None:\n '''\n Constructor examples:\n a = Matrix_4x4( ) # create identity matrix\n b = Matrix_4x4( [[1,1,1,1],[2,2,2,2],[3,3,3,3],[4,4,4,4]] ) # manually initialize matrix\n c = Matrix_4x4( [1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4] ) # manually initialize matrix\n d = Matrix_4x4( b ) # copy constructor\n '''\n if matrix is None:\n self.m = np.identity(4)\n elif type(matrix) == Matrix_4x4: \n self.m = np.copy(matrix.m)\n elif type(matrix) == Matrix_3x3: \n self.m = np.identity(4)\n self.m[0:3,0:3] = matrix.m\n else:\n self.m = np.asarray(matrix)\n self.m.shape = (4,4) #reshape if needed, throw error if impossible\n\n\n @classmethod\n def from_translation(cls, translation_vec):\n '''\n Create transformation matrix from translation_vec translation\n e.g. Matrix_4x4.from_translation((a,b,c))\n output: [[1,0,0,a],[0,1,0,b],[0,0,1,c],[0,0,0,1]]\n '''\n mat = np.identity(4)\n mat[0:3,3] = translation_vec\n return cls(mat)\n\n @classmethod\n def from_3x3_and_translation(cls, mat3x3:Matrix_3x3, translation_vec):\n '''\n Create transformation matrix from rotation matrix (3x3) and translation\n e.g. Matrix_4x4.from_3x3_and_translation(r,(a,b,c)) \n output: [[r00,r01,r02,a],[r10,r11,r12,b],[r20,r21,r22,c],[0,0,0,1]]\n '''\n mat = np.identity(4)\n mat[0:3,0:3] = mat3x3.m\n mat[0:3,3] = translation_vec\n return cls(mat)\n\n def translate(self, translation_vec, in_place=False):\n '''\n Translates the current transformation matrix\n\n Parameters\n ----------\n translation_vec : array_like, length 3\n translation vector\n in_place: bool, optional\n * True: the internal matrix is changed in-place\n * False: a new matrix is returned and the current one is not changed \n\n Returns\n -------\n result : Matrix_4x4 \n self is returned if in_place is True\n '''\n vec = np.array([*translation_vec,1])# conversion to 4D vector\n np.matmul(self.m, vec, out=vec) # compute only 4th column\n\n if in_place:\n self.m[:,3] = vec\n return self\n else:\n ret = Matrix_4x4(self.m)\n ret.m[:,3] = vec\n return ret\n\n\n def get_translation(self):\n ''' Get translation vector (x,y,z) '''\n return self.m[0:3,3] # return view\n\n def get_x(self):\n return self.m[0,3]\n\n def get_y(self):\n return self.m[1,3]\n\n def get_z(self):\n return self.m[2,3]\n\n def get_rotation_4x4(self):\n ''' Get Matrix_4x4 without translation ''' \n mat = Matrix_4x4(self)\n mat.m[0:3,3] = 0\n return mat\n\n def get_rotation(self):\n ''' Get rotation Matrix_3x3 '''\n return Matrix_3x3(self.m[0:3,0:3])\n\n def get_distance(self):\n ''' Get translation vector length '''\n return np.linalg.norm(self.m[0:3,3])\n\n def get_roll_deg(self):\n ''' Get angle around the x-axis in degrees, Rotation order: RotZ*RotY*RotX=Rot '''\n if self.m[2,1] == 0 and self.m[2,2] == 0: \n return 180\n return atan2(self.m[2,1], self.m[2,2]) * 180 / pi\n\n def get_pitch_deg(self):\n ''' Get angle around the y-axis in degrees, Rotation order: RotZ*RotY*RotX=Rot '''\n return atan2(-self.m[2,0], sqrt(self.m[2,1]*self.m[2,1] + self.m[2,2]*self.m[2,2])) * 180 / pi\n\n def get_yaw_deg(self):\n ''' Get angle around the z-axis in degrees, Rotation order: RotZ*RotY*RotX=Rot '''\n if self.m[1,0] == 0 and self.m[0,0] == 0: \n return atan2(self.m[0,1], self.m[1,1]) * 180 / pi\n return atan2(self.m[1,0], self.m[0,0]) * 180 / pi\n \n def get_inclination_deg(self):\n ''' Get inclination of z-axis in relation to reference z-axis '''\n return 90 - (asin(np.clip(self.m[2,2],-1,1)) * 180 / pi)\n\n def rotate_deg(self, rotation_vec, rotation_deg, in_place=False):\n '''\n Rotates the current transformation matrix\n\n Parameters\n ----------\n rotation_vec : array_like, length 3\n rotation vector\n rotation_rad : float\n rotation in degrees\n in_place: bool, optional\n * True: the internal matrix is changed in-place (default)\n * False: a new matrix is returned and the current one is not changed \n \n Returns\n -------\n result : Matrix_4x4 \n self is returned if in_place is True\n '''\n return self.rotate_rad(rotation_vec, rotation_deg * (pi/180) , in_place)\n\n \n def rotate_rad(self, rotation_vec, rotation_rad, in_place=False):\n '''\n Rotates the current transformation matrix\n\n Parameters\n ----------\n rotation_vec : array_like, length 3\n rotation vector\n rotation_rad : float\n rotation in radians\n in_place: bool, optional\n * True: the internal matrix is changed in-place (default)\n * False: a new matrix is returned and the current one is not changed \n \n Returns\n -------\n result : Matrix_4x4 \n self is returned if in_place is True\n '''\n\n if rotation_rad == 0: \n return self if in_place else Matrix_4x4(self)\n\n # shortcuts for rotation around 1 axis\n if rotation_vec[0]==0:\n if rotation_vec[1]==0:\n if rotation_vec[2]==1:\n return self.rotate_z_rad(rotation_rad, in_place)\n elif rotation_vec[2]==-1:\n return self.rotate_z_rad(-rotation_rad, in_place)\n elif rotation_vec[2]==0:\n if rotation_vec[1]==1:\n return self.rotate_y_rad(rotation_rad, in_place)\n elif rotation_vec[1]==-1:\n return self.rotate_y_rad(-rotation_rad, in_place)\n elif rotation_vec[1]==0 and rotation_vec[2]==0:\n if rotation_vec[0]==1:\n return self.rotate_x_rad(rotation_rad, in_place)\n elif rotation_vec[0]==-1:\n return