classmethod(): fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) plt.show() else: - assert torch.all(mask5.mask.shape == torch.tensor(resolution)) + # PyTorch operations + assert torch.equal(torch.tensor(mask5.mask.shape), torch.tensor(resolution)) desired_psf_shape = torch.tensor(resolution + (len(mask5.psf_wavelength),)) - assert torch.all(mask5.psf.shape == desired_psf_shape) + assert torch.equal(torch.tensor(mask5.psf.shape), desired_psf_shape) fig, ax = plt.subplots() im = ax.imshow(torch.angle(mask5.mask), cmap="gray") fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) @@ -124,6 +126,9 @@ def test_classmethod(): + + + if __name__ == "__main__": test_flatcam() test_phlatcam() From 8c4b49b65ee5297d214acbd8a99f48a3ee1dc06c Mon Sep 17 00:00:00 2001 From: Seif Hamed Date: Sat, 2 Dec 2023 13:09:20 +0100 Subject: [PATCH 28/39] improvement to the test. --- lensless/hardware/mask.py | 7 +++---- test/test_masks.py | 4 ++-- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index f355bebe..fdfcf578 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -176,7 +176,6 @@ def compute_psf(self): # intensity PSF self.psf = np.abs(psf) ** 2 else: - print(self.device) psf = np.zeros(tuple(self.resolution) + (len(self.psf_wavelength),), dtype=np.complex64) for i, wv in enumerate(self.psf_wavelength): psf[:, :, i] = angular_spectrum( @@ -689,7 +688,9 @@ def create_mask(self): height_range_tensor = torch.tensor(self.height_range) # Generate a random height map using PyTorch resolution = torch.tensor(self.resolution) + print('resolution=', resolution) self.height_map = torch.rand((resolution[0], resolution[1])).to(self.device) * (height_range_tensor[1] - height_range_tensor[0]) + height_range_tensor[0] + print('self.height_map.shape=', self.height_map.shape) assert self.height_map.shape == tuple(self.resolution) phase_mask = self.get_phi() self.mask = torch.exp(1j * phase_mask).to(self.device) @@ -701,6 +702,4 @@ def create_mask(self): self.mask = np.exp(1j * phase_mask) if self.is_Torch == True: self.mask = torch.tensor(self.mask).to(self.device)""" - - - + \ No newline at end of file diff --git a/test/test_masks.py b/test/test_masks.py index 1d9d4b74..d8f25975 100644 --- a/test/test_masks.py +++ b/test/test_masks.py @@ -90,7 +90,7 @@ def test_classmethod(): desired_psf_shape = np.array(tuple(resolution) + (len(mask3.psf_wavelength),)) assert np.all(mask3.psf.shape == desired_psf_shape) - mask4 = MultiLensArray.from_sensor( + '''mask4 = MultiLensArray.from_sensor( sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, N=10#radius=np.array([10, 25]), loc=np.array([[10.1, 11.3], [56.5, 89.2]]) ) assert np.all(mask4.mask.shape == resolution) @@ -100,7 +100,7 @@ def test_classmethod(): fig, ax = plt.subplots() im = ax.imshow(np.angle(mask4.mask), cmap="gray") fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) - plt.show() + plt.show()''' mask5 = HeightVarying.from_sensor( sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, is_Torch=True From 2cfaa7fb1758d50e332d7f8141977f499a78cf4b Mon Sep 17 00:00:00 2001 From: Ahmed Date: Mon, 4 Dec 2023 14:48:24 +0100 Subject: [PATCH 29/39] fix phase of multilens --- lensless/hardware/mask.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index adfd3950..b784d8fa 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -422,11 +422,8 @@ def create_mask(self): radius_res = self.radius * (1/self.feature_size[0]) height = self.create_height_map(radius_res, locs_res) - phi = (height * (self.refractive_index - 1) * 2 * np.pi / self.wavelength) #? Makes it have some noisy values instead of a continuous sphere - max_val = np.max(phi) + phi = (height * (self.refractive_index - 1) * 2 * np.pi / self.wavelength) #% (2*np.pi)#? Makes it have some noisy values instead of a continuous sphere - phi = phi * (2 * np.pi / max_val) - min_val = np.min(phi) fig, ax = plt.subplots() im = ax.imshow(phi, cmap="gray") fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) From 394201be37ad55ede9a0b7a16b255f7cff6f82fe Mon Sep 17 00:00:00 2001 From: Ahmed Date: Mon, 4 Dec 2023 16:00:07 +0100 Subject: [PATCH 30/39] torch implementation of height varying --- lensless/hardware/mask.py | 18 ++++++------------ test/test_masks.py | 5 +++-- 2 files changed, 9 insertions(+), 14 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index a6f0204e..c7251027 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -176,7 +176,7 @@ def compute_psf(self): # intensity PSF self.psf = np.abs(psf) ** 2 else: - psf = np.zeros(tuple(self.resolution) + (len(self.psf_wavelength),), dtype=np.complex64) + psf = torch.zeros(tuple(self.resolution) + (len(self.psf_wavelength),), dtype=torch.complex64) for i, wv in enumerate(self.psf_wavelength): psf[:, :, i] = angular_spectrum( u_in=self.mask, @@ -189,7 +189,7 @@ def compute_psf(self): )[0] # intensity PSF - self.psf = np.abs(psf) ** 2 + self.psf = torch.abs(psf) ** 2 self.psf = torch.Tensor(self.psf).to(self.device) @@ -672,7 +672,7 @@ def __init__( self.height_map = height_map else: self.height_map = None - np.random.seed(self.seed) + super().__init__(**kwargs) @@ -685,8 +685,9 @@ def get_phi(self): return torch.tensor(phi).to(self.device) def create_mask(self): - if self.is_Torch == False: + if self.is_Torch is None or self.is_Torch == False: if self.height_map is None: + np.random.seed(self.seed) self.height_map = np.random.uniform(self.height_range[0], self.height_range[1], self.resolution) assert self.height_map.shape == tuple(self.resolution) phase_mask = self.get_phi() @@ -694,6 +695,7 @@ def create_mask(self): else: if self.height_map is None: + torch.manual_seed(self.seed) height_range_tensor = torch.tensor(self.height_range) # Generate a random height map using PyTorch resolution = torch.tensor(self.resolution) @@ -703,12 +705,4 @@ def create_mask(self): assert self.height_map.shape == tuple(self.resolution) phase_mask = self.get_phi() self.mask = torch.exp(1j * phase_mask).to(self.device) - - """if self.height_map is None: - self.height_map = np.random.uniform(self.height_range[0], self.height_range[1], self.resolution) - assert self.height_map.shape == tuple(self.resolution) - phase_mask = self.get_phi() - self.mask = np.exp(1j * phase_mask) - if self.is_Torch == True: - self.mask = torch.tensor(self.mask).to(self.device)""" \ No newline at end of file diff --git a/test/test_masks.py b/test/test_masks.py index d0f42846..f58a75bc 100644 --- a/test/test_masks.py +++ b/test/test_masks.py @@ -103,8 +103,9 @@ def test_classmethod(): plt.show()''' mask5 = HeightVarying.from_sensor( - sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, is_Torch=True + sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, is_Torch=False ) + #assert mask5.is_Torch if not mask5.is_Torch: # NumPy operations assert np.all(mask5.mask.shape == resolution) @@ -117,7 +118,7 @@ def test_classmethod(): else: # PyTorch operations assert torch.equal(torch.tensor(mask5.mask.shape), torch.tensor(resolution)) - desired_psf_shape = torch.tensor(resolution + (len(mask5.psf_wavelength),)) + desired_psf_shape = torch.tensor(tuple(resolution) + (len(mask5.psf_wavelength),)) assert torch.equal(torch.tensor(mask5.psf.shape), desired_psf_shape) fig, ax = plt.subplots() im = ax.imshow(torch.angle(mask5.mask), cmap="gray") From 928240435060efca62b1c34ad513af3df228609f Mon Sep 17 00:00:00 2001 From: Ahmed Date: Mon, 4 Dec 2023 18:01:34 +0100 Subject: [PATCH 31/39] not working for now --- lensless/hardware/mask.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index f323e419..ea79b98e 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -348,7 +348,7 @@ class MultiLensArray(Mask): Multi-lens array mask. """ def __init__( - self, N = None, radius = None, loc = None, refractive_index = 1.2, design_wv=532e-9, seed = 0, min_height=1e-3, istorch=False, **kwargs + self, N = None, radius = None, loc = None, refractive_index = 1.2, design_wv=532e-9, seed = 0, min_height=1e-3, **kwargs ): """ Multi-lens array mask constructor. @@ -376,7 +376,6 @@ def __init__( self.wavelength = design_wv self.seed = seed self.min_height = min_height - self.istorch=istorch if not istorch : if self.radius is not None: @@ -388,7 +387,7 @@ def __init__( else: assert self.N is not None np.random.seed(self.seed) - self.radius = np.random.uniform(self.min_height, 1e-5, self.N) #TODO: check if it is the right way to do it + self.radius = None np.random.uniform(self.min_height, 1e-5, self.N) #TODO: check if it is the right way to do it assert self.N == len(self.radius) elif self.radius is not None: assert self.loc is not None From 6ee4be439ada1a49993a01a723ba796083b9322b Mon Sep 17 00:00:00 2001 From: Ahmed Date: Mon, 4 Dec 2023 18:09:22 +0100 Subject: [PATCH 32/39] add comments --- lensless/hardware/mask.py | 12 +++++++++--- test/test_masks.py | 4 ++-- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index c7251027..48db882c 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -443,11 +443,17 @@ def create_mask(self): radius_res = self.radius * (1/self.feature_size[0]) height = self.create_height_map(radius_res, locs_res) - phi = (height * (self.refractive_index - 1) * 2 * np.pi / self.wavelength) #% (2*np.pi)#? Makes it have some noisy values instead of a continuous sphere - + # Kaleidoscope effect + phi = (height * (self.refractive_index - 1) * 2 * np.pi / self.wavelength) % (2*np.pi)#? Makes it have some noisy values instead of a continuous sphere + # No kaleidoscope effect + #phi = (height * (self.refractive_index - 1) * 2 * np.pi / self.wavelength) + #max_phi = np.max(phi) + #phi = phi * (2 * np.pi / max_phi) + fig, ax = plt.subplots() - im = ax.imshow(phi, cmap="gray") + im = ax.imshow(height, cmap="gray") fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) + plt.title("Height map") plt.show() self.mask = np.exp(1j * phi) diff --git a/test/test_masks.py b/test/test_masks.py index f58a75bc..58e33bed 100644 --- a/test/test_masks.py +++ b/test/test_masks.py @@ -90,7 +90,7 @@ def test_classmethod(): desired_psf_shape = np.array(tuple(resolution) + (len(mask3.psf_wavelength),)) assert np.all(mask3.psf.shape == desired_psf_shape) - '''mask4 = MultiLensArray.from_sensor( + mask4 = MultiLensArray.from_sensor( sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, N=10#radius=np.array([10, 25]), loc=np.array([[10.1, 11.3], [56.5, 89.2]]) ) assert np.all(mask4.mask.shape == resolution) @@ -100,7 +100,7 @@ def test_classmethod(): fig, ax = plt.subplots() im = ax.imshow(np.angle(mask4.mask), cmap="gray") fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) - plt.show()''' + plt.show() mask5 = HeightVarying.from_sensor( sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, is_Torch=False From 3780cd1f013746abaab1711ebe82cb11c53a84e7 Mon Sep 17 00:00:00 2001 From: Ahmed Date: Tue, 5 Dec 2023 09:04:53 +0100 Subject: [PATCH 33/39] finish the torch implementation of MLA --- lensless/hardware/mask.py | 51 +++++++++++++++++---------------------- test/test_masks.py | 4 +-- 2 files changed, 24 insertions(+), 31 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index ea79b98e..0f3e0f60 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -377,46 +377,38 @@ def __init__( self.seed = seed self.min_height = min_height - if not istorch : - if self.radius is not None: - assert self.loc is not None - assert len(self.radius) == len(self.loc) - self.N = len(self.radius) - circles = np.array([(self.loc[i][0], self.loc[i][1], self.radius[i]) for i in range(self.N)]) - assert MultiLensArray.no_circle_overlap(circles, self.istorch) + super().__init__(**kwargs) + + def check_asserts(self): + if self.radius is not None: + assert np.all(self.radius > 0) + assert self.loc is not None, "Location of the lenses should be specified if their radius is specified" + assert len(self.radius) == len(self.loc), "Number of radius should be equal to the number of locations" + self.N = len(self.radius) + circles = np.array([(self.loc[i][0], self.loc[i][1], self.radius[i]) for i in range(self.N)]) if not self.is_Torch else torch.tensor([(self.loc[i][0], self.loc[i][1], self.radius[i]) for i in range(self.N)]) + assert MultiLensArray.no_circle_overlap(circles, self.is_Torch), "lenses should not overlap" + else: + assert self.N is not None, "If positions are not specified, the number of lenses should be specified" + if self.is_Torch: + torch.manual_seed(self.seed) + self.radius = torch.rand(self.N) * (1e-5 - self.min_height) + self.min_height else: - assert self.N is not None np.random.seed(self.seed) - self.radius = None np.random.uniform(self.min_height, 1e-5, self.N) #TODO: check if it is the right way to do it - assert self.N == len(self.radius) - elif self.radius is not None: - assert self.loc is not None - assert len(self.radius) == len(self.loc) - self.N = len(self.radius) - circles = torch.tensor([(self.loc[i][0], self.loc[i][1], self.radius[i]) for i in range(self.N)]) - assert MultiLensArray.no_circle_overlap(circles, self.istorch) - else: - assert self.N is not None - torch.manual_seed(self.seed) - self.radius = torch.rand(self.N) * (20 - self.min_height) + self.min_height #TODO: check if it is the right way to do it + self.radius = np.random.uniform(self.min_height, 1e-5, self.N) assert self.N == len(self.radius) - - super().__init__(**kwargs) - - @staticmethod - def no_circle_overlap(circles, istorch): + def no_circle_overlap(circles, is_Torch): """Check if any circle in the list overlaps with another.""" for i in range(len(circles)): - if MultiLensArray.does_circle_overlap(circles[i+1:], circles[i][0], circles[i][1], circles[i][2], istorch): + if MultiLensArray.does_circle_overlap(circles[i+1:], circles[i][0], circles[i][1], circles[i][2], is_Torch): return False return True @staticmethod - def does_circle_overlap(circles, x, y, r, istorch): + def does_circle_overlap(circles, x, y, r, is_Torch): """Check if a circle overlaps with any in the list.""" - if not istorch: + if not is_Torch: for (cx, cy, cr) in circles: if np.sqrt((x - cx)**2 + (y - cy)**2) < r/2 + cr/2: return True @@ -441,7 +433,7 @@ def place_spheres_on_plane(self, width, height, radius, max_attempts=1000): x = np.random.uniform(r, width - r) y = np.random.uniform(r, height - r) - if not MultiLensArray.does_circle_overlap(placed_circles, x , y , r): + if not MultiLensArray.does_circle_overlap(placed_circles, x , y , r, self.is_Torch): placed_circles.append((x, y, r)) placed_circles_res.append((x / self.feature_size[0], y / self.feature_size[1], r / self.feature_size[0])) placed = True @@ -458,6 +450,7 @@ def place_spheres_on_plane(self, width, height, radius, max_attempts=1000): return circles, radius def create_mask(self): + self.check_asserts() if self.loc is None: self.loc, self.radius = self.place_spheres_on_plane(self.size[0], self.size[1], self.radius) locs_res = self.loc * (1/self.feature_size) diff --git a/test/test_masks.py b/test/test_masks.py index 82fc2395..174e0c23 100644 --- a/test/test_masks.py +++ b/test/test_masks.py @@ -91,14 +91,14 @@ def test_classmethod(): desired_psf_shape = np.array(tuple(resolution) + (len(mask3.psf_wavelength),)) assert np.all(mask3.psf.shape == desired_psf_shape) - '''mask4 = MultiLensArray.from_sensor( + mask4 = MultiLensArray.from_sensor( sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, N=10#radius=np.array([10, 25]), loc=np.array([[10.1, 11.3], [56.5, 89.2]]) ) train1 = TrainableMask.from_mask(mask4) assert np.all(mask4.mask.shape == resolution) desired_psf_shape = np.array(tuple(resolution) + (len(mask4.psf_wavelength),)) assert np.all(mask4.psf.shape == desired_psf_shape) - + ''' fig, ax = plt.subplots() im = ax.imshow(np.angle(mask4.mask), cmap="gray") fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) From f2c3c2909cc7402da0697335d52b1856303f455b Mon Sep 17 00:00:00 2001 From: Eric Bezzam Date: Wed, 6 Dec 2023 10:38:51 +0100 Subject: [PATCH 34/39] Start interface for trainable coded aperture. --- lensless/hardware/mask.py | 1 - lensless/hardware/trainable_mask.py | 16 ++++++++++++++++ 2 files changed, 16 insertions(+), 1 deletion(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index f9597bf5..f6a5d797 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -94,7 +94,6 @@ def __init__( assert np.all(feature_size > 0), "Feature size should be positive" assert np.all(resolution * feature_size <= size) - self.phase_mask = None self.resolution = resolution self.size = size if feature_size is None: diff --git a/lensless/hardware/trainable_mask.py b/lensless/hardware/trainable_mask.py index f0d258ba..8c15353a 100644 --- a/lensless/hardware/trainable_mask.py +++ b/lensless/hardware/trainable_mask.py @@ -12,6 +12,7 @@ from lensless.hardware.slm import get_programmable_mask, get_intensity_psf from lensless.hardware.sensor import VirtualSensor from waveprop.devices import slm_dict +from lensless.hardware.mask import CodedAperture class TrainableMask(torch.nn.Module, metaclass=abc.ABCMeta): @@ -230,3 +231,18 @@ def project(self): self.color_filter.data = self.color_filter / self.color_filter.sum( dim=[1, 2] ).unsqueeze(-1).unsqueeze(-1) + + +class TrainableCodedAperture(CodedAperture): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + self.row = torch.nn.Parameter(self.row) + self.col = torch.nn.Parameter(self.col) + + def get_psf(self): + return super().compute_psf() + + def project(self): + self.row.data = torch.clamp(self.row, 0, 1) + self.col.data = torch.clamp(self.col, 0, 1) From cd76a3cfeb0e4091d162ab8d1114ea519d9fa547 Mon Sep 17 00:00:00 2001 From: Eric Bezzam Date: Wed, 6 Dec 2023 15:49:32 +0100 Subject: [PATCH 35/39] Update trainable mask interface. --- configs/train_coded_aperture.yaml | 38 ++++++++++ configs/train_unrolledADMM.yaml | 1 + lensless/hardware/mask.py | 80 ++++++++++++++----- lensless/hardware/sensor.py | 1 + lensless/hardware/trainable_mask.py | 106 ++++++++++++++++++-------- scripts/recon/train_unrolled.py | 114 +++++++++++++++++----------- 6 files changed, 240 insertions(+), 100 deletions(-) create mode 100644 configs/train_coded_aperture.yaml diff --git a/configs/train_coded_aperture.yaml b/configs/train_coded_aperture.yaml new file mode 100644 index 00000000..4219910d --- /dev/null +++ b/configs/train_coded_aperture.yaml @@ -0,0 +1,38 @@ +# python scripts/recon/train_unrolled.py -cn train_coded_aperture +defaults: + - train_unrolledADMM + - _self_ + +# Train Dataset +files: + dataset: mnist # Simulated : "mnist", "fashion_mnist", "cifar10", "CelebA". Measure :"DiffuserCam" + celeba_root: /scratch/bezzam + downsample: 8 + +#Trainable Mask +trainable_mask: + mask_type: TrainableCodedAperture + optimizer: Adam + mask_lr: 1e-3 + initial_value: + method: MLS + n_bits: 8 + # MURA not working... + # method: MURA + # n_bits: 3 + +simulation: + grayscale: False + flip: False + scene2mask: 40e-2 + mask2sensor: 2e-3 + sensor: "rpi_hq" + downsample: 16 + object_height: 0.30 + +training: + crop_preloss: False # crop region for computing loss + batch_size: 8 + epoch: 25 + eval_batch_size: 16 + save_every: 5 diff --git a/configs/train_unrolledADMM.yaml b/configs/train_unrolledADMM.yaml index f7602f01..16c6040d 100644 --- a/configs/train_unrolledADMM.yaml +++ b/configs/train_unrolledADMM.yaml @@ -84,6 +84,7 @@ trainable_mask: initial_value: psf grayscale: False mask_lr: 1e-3 + optimizer: Adam L1_strength: 1.0 #False or float target: "object_plane" # "original" or "object_plane" or "label" diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index f6a5d797..4ffa7b36 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -53,6 +53,8 @@ def __init__( size=None, feature_size=None, psf_wavelength=[460e-9, 550e-9, 640e-9], + is_torch=False, + torch_device="cpu", **kwargs ): """ @@ -95,6 +97,7 @@ def __init__( assert np.all(resolution * feature_size <= size) self.resolution = resolution + self.resolution = (int(self.resolution[0]), int(self.resolution[1])) self.size = size if feature_size is None: self.feature_size = self.size / self.resolution @@ -102,6 +105,9 @@ def __init__( self.feature_size = feature_size self.distance_sensor = distance_sensor + self.is_torch = is_torch + self.torch_device = torch_device + # create mask self.mask = None self.create_mask() @@ -155,19 +161,28 @@ def compute_psf(self): Compute the intensity PSF with bandlimited angular spectrum (BLAS) for each wavelength. Common to all types of masks. """ - psf = np.zeros(tuple(self.resolution) + (len(self.psf_wavelength),), dtype=np.complex64) + if self.is_torch: + psf = torch.zeros( + tuple(self.resolution) + (len(self.psf_wavelength),), dtype=torch.complex64 + ) + else: + psf = np.zeros(tuple(self.resolution) + (len(self.psf_wavelength),), dtype=np.complex64) for i, wv in enumerate(self.psf_wavelength): psf[:, :, i] = angular_spectrum( u_in=self.mask, wv=wv, d1=self.feature_size, dz=self.distance_sensor, - dtype=np.float32, + dtype=np.float32 if not self.is_torch else torch.float32, bandlimit=True, + device=self.torch_device if self.is_torch else None, )[0] # intensity PSF - self.psf = np.abs(psf) ** 2 + if self.is_torch: + self.psf = torch.abs(psf) ** 2 + else: + self.psf = np.abs(psf) ** 2 class CodedAperture(Mask): @@ -196,33 +211,55 @@ def __init__(self, method="MLS", n_bits=8, **kwargs): self.method = method self.n_bits = n_bits + assert self.method.upper() in ["MURA", "MLS"], "Method should be either 'MLS' or 'MURA'" + # TODO? use: https://github.com/bpops/codedapertures + + # initialize parameters + if self.method.upper() == "MURA": + mask = self.squarepattern(4 * self.n_bits + 1)[1:, 1:] + self.row = self.mask[0, :] + self.col = self.mask[:, 0] + outer = np.outer(self.row, self.col) + assert np.all(outer == mask) + else: + seq = max_len_seq(self.n_bits)[0] + self.row = seq + self.col = seq + + if kwargs["is_torch"]: + self.row = torch.from_numpy(self.row).float() + self.col = torch.from_numpy(self.col).float() + super().__init__(**kwargs) def create_mask(self): """ - Creating coded aperture mask using either the MURA of MLS method. + Creating coded aperture mask. """ - assert self.method.upper() in ["MURA", "MLS"], "Method should be either 'MLS' or 'MURA'" - # Generating pattern - if self.method.upper() == "MURA": - self.mask = self.squarepattern(4 * self.n_bits + 1)[1:, 1:] - self.row = 2 * self.mask[0, :] - 1 - self.col = 2 * self.mask[:, 0] - 1 + # outer product + if self.is_torch: + self.mask = torch.outer(self.row, self.col) else: - seq = max_len_seq(self.n_bits)[0] * 2 - 1 - h_r = np.r_[seq, seq] - self.row = h_r - self.col = h_r - self.mask = (np.outer(h_r, h_r) + 1) / 2 + self.mask = np.outer(self.row, self.col) - # Upscaling + # resize to sensor shape if np.any(self.resolution != self.mask.shape): - upscaled_mask = resize( - self.mask[:, :, np.newaxis], shape=tuple(self.resolution) + (1,) - ).squeeze() - upscaled_mask = np.clip(upscaled_mask, 0, 1) - self.mask = np.round(upscaled_mask).astype(int) + + if self.is_torch: + self.mask = self.mask.unsqueeze(0).unsqueeze(0) + self.mask = torch.nn.functional.interpolate( + self.mask, size=tuple(self.resolution), mode="nearest" + ).squeeze() + else: + # self.mask = resize(self.mask[:, :, np.newaxis], shape=tuple(self.resolution) + (1,)) + self.mask = resize( + self.mask[:, :, np.newaxis], + shape=tuple(self.resolution) + (1,), + interpolation=cv.INTER_NEAREST, + ).squeeze() + + # assert np.all(np.unique(self.mask) == np.array([0, 1])) def is_prime(self, n): """ @@ -246,6 +283,7 @@ def squarepattern(self, p): p: int Number of bits. """ + if not self.is_prime(p): raise ValueError("p is not a valid length. It must be prime.") A = np.zeros((p, p), dtype=int) diff --git a/lensless/hardware/sensor.py b/lensless/hardware/sensor.py index 0785204e..08d8fa46 100644 --- a/lensless/hardware/sensor.py +++ b/lensless/hardware/sensor.py @@ -213,6 +213,7 @@ def from_name(cls, name, downsample=None): Sensor. """ + if name not in SensorOptions.values(): raise ValueError(f"Sensor {name} not supported.") sensor_specs = sensor_dict[name].copy() diff --git a/lensless/hardware/trainable_mask.py b/lensless/hardware/trainable_mask.py index 8c15353a..fd6c28cd 100644 --- a/lensless/hardware/trainable_mask.py +++ b/lensless/hardware/trainable_mask.py @@ -26,24 +26,28 @@ class TrainableMask(torch.nn.Module, metaclass=abc.ABCMeta): """ - def __init__(self, initial_mask, optimizer="Adam", lr=1e-3, **kwargs): + def __init__(self, optimizer="Adam", lr=1e-3, **kwargs): """ Base constructor. Derived constructor may define new state variables Parameters ---------- - initial_mask : :py:class:`~torch.Tensor` - Initial mask parameters. optimizer : str, optional Optimizer to use for updating the mask parameters, by default "Adam" lr : float, optional Learning rate for the mask parameters, by default 1e-3 """ super().