self.rotate_x_rad(-rotation_rad, in_place)\n \n c = np.math.cos(rotation_rad)\n c1 = 1 - c\n s = np.math.sin(rotation_rad)\n x = rotation_vec[0]\n y = rotation_vec[1]\n z = rotation_vec[2]\n xxc1 = x * x * c1\n yyc1 = y * y * c1\n zzc1 = z * z * c1\n xyc1 = x * y * c1\n xzc1 = x * z * c1\n yzc1 = y * z * c1\n xs = x * s\n ys = y * s\n zs = z * s\n\n mat = np.array([\n [xxc1 + c, xyc1 - zs, xzc1 + ys, 0],\n [xyc1 + zs, yyc1 + c, yzc1 - xs, 0],\n [xzc1 - ys, yzc1 + xs, zzc1 + c, 0],\n [0, 0, 0, 1]])\n\n return self.multiply(mat, in_place)\n\n\n def rotate_x_rad(self, rotation_rad, in_place=False):\n '''\n Rotates the current transformation matrix around the x-axis\n\n Parameters\n ----------\n rotation_rad : float\n rotation in radians\n in_place: bool, optional\n * True: the internal matrix is changed in-place (default)\n * False: a new matrix is returned and the current one is not changed \n \n Returns\n -------\n result : Matrix_4x4 \n self is returned if in_place is True\n '''\n if rotation_rad == 0: \n return self if in_place else Matrix_4x4(self)\n \n c = np.math.cos(rotation_rad)\n s = np.math.sin(rotation_rad)\n\n mat = np.array([\n [1, 0, 0, 0],\n [0, c,-s, 0],\n [0, s, c, 0],\n [0, 0, 0, 1]])\n\n return self.multiply(mat, in_place)\n\n def rotate_y_rad(self, rotation_rad, in_place=False):\n '''\n Rotates the current transformation matrix around the y-axis\n\n Parameters\n ----------\n rotation_rad : float\n rotation in radians\n in_place: bool, optional\n * True: the internal matrix is changed in-place (default)\n * False: a new matrix is returned and the current one is not changed \n \n Returns\n -------\n result : Matrix_4x4 \n self is returned if in_place is True\n '''\n if rotation_rad == 0: \n return self if in_place else Matrix_4x4(self)\n \n c = np.math.cos(rotation_rad)\n s = np.math.sin(rotation_rad)\n\n mat = np.array([\n [ c, 0, s, 0],\n [ 0, 1, 0, 0],\n [-s, 0, c, 0],\n [ 0, 0, 0, 1]])\n\n return self.multiply(mat, in_place)\n\n def rotate_z_rad(self, rotation_rad, in_place=False):\n '''\n Rotates the current transformation matrix around the z-axis\n\n Parameters\n ----------\n rotation_rad : float\n rotation in radians\n in_place: bool, optional\n * True: the internal matrix is changed in-place (default)\n * False: a new matrix is returned and the current one is not changed \n \n Returns\n -------\n result : Matrix_4x4 \n self is returned if in_place is True\n '''\n if rotation_rad == 0: \n return self if in_place else Matrix_4x4(self)\n \n c = np.math.cos(rotation_rad)\n s = np.math.sin(rotation_rad)\n\n mat = np.array([\n [ c,-s, 0, 0],\n [ s, c, 0, 0],\n [ 0, 0, 1, 0],\n [ 0, 0, 0, 1]])\n\n return self.multiply(mat, in_place)\n\n def rotate_x_deg(self, rotation_deg, in_place=False):\n '''\n Rotates the current transformation matrix around the x-axis\n\n Parameters\n ----------\n rotation_rad : float\n rotation in degrees\n in_place: bool, optional\n * True: the internal matrix is changed in-place (default)\n * False: a new matrix is returned and the current one is not changed \n \n Returns\n -------\n result : Matrix_4x4 \n self is returned if in_place is True\n '''\n return self.rotate_x_rad(rotation_deg * (pi/180), in_place)\n\n def rotate_y_deg(self, rotation_deg, in_place=False):\n '''\n Rotates the current transformation matrix around the y-axis\n\n Parameters\n ----------\n rotation_rad : float\n rotation in degrees\n in_place: bool, optional\n * True: the internal matrix is changed in-place (default)\n * False: a new matrix is returned and the current one is not changed \n \n Returns\n -------\n result : Matrix_4x4 \n self is returned if in_place is True\n '''\n return self.rotate_y_rad(rotation_deg * (pi/180), in_place)\n\n def rotate_z_deg(self, rotation_deg, in_place=False):\n '''\n Rotates the current transformation matrix around the z-axis\n\n Parameters\n ----------\n rotation_rad : float\n rotation in degrees\n in_place: bool, optional\n * True: the internal matrix is changed in-place (default)\n * False: a new matrix is returned and the current one is not changed \n \n Returns\n -------\n result : Matrix_4x4 \n self is returned if in_place is True\n '''\n return self.rotate_z_rad(rotation_deg * (pi/180), in_place)\n\n def invert(self, in_place=False):\n '''\n Inverts the current transformation matrix\n\n Parameters\n ----------\n in_place: bool, optional\n * True: the internal matrix is changed in-place (default)\n * False: a new matrix is returned and the current one is not changed \n \n Returns\n -------\n result : Matrix_4x4 \n self is returned if in_place is True\n '''\n\n if in_place:\n self.m = np.linalg.inv(self.m)\n return self\n else:\n return Matrix_4x4(np.linalg.inv(self.m))\n\n def multiply(self,mat, in_place=False):\n '''\n Multiplies the current transformation matrix by mat\n\n Parameters\n ----------\n mat : Matrix_4x4 or array_like\n multiplier matrix or 3D vector\n in_place: bool, optional\n * True: the internal matrix is changed in-place (default)\n * False: a new matrix is returned and the current one is not changed (if mat is a 4x4 matrix)\n \n Returns\n -------\n result : Matrix_4x4 | array_like\n Matrix_4x4 is returned if mat is a matrix (self is returned if in_place is True); \n a 3D vector is returned if mat is a vector\n '''\n if type(mat) == Matrix_4x4: \n mat = mat.m\n else:\n mat = np.asarray(mat) # conversion to array, if needed\n if mat.ndim == 1: # multiplication by 3D vector\n vec = np.append(mat,1) # conversion to 4D vector\n return np.matmul(self.m, vec)[0:3] # conversion to 3D vector\n\n if in_place:\n np.matmul(self.m, mat, self.m)\n return self\n else:\n return Matrix_4x4(np.matmul(self.m, mat))\n\n def __call__(self,mat, is_spherical=False):\n '''\n Multiplies the current transformation matrix by mat and returns a new matrix or vector\n\n Parameters\n ----------\n mat : Matrix_4x4 or array_like\n multiplier matrix or 3D vector\n is_spherical : bool\n only relevant if mat is a 3D vector, True if it uses spherical coordinates\n \n Returns\n -------\n result : Matrix_4x4 | array_like\n Matrix_4x4 is returned if mat is a matrix; \n a 3D vector is returned if mat is a vector\n '''\n\n if is_spherical and mat.ndim == 1: mat = M.deg_sph2cart(mat)\n return self.multiply(mat,False)"},{"identifier":"Body_Part","path":"world/commons/Body_Part.py","snippet":"class Body_Part():\n def __init__(self, mass) -> None:\n self.mass = float(mass)\n self.joints = []\n self.transform = Matrix_4x4() # body part to head transformation matrix"},{"identifier":"Joint_Info","path":"world/commons/Joint_Info.py","snippet":"class Joint_Info():\n def __init__(self, xml_element) -> None:\n self.perceptor = xml_element.attrib['perceptor']\n self.effector = xml_element.attrib['effector']\n self.axes = np.array([\n float(xml_element.attrib['xaxis']), \n float(xml_element.attrib['yaxis']), \n float(xml_element.attrib['zaxis'])])\n self.min = int(xml_element.attrib['min'])\n self.max = int(xml_element.attrib['max'])\n\n self.anchor0_part = xml_element[0].attrib['part']\n self.anchor0_axes = np.array([\n float(xml_element[0].attrib['y']), \n float(xml_element[0].attrib['x']), \n float(xml_element[0].attrib['z'])]) #x and y axes are switched\n\n self.anchor1_part = xml_element[1].attrib['part']\n self.anchor1_axes_neg = np.array([\n -float(xml_element[1].attrib['y']), \n -float(xml_element[1].attrib['x']), \n -float(xml_element[1].attrib['z'])]) #x and y axes are switched"}],"string":"[\n {\n \"identifier\": \"Math_Ops\",\n \"path\": \"math_ops/Math_Ops.py\",\n \"snippet\": \"class Math_Ops():\\n '''\\n This class provides general mathematical operations that are not directly available through numpy \\n '''\\n \\n @staticmethod\\n def deg_sph2cart(spherical_vec):\\n ''' Converts SimSpark's spherical coordinates in degrees to cartesian coordinates '''\\n r = spherical_vec[0]\\n h = spherical_vec[1] * pi / 180\\n v = spherical_vec[2] * pi / 180\\n return np.array([r * cos(v) * cos(h), r * cos(v) * sin(h), r * sin(v)])\\n\\n @staticmethod\\n def deg_sin(deg_angle):\\n ''' Returns sin of degrees '''\\n return sin(deg_angle * pi / 180)\\n\\n @staticmethod\\n def deg_cos(deg_angle):\\n ''' Returns cos of degrees '''\\n return cos(deg_angle * pi / 180)\\n\\n @staticmethod\\n def to_3d(vec_2d, value=0) -> np.ndarray:\\n ''' Returns new 3d vector from 2d vector '''\\n return np.append(vec_2d,value)\\n\\n @staticmethod\\n def to_2d_as_3d(vec_3d) -> np.ndarray:\\n ''' Returns new 3d vector where the 3rd dimension is zero '''\\n vec_2d_as_3d = np.copy(vec_3d)\\n vec_2d_as_3d[2] = 0\\n return vec_2d_as_3d\\n\\n @staticmethod\\n def normalize_vec(vec) -> np.ndarray:\\n ''' Divides vector by its length '''\\n size = np.linalg.norm(vec)\\n if size == 0: return vec\\n return vec / size\\n\\n @staticmethod\\n def get_active_directory(dir:str) -> str:\\n global GLOBAL_DIR\\n return GLOBAL_DIR + dir\\n\\n @staticmethod\\n def acos(val):\\n ''' arccosine function that limits input '''\\n return acos( np.clip(val,-1,1) )\\n \\n @staticmethod\\n def asin(val):\\n ''' arcsine function that limits input '''\\n return asin( np.clip(val,-1,1) )\\n\\n @staticmethod\\n def normalize_deg(val):\\n ''' normalize val in range [-180,180[ '''\\n return (val + 180.0) % 360 - 180\\n\\n @staticmethod\\n def normalize_rad(val):\\n ''' normalize val in range [-pi,pi[ '''\\n return (val + pi) % (2*pi) - pi\\n\\n @staticmethod\\n def deg_to_rad(val):\\n ''' convert degrees to radians '''\\n return val * 0.01745329251994330\\n\\n @staticmethod\\n def rad_to_deg(val):\\n ''' convert radians to degrees '''\\n return val * 57.29577951308232\\n\\n @staticmethod\\n def vector_angle(vector, is_rad=False):\\n ''' angle (degrees or radians) of 2D vector '''\\n if is_rad:\\n return atan2(vector[1], vector[0])\\n else:\\n return atan2(vector[1], vector[0]) * 180 / pi\\n\\n @staticmethod\\n def vectors_angle(vec1, vec2, is_rad=False):\\n ''' get angle between vectors (degrees or radians) '''\\n ang_rad = acos(np.dot(Math_Ops.normalize_vec(vec1),Math_Ops.normalize_vec(vec2)))\\n return ang_rad if is_rad else ang_rad * 180 / pi\\n\\n @staticmethod\\n def vector_from_angle(angle, is_rad=False):\\n ''' unit vector with direction given by `angle` '''\\n if is_rad:\\n return np.array([cos(angle), sin(angle)], float)\\n else:\\n return np.array([Math_Ops.deg_cos(angle), Math_Ops.deg_sin(angle)], float)\\n\\n @staticmethod\\n def target_abs_angle(pos2d, target, is_rad=False):\\n ''' angle (degrees or radians) of vector (target-pos2d) '''\\n if is_rad:\\n return atan2(target[1]-pos2d[1], target[0]-pos2d[0])\\n else:\\n return atan2(target[1]-pos2d[1], target[0]-pos2d[0]) * 180 / pi\\n\\n @staticmethod\\n def target_rel_angle(pos2d, ori, target, is_rad=False):\\n ''' relative angle (degrees or radians) of target if we're located at 'pos2d' with orientation 'ori' (degrees or radians) '''\\n if is_rad:\\n return Math_Ops.normalize_rad( atan2(target[1]-pos2d[1], target[0]-pos2d[0]) - ori )\\n else:\\n return Math_Ops.normalize_deg( atan2(target[1]-pos2d[1], target[0]-pos2d[0]) * 180 / pi - ori )\\n\\n @staticmethod\\n def rotate_2d_vec(vec, angle, is_rad=False):\\n ''' rotate 2D vector anticlockwise around the origin by `angle` '''\\n cos_ang = cos(angle) if is_rad else cos(angle * pi / 180)\\n sin_ang = sin(angle) if is_rad else sin(angle * pi / 180)\\n return np.array([cos_ang*vec[0]-sin_ang*vec[1], sin_ang*vec[0]+cos_ang*vec[1]])\\n\\n @staticmethod\\n def distance_point_to_line(p:np.ndarray, a:np.ndarray, b:np.ndarray):\\n ''' \\n Distance between point p and 2d line 'ab' (and side where p is)\\n\\n Parameters\\n ----------\\n a : ndarray\\n 2D point that defines line\\n b : ndarray\\n 2D point that defines line\\n p : ndarray\\n 2D point\\n\\n Returns\\n -------\\n distance : float\\n distance between line and point\\n side : str\\n if we are at a, looking at b, p may be at our \\\"left\\\" or \\\"right\\\"\\n '''\\n line_len = np.linalg.norm(b-a)\\n\\n if line_len == 0: # assumes vertical line\\n dist = sdist = np.linalg.norm(p-a)\\n else:\\n sdist = np.cross(b-a,p-a)/line_len\\n dist = abs(sdist)\\n\\n return dist, \\\"left\\\" if sdist>0 else \\\"right\\\"\\n\\n @staticmethod\\n def distance_point_to_segment(p:np.ndarray, a:np.ndarray, b:np.ndarray):\\n ''' Distance from point p to 2d line segment 'ab' '''\\n \\n ap = p-a\\n ab = b-a\\n\\n ad = Math_Ops.vector_projection(ap,ab)\\n\\n # Is d in ab? We can find k in (ad = k * ab) without computing any norm\\n # we use the largest dimension of ab to avoid division by 0\\n k = ad[0]/ab[0] if abs(ab[0])>abs(ab[1]) else ad[1]/ab[1]\\n\\n if k <= 0: return np.linalg.norm(ap)\\n elif k >= 1: return np.linalg.norm(p-b)\\n else: return np.linalg.norm(p-(ad + a)) # p-d\\n\\n @staticmethod\\n def distance_point_to_ray(p:np.ndarray, ray_start:np.ndarray, ray_direction:np.ndarray):\\n ''' Distance from point p to 2d ray '''\\n \\n rp = p-ray_start\\n rd = Math_Ops.vector_projection(rp,ray_direction)\\n\\n # Is d in ray? We can find k in (rd = k * ray_direction) without computing any norm\\n # we use the largest dimension of ray_direction to avoid division by 0\\n k = rd[0]/ray_direction[0] if abs(ray_direction[0])>abs(ray_direction[1]) else rd[1]/ray_direction[1]\\n\\n if k <= 0: return np.linalg.norm(rp)\\n else: return np.linalg.norm(p-(rd + ray_start)) # p-d\\n\\n @staticmethod\\n def closest_point_on_ray_to_point(p:np.ndarray, ray_start:np.ndarray, ray_direction:np.ndarray):\\n ''' Point on ray closest to point p '''\\n \\n rp = p-ray_start\\n rd = Math_Ops.vector_projection(rp,ray_direction)\\n\\n # Is d in ray? We can find k in (rd = k * ray_direction) without computing any norm\\n # we use the largest dimension of ray_direction to avoid division by 0\\n k = rd[0]/ray_direction[0] if abs(ray_direction[0])>abs(ray_direction[1]) else rd[1]/ray_direction[1]\\n\\n if k <= 0: return ray_start\\n else: return rd + ray_start\\n\\n @staticmethod\\n def does_circle_intersect_segment(p:np.ndarray, r, a:np.ndarray, b:np.ndarray):\\n ''' Returns true if circle (center p, radius r) intersect 2d line segment '''\\n\\n ap = p-a\\n ab = b-a\\n\\n ad = Math_Ops.vector_projection(ap,ab)\\n\\n # Is d in ab? We can find k in (ad = k * ab) without computing any norm\\n # we use the largest dimension of ab to avoid division by 0\\n k = ad[0]/ab[0] if abs(ab[0])>abs(ab[1]) else ad[1]/ab[1]\\n\\n if k <= 0: return np.dot(ap,ap) <= r*r\\n elif k >= 1: return np.dot(p-b,p-b) <= r*r\\n \\n dp = p-(ad + a)\\n return np.dot(dp,dp) <= r*r\\n\\n @staticmethod\\n def vector_projection(a:np.ndarray, b:np.ndarray):\\n ''' Vector projection of a onto b '''\\n b_dot = np.dot(b,b)\\n return b * np.dot(a,b) / b_dot if b_dot != 0 else b\\n\\n @staticmethod\\n def do_noncollinear_segments_intersect(a,b,c,d):\\n ''' \\n Check if 2d line segment 'ab' intersects with noncollinear 2d line segment 'cd' \\n Explanation: https://www.geeksforgeeks.org/check-if-two-given-line-segments-intersect/ \\n '''\\n\\n ccw = lambda a,b,c: (c[1]-a[1]) * (b[0]-a[0]) > (b[1]-a[1]) * (c[0]-a[0])\\n return ccw(a,c,d) != ccw(b,c,d) and ccw(a,b,c) != ccw(a,b,d)\\n\\n @staticmethod\\n def intersection_segment_opp_goal(a:np.ndarray, b:np.ndarray):\\n ''' Computes the intersection point of 2d segment 'ab' and the opponents' goal (front line) '''\\n vec_x = b[0]-a[0]\\n\\n # Collinear intersections are not accepted\\n if vec_x == 0: return None\\n \\n k = (15.01-a[0])/vec_x\\n\\n # No collision\\n if k < 0 or k > 1: return None\\n\\n intersection_pt = a + (b-a) * k\\n\\n if -1.01 <= intersection_pt[1] <= 1.01:\\n return intersection_pt\\n else:\\n return None\\n\\n @staticmethod\\n def