__init__() - self._mask = torch.nn.Parameter(initial_mask) - self._optimizer = getattr(torch.optim, optimizer)([self._mask], lr=lr) - self.train_mask_vals = True - self._counter = 0 + # self._param = [torch.nn.Parameter(p, requires_grad=True) for p in initial_param] + # # self._param = initial_param + # self._optimizer = getattr(torch.optim, optimizer)(self._param, lr=lr) + # self._counter = 0 + self.optimizer = optimizer + self.lr = lr + + def _set_optimizer(self, param): + """Set the optimizer for the mask parameters.""" + self._optimizer = getattr(torch.optim, self.optimizer)(param, lr=self.lr) @abc.abstractmethod def get_psf(self): @@ -66,7 +70,7 @@ def update_mask(self): def get_vals(self): """Get the mask parameters.""" - return self._mask + return self._param @abc.abstractmethod def project(self): @@ -85,30 +89,38 @@ class TrainablePSF(TrainableMask): Otherwise PSF will be returned as RGB. By default False. """ - def __init__(self, initial_mask, optimizer="Adam", lr=1e-3, grayscale=False, **kwargs): - super().__init__(initial_mask, optimizer, lr, **kwargs) - assert ( - len(initial_mask.shape) == 4 - ), "Mask must be of shape (depth, height, width, channels)" + def __init__(self, initial_psf, optimizer="Adam", lr=1e-3, grayscale=False, **kwargs): + + super().__init__(optimizer, lr, **kwargs) + + # cast as learnable parameters + self._psf = torch.nn.Parameter(initial_psf) + + # set optimizer + initial_param = [self._psf] + self._set_optimizer(initial_param) + + # checks + assert len(initial_psf.shape) == 4, "Mask must be of shape (depth, height, width, channels)" self.grayscale = grayscale - self._is_grayscale = is_grayscale(initial_mask) + self._is_grayscale = is_grayscale(initial_psf) if grayscale: - assert self._is_grayscale, "Mask must be grayscale" + assert self._is_grayscale, "PSF must be grayscale" def get_psf(self): if self._is_grayscale: if self.grayscale: # simulation in grayscale - return self._mask + return self._psf else: # replicate to 3 channels - return self._mask.expand(-1, -1, -1, 3) + return self._psf.expand(-1, -1, -1, 3) else: # assume RGB - return self._mask + return self._psf def project(self): - self._mask.data = torch.clamp(self._mask, 0, 1) + self._psf.data = torch.clamp(self._psf, 0, 1) class AdafruitLCD(TrainableMask): @@ -147,23 +159,27 @@ def __init__( Whether to flip the mask vertically, by default False """ - super().__init__(initial_vals, **kwargs) + super().__init__(optimizer, lr, **kwargs) self.train_mask_vals = train_mask_vals + if train_mask_vals: + self._mask = torch.nn.Parameter(initial_vals) + else: + self._mask = initial_vals + if color_filter is not None: self.color_filter = torch.nn.Parameter(color_filter) if train_mask_vals: - param = [self._mask, self.color_filter] + initial_param = [self._mask, self.color_filter] else: - del self._mask - self._mask = initial_vals - param = [self.color_filter] - self._optimizer = getattr(torch.optim, optimizer)(param, lr=lr) + initial_param = [self.color_filter] else: - self.color_filter = None assert ( train_mask_vals ), "If color filter is not trainable, mask values must be trainable" + # set optimizer + self._set_optimizer(initial_param) + self.slm_param = slm_dict[slm] self.device = slm self.sensor = VirtualSensor.from_name(sensor, downsample=downsample) @@ -233,16 +249,40 @@ def project(self): ).unsqueeze(-1).unsqueeze(-1) -class TrainableCodedAperture(CodedAperture): - def __init__(self, **kwargs): - super().__init__(**kwargs) +class TrainableCodedAperture(TrainableMask): + def __init__( + self, sensor_name, downsample=None, binary=True, optimizer="Adam", lr=1e-3, **kwargs + ): + """ + TODO: Distinguish between separable and non-separable. + """ + + super().__init__(optimizer, lr, **kwargs) + + assert "distance_sensor" in kwargs, "Distance to sensor must be specified" + assert "method" in kwargs, "Method must be specified." + assert "n_bits" in kwargs, "Number of bits must be specified." + + # initialize mask + self._mask = CodedAperture.from_sensor(sensor_name, downsample, is_torch=True, **kwargs) + + # set learnable parameters (should be immediate attributes of the class) + self._row = torch.nn.Parameter(self._mask.row) + self._col = torch.nn.Parameter(self._mask.col) + initial_param = [self._row, self._col] + self.binary = binary - self.row = torch.nn.Parameter(self.row) - self.col = torch.nn.Parameter(self.col) + # set optimizer + self._set_optimizer(initial_param) def get_psf(self): - return super().compute_psf() + self._mask.create_mask() + self._mask.compute_psf() + return self._mask.psf.unsqueeze(0) def project(self): self.row.data = torch.clamp(self.row, 0, 1) - self.col.data = torch.clamp(self.col, 0, 1) + self.col = torch.clamp(self.col, 0, 1) + if self.binary: + self.row.data = torch.round(self.row) + self.col.data = torch.round(self.col) diff --git a/scripts/recon/train_unrolled.py b/scripts/recon/train_unrolled.py index eaace9a8..8a326a7a 100644 --- a/scripts/recon/train_unrolled.py +++ b/scripts/recon/train_unrolled.py @@ -33,6 +33,7 @@ """ import logging +import omegaconf import hydra from hydra.utils import get_original_cwd import os @@ -271,56 +272,77 @@ def prep_trainable_mask(config, psf=None, downsample=None): if config.trainable_mask.mask_type is not None: mask_class = getattr(lensless.hardware.trainable_mask, config.trainable_mask.mask_type) - if config.trainable_mask.initial_value == "random": - if psf is not None: - initial_mask = torch.rand_like(psf) - else: - sensor = VirtualSensor.from_name(config.simulation.sensor, downsample=downsample) - resolution = sensor.resolution - initial_mask = torch.rand((1, *resolution, 3)) - elif config.trainable_mask.initial_value == "psf": - initial_mask = psf.clone() - # if file ending with "npy" - elif config.trainable_mask.initial_value.endswith("npy"): - pattern = np.load(os.path.join(get_original_cwd(), config.trainable_mask.initial_value)) - - initial_mask = full2subpattern( - pattern=pattern, - shape=config.trainable_mask.ap_shape, - center=config.trainable_mask.ap_center, - slm=config.trainable_mask.slm, + if isinstance(config.trainable_mask.initial_value, omegaconf.dictconfig.DictConfig): + + from lensless.hardware.trainable_mask import TrainableCodedAperture + + # from mask config + mask = mask_class( + # mask = TrainableCodedAperture( + sensor_name=config.simulation.sensor, + downsample=downsample, + distance_sensor=config.simulation.mask2sensor, + optimizer=config.trainable_mask.optimizer, + lr=config.trainable_mask.mask_lr, + **config.trainable_mask.initial_value, ) - initial_mask = torch.from_numpy(initial_mask.astype(np.float32)) - - # prepare color filter if needed - from waveprop.devices import slm_dict - from waveprop.devices import SLMParam as SLMParam_wp - - slm_param = slm_dict[config.trainable_mask.slm] - if ( - config.trainable_mask.train_color_filter - and SLMParam_wp.COLOR_FILTER in slm_param.keys() - ): - color_filter = slm_param[SLMParam_wp.COLOR_FILTER] - color_filter = torch.from_numpy(color_filter.copy()).to(dtype=torch.float32) - - # add small random values - color_filter = color_filter + 0.1 * torch.rand_like(color_filter) + else: - raise ValueError( - f"Initial PSF value {config.trainable_mask.initial_value} not supported" - ) - if config.trainable_mask.grayscale and not is_grayscale(initial_mask): - initial_mask = rgb2gray(initial_mask) + if config.trainable_mask.initial_value == "random": + if psf is not None: + initial_mask = torch.rand_like(psf) + else: + sensor = VirtualSensor.from_name( + config.simulation.sensor, downsample=downsample + ) + resolution = sensor.resolution + initial_mask = torch.rand((1, *resolution, 3)) + elif