intersection_circle_opp_goal(p:np.ndarray, r):\\n ''' \\n Computes the intersection segment of circle (center p, radius r) and the opponents' goal (front line)\\n Only the y coordinates are returned since the x coordinates are always equal to 15\\n '''\\n\\n x_dev = abs(15-p[0])\\n\\n if x_dev > r:\\n return None # no intersection with x=15\\n\\n y_dev = sqrt(r*r - x_dev*x_dev)\\n\\n p1 = max(p[1] - y_dev, -1.01)\\n p2 = min(p[1] + y_dev, 1.01)\\n\\n if p1 == p2:\\n return p1 # return the y coordinate of a single intersection point\\n elif p2 < p1:\\n return None # no intersection\\n else:\\n return p1, p2 # return the y coordinates of the intersection segment\\n\\n\\n @staticmethod\\n def distance_point_to_opp_goal(p:np.ndarray):\\n ''' Distance between point 'p' and the opponents' goal (front line) '''\\n\\n if p[1] < -1.01:\\n return np.linalg.norm( p-(15,-1.01) )\\n elif p[1] > 1.01:\\n return np.linalg.norm( p-(15, 1.01) )\\n else:\\n return abs(15-p[0])\\n\\n\\n @staticmethod\\n def circle_line_segment_intersection(circle_center, circle_radius, pt1, pt2, full_line=True, tangent_tol=1e-9):\\n \\\"\\\"\\\" Find the points at which a circle intersects a line-segment. This can happen at 0, 1, or 2 points.\\n\\n :param circle_center: The (x, y) location of the circle center\\n :param circle_radius: The radius of the circle\\n :param pt1: The (x, y) location of the first point of the segment\\n :param pt2: The (x, y) location of the second point of the segment\\n :param full_line: True to find intersections along full line - not just in the segment. False will just return intersections within the segment.\\n :param tangent_tol: Numerical tolerance at which we decide the intersections are close enough to consider it a tangent\\n :return Sequence[Tuple[float, float]]: A list of length 0, 1, or 2, where each element is a point at which the circle intercepts a line segment.\\n\\n Note: We follow: http://mathworld.wolfram.com/Circle-LineIntersection.html\\n \\\"\\\"\\\"\\n\\n (p1x, p1y), (p2x, p2y), (cx, cy) = pt1, pt2, circle_center\\n (x1, y1), (x2, y2) = (p1x - cx, p1y - cy), (p2x - cx, p2y - cy)\\n dx, dy = (x2 - x1), (y2 - y1)\\n dr = (dx ** 2 + dy ** 2)**.5\\n big_d = x1 * y2 - x2 * y1\\n discriminant = circle_radius ** 2 * dr ** 2 - big_d ** 2\\n\\n if discriminant < 0: # No intersection between circle and line\\n return []\\n else: # There may be 0, 1, or 2 intersections with the segment\\n intersections = [\\n (cx + (big_d * dy + sign * (-1 if dy < 0 else 1) * dx * discriminant**.5) / dr ** 2,\\n cy + (-big_d * dx + sign * abs(dy) * discriminant**.5) / dr ** 2)\\n for sign in ((1, -1) if dy < 0 else (-1, 1))] # This makes sure the order along the segment is correct\\n if not full_line: # If only considering the segment, filter out intersections that do not fall within the segment\\n fraction_along_segment = [\\n (xi - p1x) / dx if abs(dx) > abs(dy) else (yi - p1y) / dy for xi, yi in intersections]\\n intersections = [pt for pt, frac in zip(\\n intersections, fraction_along_segment) if 0 <= frac <= 1]\\n # If line is tangent to circle, return just one point (as both intersections have same location)\\n if len(intersections) == 2 and abs(discriminant) <= tangent_tol:\\n return [intersections[0]]\\n else:\\n return intersections\\n\\n\\n\\n\\n # adapted from https://stackoverflow.com/questions/3252194/numpy-and-line-intersections\\n @staticmethod\\n def get_line_intersection(a1, a2, b1, b2):\\n \\\"\\\"\\\" \\n Returns the point of intersection of the lines passing through a2,a1 and b2,b1.\\n a1: [x, y] a point on the first line\\n a2: [x, y] another point on the first line\\n b1: [x, y] a point on the second line\\n b2: [x, y] another point on the second line\\n \\\"\\\"\\\"\\n s = np.vstack([a1,a2,b1,b2]) # s for stacked\\n h = np.hstack((s, np.ones((4, 1)))) # h for homogeneous\\n l1 = np.cross(h[0], h[1]) # get first line\\n l2 = np.cross(h[2], h[3]) # get second line\\n x, y, z = np.cross(l1, l2) # point of intersection\\n if z == 0: # lines are parallel\\n return np.array([float('inf'), float('inf')])\\n return np.array([x/z, y/z],float)\"\n },\n {\n \"identifier\": \"Matrix_3x3\",\n \"path\": \"math_ops/Matrix_3x3.py\",\n \"snippet\": \"class Matrix_3x3():\\n\\n def __init__(self, matrix = None) -> None:\\n '''\\n Constructor examples:\\n a = Matrix_3x3( ) # create identity matrix\\n b = Matrix_3x3( [[1,1,1],[2,2,2],[3,3,3]] ) # manually initialize matrix\\n c = Matrix_3x3( [1,1,1,2,2,2,3,3,3] ) # manually initialize matrix\\n d = Matrix_3x3( b ) # copy constructor\\n '''\\n if matrix is None:\\n self.m = np.identity(3)\\n elif type(matrix) == Matrix_3x3: \\n self.m = np.copy(matrix.m)\\n else:\\n self.m = np.asarray(matrix)\\n self.m.shape = (3,3) #reshape if needed, throw error if impossible\\n\\n\\n self.rotation_shortcuts={(1,0,0):self.rotate_x_rad, (-1, 0, 0):self._rotate_x_neg_rad,\\n (0,1,0):self.rotate_y_rad, ( 0,-1, 0):self._rotate_y_neg_rad,\\n (0,0,1):self.rotate_z_rad, ( 0, 0,-1):self._rotate_z_neg_rad}\\n\\n @classmethod\\n def from_rotation_deg(cls, euler_vec):\\n '''\\n Create rotation matrix from Euler angles, in degrees.