config.trainable_mask.initial_value == "psf": + initial_mask = psf.clone() + # if file ending with "npy" + elif config.trainable_mask.initial_value.endswith("npy"): + pattern = np.load( + os.path.join(get_original_cwd(), config.trainable_mask.initial_value) + ) + + initial_mask = full2subpattern( + pattern=pattern, + shape=config.trainable_mask.ap_shape, + center=config.trainable_mask.ap_center, + slm=config.trainable_mask.slm, + ) + initial_mask = torch.from_numpy(initial_mask.astype(np.float32)) + + # prepare color filter if needed + from waveprop.devices import slm_dict + from waveprop.devices import SLMParam as SLMParam_wp + + slm_param = slm_dict[config.trainable_mask.slm] + if ( + config.trainable_mask.train_color_filter + and SLMParam_wp.COLOR_FILTER in slm_param.keys() + ): + color_filter = slm_param[SLMParam_wp.COLOR_FILTER] + color_filter = torch.from_numpy(color_filter.copy()).to(dtype=torch.float32) + + # add small random values + color_filter = color_filter + 0.1 * torch.rand_like(color_filter) - mask = mask_class( - initial_mask, - optimizer="Adam", - downsample=downsample, - color_filter=color_filter, - **config.trainable_mask, - ) + else: + raise ValueError( + f"Initial PSF value {config.trainable_mask.initial_value} not supported" + ) + + if config.trainable_mask.grayscale and not is_grayscale(initial_mask): + initial_mask = rgb2gray(initial_mask) + + mask = mask_class( + initial_mask, + downsample=downsample, + color_filter=color_filter, + **config.trainable_mask, + ) return mask From 21f0d0876f0cc46cd0be1f8cb7222b9f48b5b0c0 Mon Sep 17 00:00:00 2001 From: Ahmed Date: Wed, 6 Dec 2023 16:24:40 +0100 Subject: [PATCH 36/39] beginning of multilens in trainable --- lensless/hardware/mask.py | 64 +++++++++++++---------------- lensless/hardware/trainable_mask.py | 40 +++++++++++++++++- test/test_masks.py | 32 ++++++++------- 3 files changed, 85 insertions(+), 51 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index a4ee00b0..dcd7e56a 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -119,7 +119,7 @@ def __init__( self.compute_psf() @classmethod - def from_sensor(cls, sensor_name, downsample=None, istorch=False,**kwargs): + def from_sensor(cls, sensor_name, downsample=None,**kwargs): """ Constructor from an existing virtual sensor that copies over the sensor parameters (sensor resolution, sensor size, feature size). @@ -348,7 +348,7 @@ class MultiLensArray(Mask): Multi-lens array mask. """ def __init__( - self, N = None, radius = None, loc = None, refractive_index = 1.2, design_wv=532e-9, seed = 0, min_height=1e-3, **kwargs + self, N = None, radius = None, loc = None, refractive_index = 1.2, design_wv=532e-9, seed = 0, min_height=1e-5, **kwargs ): """ Multi-lens array mask constructor. @@ -386,56 +386,51 @@ def check_asserts(self): assert len(self.radius) == len(self.loc), "Number of radius should be equal to the number of locations" self.N = len(self.radius) circles = np.array([(self.loc[i][0], self.loc[i][1], self.radius[i]) for i in range(self.N)]) if not self.is_Torch else torch.tensor([(self.loc[i][0], self.loc[i][1], self.radius[i]) for i in range(self.N)]) - assert MultiLensArray.no_circle_overlap(circles, self.is_Torch), "lenses should not overlap" + assert self.no_circle_overlap(circles), "lenses should not overlap" else: assert self.N is not None, "If positions are not specified, the number of lenses should be specified" if self.is_Torch: torch.manual_seed(self.seed) - self.radius = torch.rand(self.N) * (1e-5 - self.min_height) + self.min_height + self.radius = torch.rand(self.N) * (1e-3 - self.min_height) + self.min_height else: np.random.seed(self.seed) - self.radius = np.random.uniform(self.min_height, 1e-5, self.N) + self.radius = np.random.uniform(self.min_height, 1e-3, self.N) assert self.N == len(self.radius) - @staticmethod - def no_circle_overlap(circles, is_Torch): + def no_circle_overlap(self, circles): """Check if any circle in the list overlaps with another.""" for i in range(len(circles)): - if MultiLensArray.does_circle_overlap(circles[i+1:], circles[i][0], circles[i][1], circles[i][2], is_Torch): + if self.does_circle_overlap(circles[i+1:], circles[i][0], circles[i][1], circles[i][2]): return False return True - @staticmethod - def does_circle_overlap(circles, x, y, r, is_Torch): + def does_circle_overlap(self, circles, x, y, r): """Check if a circle overlaps with any in the list.""" - if not is_Torch: + if not self.is_Torch: for (cx, cy, cr) in circles: - if np.sqrt((x - cx)**2 + (y - cy)**2) < r/2 + cr/2: - return True + if np.sqrt((x - cx)**2 + (y - cy)**2) <= r + cr: + return True, (cx, cy, cr) return False else: - for circle in circles: - cx, cy, cr = circle.tolist() # Convert the PyTorch tensor to a list - if torch.sqrt((x - cx)**2 + (y - cy)**2) < r/2 + cr/2: - return True - return False + for (cx, cy, cr) in circles: + if torch.sqrt((x - cx)**2 + (y - cy)**2) <= r + cr: + return True, (cx, cy, cr) + return False def place_spheres_on_plane(self, width, height, radius, max_attempts=1000): """Try to place circles on a 2D plane.""" placed_circles = [] - placed_circles_res = [] radius_sorted = sorted(radius, reverse=True) # Place larger circles first for r in radius_sorted: placed = False for _ in range(max_attempts): - x = np.random.uniform(r, width - r) - y = np.random.uniform(r, height - r) + x = np.random.uniform(r, width - r) if self.is_Torch == False else torch.rand(1) * (width - 2*r) + r + y = np.random.uniform(r, height - r) if self.is_Torch == False else torch.rand(1) * (height - 2*r) + r - if not MultiLensArray.does_circle_overlap(placed_circles, x , y , r, self.is_Torch): + if not self.does_circle_overlap(placed_circles, x , y , r): placed_circles.append((x, y, r)) - placed_circles_res.append((x / self.feature_size[0], y / self.feature_size[1], r / self.feature_size[0])) placed = True print(f"Placed circle with rad {r}, and center ({x}, {y})") break @@ -444,7 +439,8 @@ def place_spheres_on_plane(self, width, height, radius, max_attempts=1000): print(f"Failed to place circle with rad {r}") continue - placed_circles = np.array(placed_circles) + placed_circles = np.array(placed_circles) if not self.is_Torch else torch.tensor(placed_circles) + circles = placed_circles[:, :2] radius = placed_circles[:, 2] return circles, radius @@ -453,37 +449,33 @@ def create_mask(self): self.check_asserts() if self.loc is None: self.loc, self.radius = self.place_spheres_on_plane(self.size[0], self.size[1], self.radius) - locs_res = self.loc * (1/self.feature_size) + locs_res = self.loc * (1/self.feature_size[0]) radius_res = self.radius * (1/self.feature_size[0]) height = self.create_height_map(radius_res, locs_res) - # Kaleidoscope effect - phi = (height * (self.refractive_index - 1) * 2 * np.pi / self.wavelength) % (2*np.pi)#? Makes it have some noisy values instead of a continuous sphere - # No kaleidoscope effect - #phi = (height * (self.refractive_index - 1) * 2 * np.pi / self.wavelength) - #max_phi = np.max(phi) - #phi = phi * (2 * np.pi / max_phi) + self.phi = (height * (self.refractive_index - 1) * 2 * np.pi / self.wavelength) + fig, ax = plt.subplots() - im = ax.imshow(height, cmap="gray") + im = ax.imshow(height.cpu().detach().numpy() if self.is_Torch else height, cmap="gray") fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) plt.title("Height map") plt.show() - self.mask = np.exp(1j * phi) + self.mask = np.exp(1j * self.phi) if not self.is_Torch else torch.exp(1j * self.phi) def create_height_map(self, radius, locs): - height = np.full((self.resolution[0], self.resolution[1]), self.min_height) + height = np.full((self.resolution[0], self.resolution[1]), self.min_height) if not self.is_Torch else torch.full((self.resolution[0], self.resolution[1]), self.min_height) for x in range(height.shape[0]): for y in range(height.shape[1]): height[x, y] += self.lens_contribution(radius, locs, (x + 0.5), (y + 0.5)) * self.feature_size[0] - assert np.all(height >= self.min_height) + assert np.all(height >= self.min_height) if not self.is_Torch else torch.all(torch.ge(height, self.min_height)) return height def lens_contribution(self, radius, locs, x, y): contribution = 0 for idx, loc in enumerate(locs): if (x-loc[0])**2 + (y-loc[1])**2 < radius[idx]**2: - contribution = np.sqrt((radius[idx])**2 - ((x-loc[0]))**2 - ((y -loc[1]))**2) + contribution = np.sqrt((radius[idx])**2 - ((x-loc[0]))**2 - ((y -loc[1]))**2) if not self.is_Torch else torch.sqrt((radius[idx])**2 - ((x-loc[0]))**2 - ((y -loc[1]))**2) return contribution return contribution diff --git a/lensless/hardware/trainable_mask.py b/lensless/hardware/trainable_mask.py index e1ed68b7..0317d5b4 100644 --- a/lensless/hardware/trainable_mask.py +++ b/lensless/hardware/trainable_mask.py @@ -74,9 +74,47 @@ def project(self): @classmethod def from_mask(cls, mask, **kwargs): - return cls(initial_mask=mask) + return cls(initial_mask=mask, **kwargs) +class TrainableMultiLensArray(TrainableMask): + + def __init__(self, initial_mask, optimizer="Adam", lr=1e-3, **kwargs): + super().__init__(initial_mask, optimizer, lr, **kwargs) + self._loc = torch.nn.Parameter(self._mask.loc) + self._radius = torch.nn.Parameter(self._mask.radius) + + + def get_psf(self): + self._mask.compute_psf() + return self._mask.psf + + def project(self): + # clamp back the radiuses + torch.clamp(self._radius, 0, self._mask.size[0] / 2) + + # sort in descending order + self._radius, idx = torch.sort(self._radius, descending=True) + self._loc = self._loc[idx] + + circles = torch.cat((self._loc, self._radius.unsqueeze(-1)), dim=-1) + for idx, r in enumerate(self._radius): + # clamp back the locations + torch.clamp(self._loc[idx, 0], r, self._mask.size[0] - r) + torch.clamp(self._loc[idx, 1], r, self._mask.size[1] - r) + + # check for overlapping + for (cx, cy, cr) in circles[idx+1:]: + dist = torch.sqrt((self._loc[idx, 0] - cx)**2 + (self._loc[idx, 1] - cy)**2) + if dist <= r + cr: + self._radius[idx] = dist - cr + if self._radius[idx] < 0: + self._radius[idx] = 0 + break + + + + class TrainablePSF(TrainableMask): """ diff --git a/test/test_masks.py b/test/test_masks.py index 39f5b74d..175b3c8c 100644 --- a/test/test_masks.py +++ b/test/test_masks.py @@ -92,18 +92,27 @@ def test_classmethod(): assert np.all(mask3.psf.shape == desired_psf_shape) mask4 = MultiLensArray.from_sensor( - sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, N=10#radius=np.array([10, 25]), loc=np.array([[10.1, 11.3], [56.5, 89.2]]) + sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, N=10, is_Torch=False#radius=np.array([10, 25]), loc=np.array([[10.1, 11.3], [56.5, 89.2]]) ) - train1 = TrainableMask.from_mask(mask4) - assert np.all(mask4.mask.shape == resolution) - desired_psf_shape = np.array(tuple(resolution) + (len(mask4.psf_wavelength),)) - assert np.all(mask4.psf.shape == desired_psf_shape) - ''' + train1 = TrainableMask.from_mask(mask4) # TODO: see why this is not working + mask4 = train1.get_vals() + phase = None + if not mask4.is_Torch: + assert np.all(mask4.mask.shape == resolution) + desired_psf_shape = np.array(tuple(resolution) + (len(mask4.psf_wavelength),)) + assert np.all(mask4.psf.shape == desired_psf_shape) + phase = mask4.phi + else: + # PyTorch operations + assert torch.equal(torch.tensor(mask4.mask.shape), torch.tensor(resolution)) + desired_psf_shape = torch.tensor(tuple(resolution) + (len(mask4.psf_wavelength),)) + assert torch.equal(torch.tensor(mask4.psf.shape), desired_psf_shape) + angle=torch.angle(mask4.mask).cpu().detach().numpy() fig, ax = plt.subplots() - im = ax.imshow(np.angle(mask4.mask), cmap="gray") + im = ax.imshow(phase, cmap="gray") fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) plt.show() - + ''' mask5 = HeightVarying.from_sensor( sensor_name="rpi_hq", downsample=downsample, distance_sensor=dz, is_Torch=False ) @@ -126,12 +135,7 @@ def test_classmethod(): im = ax.imshow(torch.angle(mask5.mask), cmap="gray") fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) plt.show() - - - - - - + ''' if __name__ == "__main__": From 73bb22926f2e1657d13c968f7323c486050b959a Mon Sep 17 00:00:00 2001 From: Eric Bezzam Date: Wed, 6 Dec 2023 16:49:46 +0100 Subject: [PATCH 37/39] Improve trainable mask API. --- configs/train_coded_aperture.yaml | 7 ++-- lensless/hardware/trainable_mask.py | 62 ++++++++++++++--------------- lensless/recon/utils.py | 33 +++++++-------- scripts/recon/train_unrolled.py | 9 ++++- 4 files changed, 58 insertions(+), 53 deletions(-) diff --git a/configs/train_coded_aperture.yaml b/configs/train_coded_aperture.yaml index 4219910d..5d8aa6d4 100644 --- a/configs/train_coded_aperture.yaml +++ b/configs/train_coded_aperture.yaml @@ -7,13 +7,14 @@ defaults: files: dataset: mnist # Simulated : "mnist", "fashion_mnist", "cifar10", "CelebA". Measure :"DiffuserCam" celeba_root: /scratch/bezzam - downsample: 8 + downsample: 16 # TODO use simulation instead? #Trainable Mask trainable_mask: mask_type: TrainableCodedAperture optimizer: Adam mask_lr: 1e-3 + L1_strength: False initial_value: method: MLS n_bits: 8 @@ -32,7 +33,7 @@ simulation: training: crop_preloss: False # crop region for computing loss - batch_size: 8 + batch_size: 4 epoch: 25 eval_batch_size: 16 - save_every: 5 + save_every: 1 diff --git a/lensless/hardware/trainable_mask.py b/lensless/hardware/trainable_mask.py index fd6c28cd..b4328f45 100644 --- a/lensless/hardware/trainable_mask.py +++ b/lensless/hardware/trainable_mask.py @@ -42,12 +42,13 @@ def __init__(self, optimizer="Adam", lr=1e-3, **kwargs): # # self._param = initial_param # self._optimizer = getattr(torch.optim, optimizer)(self._param, lr=lr) # self._counter = 0 - self.optimizer = optimizer - self.lr = lr + self._optimizer = optimizer + self._lr = lr + self._counter = 0 def _set_optimizer(self, param): """Set the optimizer for the mask parameters.""" - self._optimizer = getattr(torch.optim, self.optimizer)(param, lr=self.lr) + self._optimizer = getattr(torch.optim, self._optimizer)(param, lr=self._lr) @abc.abstractmethod def get_psf(self): @@ -68,10 +69,6 @@ def update_mask(self): self.project() self._counter += 1 - def get_vals(self): - """Get the mask parameters.""" - return self._param - @abc.abstractmethod def project(self): """Abstract method for projecting the mask parameters to a valid space (should be a subspace of [0,1]).""" @@ -162,16 +159,16 @@ def __init__( super().__init__(optimizer, lr, **kwargs) self.train_mask_vals = train_mask_vals if train_mask_vals: - self._mask = torch.nn.Parameter(initial_vals) + self._vals = torch.nn.Parameter(initial_vals) else: - self._mask = initial_vals + self._vals = initial_vals if color_filter is not None: - self.color_filter = torch.nn.Parameter(color_filter) + self._color_filter = torch.nn.Parameter(color_filter) if train_mask_vals: - initial_param = [self._mask, self.color_filter] + initial_param = [self._vals, self._color_filter] else: - initial_param = [self.color_filter] + initial_param = [self._color_filter] else: assert ( train_mask_vals @@ -202,12 +199,12 @@ def __init__( def get_psf(self): mask = get_programmable_mask( - vals=self._mask, + vals=self._vals, sensor=self.sensor, slm_param=self.slm_param, rotate=self.rotate, flipud=self.flipud, - color_filter=self.color_filter, + color_filter=self._color_filter, ) if self.vertical_shift is not None: @@ -240,11 +237,11 @@ def get_psf(self): def project(self): if self.train_mask_vals: - self._mask.data = torch.clamp(self._mask, self.min_val, 1) - if self.color_filter is not None: - self.color_filter.data = torch.clamp(self.color_filter, 0, 1) + self._vals.data = torch.clamp(self._vals, self.min_val, 1) + if self._color_filter is not None: + self._color_filter.data = torch.clamp(self._color_filter, 0, 1) # normalize each row to 1 - self.color_filter.data = self.color_filter / self.color_filter.sum( + self._color_filter.data = self._color_filter / self._color_filter.sum( dim=[1, 2] ).unsqueeze(-1).unsqueeze(-1) @@ -257,32 +254,33 @@ def __init__( TODO: Distinguish between separable and non-separable. """ + # 1) call base constructor so parameters can be set super().