\\n Rotation order: RotZ*RotY*RotX\\n\\n Parameters\\n ----------\\n euler_vec : array_like, length 3\\n vector with Euler angles (x,y,z) aka (roll, pitch, yaw)\\n\\n Example\\n ----------\\n Matrix_3x3.from_rotation_deg((roll,pitch,yaw)) # Creates: RotZ(yaw)*RotY(pitch)*RotX(roll)\\n '''\\n mat = cls().rotate_z_deg(euler_vec[2], True).rotate_y_deg(euler_vec[1], True).rotate_x_deg(euler_vec[0], True)\\n return mat\\n\\n def get_roll_deg(self):\\n ''' Get angle around the x-axis in degrees, Rotation order: RotZ*RotY*RotX=Rot '''\\n if self.m[2,1] == 0 and self.m[2,2] == 0: \\n return 180\\n return atan2(self.m[2,1], self.m[2,2]) * 180 / pi\\n\\n def get_pitch_deg(self):\\n ''' Get angle around the y-axis in degrees, Rotation order: RotZ*RotY*RotX=Rot '''\\n return atan2(-self.m[2,0], sqrt(self.m[2,1]*self.m[2,1] + self.m[2,2]*self.m[2,2])) * 180 / pi\\n\\n def get_yaw_deg(self):\\n ''' Get angle around the z-axis in degrees, Rotation order: RotZ*RotY*RotX=Rot '''\\n if self.m[1,0] == 0 and self.m[0,0] == 0: \\n return atan2(self.m[0,1], self.m[1,1]) * 180 / pi\\n return atan2(self.m[1,0], self.m[0,0]) * 180 / pi\\n\\n def get_inclination_deg(self):\\n ''' Get inclination of z-axis in relation to reference z-axis '''\\n return 90 - (asin(self.m[2,2]) * 180 / pi)\\n\\n\\n def rotate_deg(self, rotation_vec, rotation_deg, in_place=False):\\n '''\\n Rotates the current rotation matrix\\n\\n Parameters\\n ----------\\n rotation_vec : array_like, length 3\\n rotation vector\\n rotation_rad : float\\n rotation in degrees\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_3x3 \\n self is returned if in_place is True\\n '''\\n return self.rotate_rad(rotation_vec, rotation_deg * (pi/180) , in_place)\\n\\n \\n def rotate_rad(self, rotation_vec, rotation_rad, in_place=False):\\n '''\\n Rotates the current rotation matrix\\n\\n Parameters\\n ----------\\n rotation_vec : array_like, length 3\\n rotation vector\\n rotation_rad : float\\n rotation in radians\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_3x3 \\n self is returned if in_place is True\\n '''\\n\\n if rotation_rad == 0: return\\n\\n shortcut = self.rotation_shortcuts.get(tuple(a for a in rotation_vec))\\n if shortcut:\\n return shortcut(rotation_rad, in_place)\\n \\n c = np.math.cos(rotation_rad)\\n c1 = 1 - c\\n s = np.math.sin(rotation_rad)\\n x = rotation_vec[0]\\n y = rotation_vec[1]\\n z = rotation_vec[2]\\n xxc1 = x * x * c1\\n yyc1 = y * y * c1\\n zzc1 = z * z * c1\\n xyc1 = x * y * c1\\n xzc1 = x * z * c1\\n yzc1 = y * z * c1\\n xs = x * s\\n ys = y * s\\n zs = z * s\\n\\n mat = np.array([\\n [xxc1 + c, xyc1 - zs, xzc1 + ys],\\n [xyc1 + zs, yyc1 + c, yzc1 - xs],\\n [xzc1 - ys, yzc1 + xs, zzc1 + c]])\\n\\n return self.multiply(mat, in_place)\\n\\n\\n def _rotate_x_neg_rad(self, rotation_rad, in_place=False):\\n self.rotate_x_rad(-rotation_rad, in_place)\\n\\n def _rotate_y_neg_rad(self, rotation_rad, in_place=False):\\n self.rotate_y_rad(-rotation_rad, in_place)\\n\\n def _rotate_z_neg_rad(self, rotation_rad, in_place=False):\\n self.rotate_z_rad(-rotation_rad, in_place)\\n\\n def rotate_x_rad(self, rotation_rad, in_place=False):\\n '''\\n Rotates the current rotation matrix around the x-axis\\n\\n Parameters\\n ----------\\n rotation_rad : float\\n rotation in radians\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_3x3 \\n self is returned if in_place is True\\n '''\\n if rotation_rad == 0: \\n return self if in_place else Matrix_3x3(self)\\n \\n c = np.math.cos(rotation_rad)\\n s = np.math.sin(rotation_rad)\\n\\n mat = np.array([\\n [1, 0, 0],\\n [0, c,-s],\\n [0, s, c]])\\n\\n return self.multiply(mat, in_place)\\n\\n def rotate_y_rad(self, rotation_rad, in_place=False):\\n '''\\n Rotates the current rotation matrix around the y-axis\\n\\n Parameters\\n ----------\\n rotation_rad : float\\n rotation in radians\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_3x3 \\n self is returned if in_place is True\\n '''\\n if rotation_rad == 0: \\n return self if in_place else Matrix_3x3(self)\\n \\n c = np.math.cos(rotation_rad)\\n s = np.math.sin(rotation_rad)\\n\\n mat = np.array([\\n [ c, 0, s],\\n [ 0, 1, 0],\\n [-s, 0, c]])\\n\\n return self.multiply(mat, in_place)\\n\\n def rotate_z_rad(self, rotation_rad, in_place=False):\\n '''\\n Rotates the current rotation matrix around the z-axis\\n\\n Parameters\\n ----------\\n rotation_rad : float\\n rotation in radians\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_3x3 \\n self is returned if in_place is True\\n '''\\n if rotation_rad == 0: \\n return self if in_place else Matrix_3x3(self)\\n \\n c = np.math.cos(rotation_rad)\\n s = np.math.sin(rotation_rad)\\n\\n mat = np.array([\\n [ c,-s, 0],\\n [ s, c, 0],\\n [ 0, 0, 1]])\\n\\n return self.multiply(mat, in_place)\\n\\n def rotate_x_deg(self, rotation_deg, in_place=False):\\n '''\\n Rotates the current rotation matrix around the x-axis\\n\\n Parameters\\n ----------\\n rotation_rad : float\\n rotation in degrees\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_3x3 \\n self is returned if in_place is True\\n '''\\n return self.rotate_x_rad(rotation_deg * (pi/180), in_place)\\n\\n def rotate_y_deg(self, rotation_deg, in_place=False):\\n '''\\n Rotates the current rotation matrix around the y-axis\\n\\n Parameters\\n ----------\\n rotation_rad : float\\n rotation in degrees\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_3x3 \\n self is returned if in_place is True\\n '''\\n return self.rotate_y_rad(rotation_deg * (pi/180), in_place)\\n\\n def rotate_z_deg(self, rotation_deg, in_place=False):\\n '''\\n Rotates the current rotation matrix around the z-axis\\n\\n Parameters\\n ----------\\n rotation_rad : float\\n rotation in degrees\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_3x3 \\n self is returned if in_place is True\\n '''\\n return self.rotate_z_rad(rotation_deg * (pi/180), in_place)\\n\\n def invert(self, in_place=False):\\n '''\\n Inverts the current rotation matrix\\n\\n Parameters\\n ----------\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_3x3 \\n self is returned if in_place is True\\n '''\\n\\n if in_place:\\n self.m = np.linalg.inv(self.m)\\n return self\\n else:\\n return Matrix_3x3(np.linalg.inv(self.m))\\n\\n def multiply(self,mat, in_place=False, reverse_order=False):\\n '''\\n Multiplies the current rotation matrix by mat\\n\\n Parameters\\n ----------\\n mat : Matrix_3x3 or array_like\\n multiplier matrix or 3D vector\\n in_place: bool, optional\\n - True: the internal matrix is changed in-place\\n - False: a new matrix is returned and the current one is not changed (default) \\n reverse_order: bool, optional\\n - False: self * mat\\n - True: mat * self\\n \\n Returns\\n -------\\n result : Matrix_3x3 | array_like\\n Matrix_3x3 is returned if mat is a matrix (self is returned if in_place is True); \\n a 3D vector is returned if mat is a vector\\n '''\\n # get array from matrix object or convert to numpy array (if needed) \\n mat = mat.m if type(mat) == Matrix_3x3 else np.asarray(mat)\\n\\n a,b = (mat, self.m) if reverse_order else (self.m, mat)\\n\\n if mat.ndim == 1: \\n return np.matmul(a, b) # multiplication by 3D vector\\n elif in_place:\\n np.matmul(a, b, self.m) # multiplication by matrix, in place\\n return self\\n else: # multiplication by matrix, return new Matrix_3x3\\n return Matrix_3x3(np.matmul(a, b))\"\n },\n {\n \"identifier\": \"Matrix_4x4\",\n \"path\": \"math_ops/Matrix_4x4.py\",\n \"snippet\": \"class Matrix_4x4():\\n\\n def __init__(self, matrix = None) -> None:\\n '''\\n Constructor examples:\\n a = Matrix_4x4( ) # create identity matrix\\n b = Matrix_4x4( [[1,1,1,1],[2,2,2,2],[3,3,3,3],[4,4,4,4]] ) # manually initialize matrix\\n c = Matrix_4x4( [1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4] ) # manually initialize matrix\\n d = Matrix_4x4( b ) # copy constructor\\n '''\\n if matrix is None:\\n self.m = np.identity(4)\\n elif type(matrix) == Matrix_4x4: \\n self.m = np.copy(matrix.m)\\n elif type(matrix) == Matrix_3x3: \\n self.m = np.identity(4)\\n self.m[0:3,0:3] = matrix.m\\n else:\\n self.m = np.asarray(matrix)\\n self.m.shape = (4,4) #reshape if needed, throw error if impossible\\n\\n\\n @classmethod\\n def from_translation(cls, translation_vec):\\n '''\\n Create transformation matrix from translation_vec translation\\n e.g. Matrix_4x4.from_translation((a,b,c))\\n output: [[1,0,0,a],[0,1,0,b],[0,0,1,c],[0,0,0,1]]\\n '''\\n mat = np.identity(4)\\n mat[0:3,3] = translation_vec\\n return cls(mat)\\n\\n @classmethod\\n def from_3x3_and_translation(cls, mat3x3:Matrix_3x3, translation_vec):\\n '''\\n Create transformation matrix from rotation matrix (3x3) and translation\\n e.g. Matrix_4x4.from_3x3_and_translation(r,(a,b,c)) \\n output: [[r00,r01,r02,a],[r10,r11,r12,b],[r20,r21,r22,c],[0,0,0,1]]\\n '''\\n mat = np.identity(4)\\n mat[0:3,0:3] = mat3x3.m\\n mat[0:3,3] = translation_vec\\n return cls(mat)\\n\\n def translate(self, translation_vec, in_place=False):\\n '''\\n Translates the current transformation matrix\\n\\n Parameters\\n ----------\\n translation_vec : array_like, length 3\\n translation vector\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place\\n * False: a new matrix is returned and the current one is not changed \\n\\n Returns\\n -------\\n result : Matrix_4x4 \\n self is returned if in_place is True\\n '''\\n vec = np.array([*translation_vec,1])# conversion to 4D vector\\n np.matmul(self.m, vec, out=vec) # compute only 4th column\\n\\n if in_place:\\n self.m[:,3] = vec\\n return self\\n else:\\n ret = Matrix_4x4(self.m)\\n ret.m[:,3] = vec\\n return ret\\n\\n\\n def get_translation(self):\\n ''' Get translation vector (x,y,z) '''\\n return self.m[0:3,3] # return view\\n\\n def get_x(self):\\n return self.m[0,3]\\n\\n def get_y(self):\\n return self.m[1,3]\\n\\n def get_z(self):\\n return self.m[2,3]\\n\\n def get_rotation_4x4(self):\\n ''' Get Matrix_4x4 without translation ''' \\n mat = Matrix_4x4(self)\\n mat.m[0:3,3] = 0\\n return mat\\n\\n def get_rotation(self):\\n ''' Get rotation Matrix_3x3 '''\\n return Matrix_3x3(self.m[0:3,0:3])\\n\\n def get_distance(self):\\n ''' Get translation vector length '''\\n return np.linalg.norm(self.m[0:3,3])\\n\\n def get_roll_deg(self):\\n ''' Get angle around the x-axis in degrees, Rotation order: RotZ*RotY*RotX=Rot '''\\n if self.m[2,1] == 0 and self.m[2,2] == 0: \\n return 180\\n return atan2(self.m[2,1], self.m[2,2]) * 180 / pi\\n\\n def get_pitch_deg(self):\\n ''' Get angle around the y-axis in