__init__(optimizer, lr, **kwargs) + # 2) initialize mask assert "distance_sensor" in kwargs, "Distance to sensor must be specified" assert "method" in kwargs, "Method must be specified." assert "n_bits" in kwargs, "Number of bits must be specified." + self._mask_obj = CodedAperture.from_sensor(sensor_name, downsample, is_torch=True, **kwargs) + self._mask = self._mask_obj.mask - # initialize mask - self._mask = CodedAperture.from_sensor(sensor_name, downsample, is_torch=True, **kwargs) - - # set learnable parameters (should be immediate attributes of the class) - self._row = torch.nn.Parameter(self._mask.row) - self._col = torch.nn.Parameter(self._mask.col) + # 3) set learnable parameters (should be immediate attributes of the class) + self._row = torch.nn.Parameter(self._mask_obj.row) + self._col = torch.nn.Parameter(self._mask_obj.col) initial_param = [self._row, self._col] self.binary = binary - # set optimizer + # 4) set optimizer self._set_optimizer(initial_param) def get_psf(self): - self._mask.create_mask() - self._mask.compute_psf() - return self._mask.psf.unsqueeze(0) + self._mask_obj.create_mask() + self._mask_obj.compute_psf() + return self._mask_obj.psf.unsqueeze(0) def project(self): - self.row.data = torch.clamp(self.row, 0, 1) - self.col = torch.clamp(self.col, 0, 1) + self._row.data = torch.clamp(self._row, 0, 1) + self._col.data = torch.clamp(self._col, 0, 1) if self.binary: - self.row.data = torch.round(self.row) - self.col.data = torch.round(self.col) + self._row.data = torch.round(self._row) + self._col.data = torch.round(self._col) diff --git a/lensless/recon/utils.py b/lensless/recon/utils.py index 53f23a1b..f249f2b1 100644 --- a/lensless/recon/utils.py +++ b/lensless/recon/utils.py @@ -579,7 +579,9 @@ def train_epoch(self, data_loader): self.Loss_lpips(2 * y_pred - 1, 2 * y - 1) ) if self.use_mask and self.l1_mask: - loss_v = loss_v + self.l1_mask * torch.mean(torch.abs(self.mask._mask)) + for p in self.mask.parameters(): + if p.requires_grad: + loss_v = loss_v + self.l1_mask * torch.mean(torch.abs(p)) loss_v.backward() if self.clip_grad_norm is not None: @@ -659,7 +661,10 @@ def evaluate(self, mean_loss, save_pt, epoch, disp=None): if self.lpips is not None: eval_loss += self.lpips * current_metrics["LPIPS_Vgg"] if self.use_mask and self.l1_mask: - eval_loss += self.l1_mask * np.mean(np.abs(self.mask._mask.cpu().detach().numpy())) + for p in self.mask.parameters(): + if p.requires_grad: + eval_loss += self.l1_mask * np.mean(np.abs(p.cpu().detach().numpy())) + # eval_loss += self.l1_mask * np.mean(np.abs(self.mask._mask.cpu().detach().numpy())) return eval_loss else: return current_metrics[self.metrics["metric_for_best_model"]] @@ -771,23 +776,18 @@ def save(self, epoch, path="recon", include_optimizer=False): # create directory if it does not exist if not os.path.exists(path): os.makedirs(path) - # save mask + + # save mask parameters if self.use_mask: - # torch.save(self.mask._mask, os.path.join(path, f"mask_epoch{epoch}.pt")) - # save mask as numpy array - if self.mask.train_mask_vals: - np.save( - os.path.join(path, f"mask_epoch{epoch}.npy"), - self.mask._mask.cpu().detach().numpy(), - ) + for name, param in self.mask.named_parameters(): - if self.mask.color_filter is not None: - # save save numpy array - np.save( - os.path.join(path, f"mask_color_filter_epoch{epoch}.npy"), - self.mask.color_filter.cpu().detach().numpy(), - ) + # save as numpy array + if param.requires_grad: + np.save( + os.path.join(path, f"mask{name}_epoch{epoch}.npy"), + param.cpu().detach().numpy(), + ) torch.save( self.mask._optimizer.state_dict(), os.path.join(path, f"mask_optim_epoch{epoch}.pt") @@ -802,5 +802,6 @@ def save(self, epoch, path="recon", include_optimizer=False): # save optimizer if include_optimizer: torch.save(self.optimizer.state_dict(), os.path.join(path, f"optim_epoch{epoch}.pt")) + # save recon torch.save(self.recon.state_dict(), os.path.join(path, f"recon_epoch{epoch}")) diff --git a/scripts/recon/train_unrolled.py b/scripts/recon/train_unrolled.py index 8a326a7a..c9193674 100644 --- a/scripts/recon/train_unrolled.py +++ b/scripts/recon/train_unrolled.py @@ -108,6 +108,13 @@ def simulate_dataset(config, generator=None): transform = transforms.Compose(transforms_list) train_ds = datasets.MNIST(root=data_path, train=True, download=True, transform=transform) test_ds = datasets.MNIST(root=data_path, train=False, download=True, transform=transform) + + if config.files.n_files is not None: + train_size = int((1 - config.files.test_size) * config.files.n_files) + test_size = config.files.n_files - train_size + train_ds = Subset(train_ds, np.arange(train_size)) + test_ds = Subset(test_ds, np.arange(test_size)) + elif config.files.dataset == "fashion_mnist": transform = transforms.Compose(transforms_list) train_ds = datasets.FashionMNIST( @@ -274,8 +281,6 @@ def prep_trainable_mask(config, psf=None, downsample=None): if isinstance(config.trainable_mask.initial_value, omegaconf.dictconfig.DictConfig): - from lensless.hardware.trainable_mask import TrainableCodedAperture - # from mask config mask = mask_class( # mask = TrainableCodedAperture( From 5e1f8cc8059c0acd5c7e3ff9e8a955d1f0dcb701 Mon Sep 17 00:00:00 2001 From: Eric Bezzam Date: Wed, 6 Dec 2023 17:46:11 +0100 Subject: [PATCH 38/39] Fix MURA. --- configs/train_coded_aperture.yaml | 6 +++--- lensless/hardware/mask.py | 27 +++++++++++++++------------ lensless/hardware/trainable_mask.py | 29 +++++++++++++++++++++-------- scripts/recon/train_unrolled.py | 1 + 4 files changed, 40 insertions(+), 23 deletions(-) diff --git a/configs/train_coded_aperture.yaml b/configs/train_coded_aperture.yaml index 5d8aa6d4..b1e5e30a 100644 --- a/configs/train_coded_aperture.yaml +++ b/configs/train_coded_aperture.yaml @@ -15,12 +15,12 @@ trainable_mask: optimizer: Adam mask_lr: 1e-3 L1_strength: False + binary: False initial_value: method: MLS - n_bits: 8 - # MURA not working... + n_bits: 8 # (2**n_bits-1, 2**n_bits-1) # method: MURA - # n_bits: 3 + # n_bits: 25 # (4*nbits*1, 4*nbits*1) simulation: grayscale: False diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index 4ffa7b36..7c34d627 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -109,7 +109,6 @@ def __init__( self.torch_device = torch_device # create mask - self.mask = None self.create_mask() self.shape = self.mask.shape @@ -216,19 +215,20 @@ def __init__(self, method="MLS", n_bits=8, **kwargs): # initialize parameters if self.method.upper() == "MURA": - mask = self.squarepattern(4 * self.n_bits + 1)[1:, 1:] - self.row = self.mask[0, :] - self.col = self.mask[:, 0] - outer = np.outer(self.row, self.col) - assert np.all(outer == mask) + self.mask = self.squarepattern(4 * self.n_bits + 1) + self.row = None + self.col = None else: seq = max_len_seq(self.n_bits)[0] self.row = seq self.col = seq - if kwargs["is_torch"]: - self.row = torch.from_numpy(self.row).float() - self.col = torch.from_numpy(self.col).float() + if "is_torch" in kwargs and kwargs["is_torch"]: + if self.row is not None and self.col is not None: + self.row = torch.from_numpy(self.row).float() + self.col = torch.from_numpy(self.col).float() + else: + self.mask = torch.from_numpy(self.mask).float() super().__init__(**kwargs) @@ -238,10 +238,13 @@ def create_mask(self): """ # outer product - if self.is_torch: - self.mask = torch.outer(self.row, self.col) + if self.row is not None and self.col is not None: + if self.is_torch: + self.mask = torch.outer(self.row, self.col) + else: + self.mask = np.outer(self.row, self.col) else: - self.mask = np.outer(self.row, self.col) + assert self.mask is not None # resize to sensor shape if np.any(self.resolution != self.mask.shape): diff --git a/lensless/hardware/trainable_mask.py b/lensless/hardware/trainable_mask.py index b4328f45..eef57933 100644 --- a/lensless/hardware/trainable_mask.py +++ b/lensless/hardware/trainable_mask.py @@ -265,9 +265,17 @@ def __init__( self._mask = self._mask_obj.mask # 3) set learnable parameters (should be immediate attributes of the class) - self._row = torch.nn.Parameter(self._mask_obj.row) - self._col = torch.nn.Parameter(self._mask_obj.col) - initial_param = [self._row, self._col] + if self._mask_obj.row is not None: + # seperable + self.separable = True + self._row = torch.nn.Parameter(self._mask_obj.row) + self._col = torch.nn.Parameter(self._mask_obj.col) + initial_param = [self._row, self._col] + else: + # non-seperable + self.separable = False + self._vals = torch.nn.Parameter(self._mask_obj.mask) + initial_param = [self._vals] self.binary = binary # 4) set optimizer @@ -279,8 +287,13 @@ def get_psf(self): return self._mask_obj.psf.unsqueeze(0) def project(self): - self._row.data = torch.clamp(self._row, 0, 1) - self._col.data = torch.clamp(self._col, 0, 1) - if self.binary: - self._row.data = torch.round(self._row) - self._col.data = torch.round(self._col) + if self.separable: + self._row.data = torch.clamp(self._row, 0, 1) + self._col.data = torch.clamp(self._col, 0, 1) + if self.binary: + self._row.data = torch.round(self._row) + self._col.data = torch.round(self._col) + else: + self._vals.data = torch.clamp(self._vals, 0, 1) + if self.binary: + self._vals.data = torch.round(self._vals) diff --git a/scripts/recon/train_unrolled.py b/scripts/recon/train_unrolled.py index c9193674..132464f0 100644 --- a/scripts/recon/train_unrolled.py +++ b/scripts/recon/train_unrolled.py @@ -289,6 +289,7 @@ def prep_trainable_mask(config, psf=None, downsample=None): distance_sensor=config.simulation.mask2sensor, optimizer=config.trainable_mask.optimizer, lr=config.trainable_mask.mask_lr, + binary=config.trainable_mask.binary, **config.trainable_mask.initial_value, ) From 922c866e773e9d67f5ab4a3cf9084800b941ffff Mon Sep 17 00:00:00 2001 From: Ahmed Date: Thu, 7 Dec 2023 15:05:42 +0100 Subject: [PATCH 39/39] matching names --- lensless/hardware/mask.py | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/lensless/hardware/mask.py b/lensless/hardware/mask.py index 0271ed93..4398b63c 100644 --- a/lensless/hardware/mask.py +++ b/lensless/hardware/mask.py @@ -175,7 +175,7 @@ def compute_psf(self): dz=self.distance_sensor, dtype=np.float32 if not self.is_torch else torch.float32, bandlimit=True, - device=self.torch_device if self.is_torch else None, + torch_device=self.torch_device if self.is_torch else None, )[0] # intensity PSF @@ -186,7 +186,7 @@ def compute_psf(self): # intensity PSF self.psf = torch.abs(psf) ** 2 - self.psf = torch.Tensor(self.psf).to(self.device) + self.psf = torch.Tensor(self.psf).to(self.torch_device) class CodedAperture(Mask): @@ -408,11 +408,11 @@ def check_asserts(self): assert self.loc is not None, "Location of the lenses should be specified if their radius is specified" assert len(self.radius) == len(self.loc), "Number of radius should be equal to the number of locations" self.N = len(self.radius) - circles = np.array([(self.loc[i][0], self.loc[i][1], self.radius[i]) for i in range(self.N)]) if not self.is_Torch else torch.tensor([(self.loc[i][0], self.loc[i][1], self.radius[i]) for i in range(self.N)]) + circles = np.array([(self.loc[i][0], self.loc[i][1], self.radius[i]) for i in range(self.N)]) if not self.is_torch else torch.tensor([(self.loc[i][0], self.loc[i][1], self.radius[i]) for i in range(self.N)]) assert self.no_circle_overlap(circles), "lenses should not overlap" else: assert self.N is not None, "If positions are not specified, the number of lenses should be specified" - if self.is_Torch: + if self.is_torch: torch.manual_seed(self.seed) self.radius = torch.rand(self.N) * (1e-3 - self.min_height) + self.min_height else: @@ -429,7 +429,7 @@ def no_circle_overlap(self, circles): def does_circle_overlap(self, circles, x, y, r): """Check if a circle overlaps with any in the list.""" - if not self.is_Torch: + if not self.is_torch: for (cx, cy, cr) in circles: if np.sqrt((x - cx)**2 + (y - cy)**2) <= r + cr: return True, (cx, cy, cr) @@ -449,8 +449,8 @@ def place_spheres_on_plane(self, width, height, radius, max_attempts=1000): for r in radius_sorted: placed = False for _ in range(max_attempts): - x = np.random.uniform(r, width - r) if self.is_Torch == False else torch.rand(1) * (width - 2*r) + r - y = np.random.uniform(r, height - r) if self.is_Torch == False else torch.rand(1) * (height - 2*r) + r + x = np.random.uniform(r, width - r) if self.is_torch == False else torch.rand(1) * (width - 2*r) + r + y = np.random.uniform(r, height - r) if self.is_torch == False else torch.rand(1) * (height - 2*r) + r if not self.does_circle_overlap(placed_circles, x , y , r): placed_circles.append((x, y, r)) @@ -462,7 +462,7 @@ def place_spheres_on_plane(self, width, height, radius, max_attempts=1000): print(f"Failed to place circle with rad {r}") continue - placed_circles = np.array(placed_circles) if not self.is_Torch else torch.tensor(placed_circles) + placed_circles = np.array(placed_circles) if not self.is_torch else torch.tensor(placed_circles) circles = placed_circles[:, :2] radius = placed_circles[:, 2] @@ -480,25 +480,25 @@ def create_mask(self): fig, ax = plt.subplots() - im = ax.imshow(height.cpu().detach().numpy() if self.is_Torch else height, cmap="gray") + im = ax.imshow(height.cpu().detach().numpy() if self.is_torch else height, cmap="gray") fig.colorbar(im, ax=ax, shrink=0.5, aspect=5) plt.title("Height map") plt.show() - self.mask = np.exp(1j * self.phi) if not self.is_Torch else torch.exp(1j * self.phi) + self.mask = np.exp(1j * self.phi) if not self.is_torch else torch.exp(1j * self.phi) def create_height_map(self, radius, locs): - height = np.full((self.resolution[0], self.resolution[1]), self.min_height) if not self.is_Torch else torch.full((self.resolution[0], self.resolution[1]), self.min_height) + height = np.full((self.resolution[0], self.resolution[1]), self.min_height) if not self.is_torch else torch.full((self.resolution[0], self.resolution[1]), self.min_height) for x in range(height.shape[0]): for y in range(height.shape[1]): height[x, y] += self.lens_contribution(radius, locs, (x + 0.5), (y + 0.5)) * self.feature_size[0] - assert np.all(height >= self.min_height) if not self.is_Torch else torch.all(torch.ge(height, self.min_height)) + assert np.all(height >= self.min_height) if not self.is_torch else torch.all(torch.ge(height, self.min_height)) return height def lens_contribution(self, radius, locs, x, y): contribution = 0 for idx, loc in enumerate(locs): if (x-loc[0])**2 + (y-loc[1])**2 < radius[idx]**2: - contribution = np.sqrt((radius[idx])**2 - ((x-loc[0]))**2 - ((y -loc[1]))**2) if not self.is_Torch else torch.sqrt((radius[idx])**2 - ((x-loc[0]))**2 - ((y -loc[1]))**2) + contribution = np.sqrt((radius[idx])**2 - ((x-loc[0]))**2 - ((y -loc[1]))**2) if not self.is_torch else torch.sqrt((radius[idx])**2 - ((x-loc[0]))**2 - ((y -loc[1]))**2) return contribution return contribution @@ -714,13 +714,13 @@ def __init__( def get_phi(self): phi = self.height_map * (2*np.pi*(self.refractive_index-1) / self.wavelength) phi = phi % (2*np.pi) - if self.is_Torch == False: + if self.is_torch == False: return phi else: - return torch.tensor(phi).to(self.device) + return torch.tensor(phi).to(self.torch_device) def create_mask(self): - if self.is_Torch is None or self.is_Torch == False: + if self.is_torch is None or self.is_torch == False: if self.height_map is None: np.random.seed(self.seed) self.height_map = np.random.uniform(self.height_range[0], self.height_range[1], self.resolution) @@ -735,9 +735,9 @@ def create_mask(self): # Generate a random height map using PyTorch resolution = torch.tensor(self.resolution) print('resolution=', resolution) - self.height_map = torch.rand((resolution[0], resolution[1])).to(self.device) * (height_range_tensor[1] - height_range_tensor[0]) + height_range_tensor[0] + self.height_map = torch.rand((resolution[0], resolution[1])).to(self.torch_device) * (height_range_tensor[1] - height_range_tensor[0]) + height_range_tensor[0] print('self.height_map.shape=', self.height_map.shape) assert self.height_map.shape == tuple(self.resolution) phase_mask = self.get_phi() - self.mask = torch.exp(1j * phase_mask).to(self.device) + self.mask = torch.exp(1j * phase_mask).to(self.torch_device) \ No newline at end of file

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