degrees, Rotation order: RotZ*RotY*RotX=Rot '''\\n return atan2(-self.m[2,0], sqrt(self.m[2,1]*self.m[2,1] + self.m[2,2]*self.m[2,2])) * 180 / pi\\n\\n def get_yaw_deg(self):\\n ''' Get angle around the z-axis in degrees, Rotation order: RotZ*RotY*RotX=Rot '''\\n if self.m[1,0] == 0 and self.m[0,0] == 0: \\n return atan2(self.m[0,1], self.m[1,1]) * 180 / pi\\n return atan2(self.m[1,0], self.m[0,0]) * 180 / pi\\n \\n def get_inclination_deg(self):\\n ''' Get inclination of z-axis in relation to reference z-axis '''\\n return 90 - (asin(np.clip(self.m[2,2],-1,1)) * 180 / pi)\\n\\n def rotate_deg(self, rotation_vec, rotation_deg, in_place=False):\\n '''\\n Rotates the current transformation matrix\\n\\n Parameters\\n ----------\\n rotation_vec : array_like, length 3\\n rotation vector\\n rotation_rad : float\\n rotation in degrees\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_4x4 \\n self is returned if in_place is True\\n '''\\n return self.rotate_rad(rotation_vec, rotation_deg * (pi/180) , in_place)\\n\\n \\n def rotate_rad(self, rotation_vec, rotation_rad, in_place=False):\\n '''\\n Rotates the current transformation matrix\\n\\n Parameters\\n ----------\\n rotation_vec : array_like, length 3\\n rotation vector\\n rotation_rad : float\\n rotation in radians\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_4x4 \\n self is returned if in_place is True\\n '''\\n\\n if rotation_rad == 0: \\n return self if in_place else Matrix_4x4(self)\\n\\n # shortcuts for rotation around 1 axis\\n if rotation_vec[0]==0:\\n if rotation_vec[1]==0:\\n if rotation_vec[2]==1:\\n return self.rotate_z_rad(rotation_rad, in_place)\\n elif rotation_vec[2]==-1:\\n return self.rotate_z_rad(-rotation_rad, in_place)\\n elif rotation_vec[2]==0:\\n if rotation_vec[1]==1:\\n return self.rotate_y_rad(rotation_rad, in_place)\\n elif rotation_vec[1]==-1:\\n return self.rotate_y_rad(-rotation_rad, in_place)\\n elif rotation_vec[1]==0 and rotation_vec[2]==0:\\n if rotation_vec[0]==1:\\n return self.rotate_x_rad(rotation_rad, in_place)\\n elif rotation_vec[0]==-1:\\n return self.rotate_x_rad(-rotation_rad, in_place)\\n \\n c = np.math.cos(rotation_rad)\\n c1 = 1 - c\\n s = np.math.sin(rotation_rad)\\n x = rotation_vec[0]\\n y = rotation_vec[1]\\n z = rotation_vec[2]\\n xxc1 = x * x * c1\\n yyc1 = y * y * c1\\n zzc1 = z * z * c1\\n xyc1 = x * y * c1\\n xzc1 = x * z * c1\\n yzc1 = y * z * c1\\n xs = x * s\\n ys = y * s\\n zs = z * s\\n\\n mat = np.array([\\n [xxc1 + c, xyc1 - zs, xzc1 + ys, 0],\\n [xyc1 + zs, yyc1 + c, yzc1 - xs, 0],\\n [xzc1 - ys, yzc1 + xs, zzc1 + c, 0],\\n [0, 0, 0, 1]])\\n\\n return self.multiply(mat, in_place)\\n\\n\\n def rotate_x_rad(self, rotation_rad, in_place=False):\\n '''\\n Rotates the current transformation matrix around the x-axis\\n\\n Parameters\\n ----------\\n rotation_rad : float\\n rotation in radians\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_4x4 \\n self is returned if in_place is True\\n '''\\n if rotation_rad == 0: \\n return self if in_place else Matrix_4x4(self)\\n \\n c = np.math.cos(rotation_rad)\\n s = np.math.sin(rotation_rad)\\n\\n mat = np.array([\\n [1, 0, 0, 0],\\n [0, c,-s, 0],\\n [0, s, c, 0],\\n [0, 0, 0, 1]])\\n\\n return self.multiply(mat, in_place)\\n\\n def rotate_y_rad(self, rotation_rad, in_place=False):\\n '''\\n Rotates the current transformation matrix around the y-axis\\n\\n Parameters\\n ----------\\n rotation_rad : float\\n rotation in radians\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_4x4 \\n self is returned if in_place is True\\n '''\\n if rotation_rad == 0: \\n return self if in_place else Matrix_4x4(self)\\n \\n c = np.math.cos(rotation_rad)\\n s = np.math.sin(rotation_rad)\\n\\n mat = np.array([\\n [ c, 0, s, 0],\\n [ 0, 1, 0, 0],\\n [-s, 0, c, 0],\\n [ 0, 0, 0, 1]])\\n\\n return self.multiply(mat, in_place)\\n\\n def rotate_z_rad(self, rotation_rad, in_place=False):\\n '''\\n Rotates the current transformation matrix around the z-axis\\n\\n Parameters\\n ----------\\n rotation_rad : float\\n rotation in radians\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_4x4 \\n self is returned if in_place is True\\n '''\\n if rotation_rad == 0: \\n return self if in_place else Matrix_4x4(self)\\n \\n c = np.math.cos(rotation_rad)\\n s = np.math.sin(rotation_rad)\\n\\n mat = np.array([\\n [ c,-s, 0, 0],\\n [ s, c, 0, 0],\\n [ 0, 0, 1, 0],\\n [ 0, 0, 0, 1]])\\n\\n return self.multiply(mat, in_place)\\n\\n def rotate_x_deg(self, rotation_deg, in_place=False):\\n '''\\n Rotates the current transformation matrix around the x-axis\\n\\n Parameters\\n ----------\\n rotation_rad : float\\n rotation in degrees\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_4x4 \\n self is returned if in_place is True\\n '''\\n return self.rotate_x_rad(rotation_deg * (pi/180), in_place)\\n\\n def rotate_y_deg(self, rotation_deg, in_place=False):\\n '''\\n Rotates the current transformation matrix around the y-axis\\n\\n Parameters\\n ----------\\n rotation_rad : float\\n rotation in degrees\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_4x4 \\n self is returned if in_place is True\\n '''\\n return self.rotate_y_rad(rotation_deg * (pi/180), in_place)\\n\\n def rotate_z_deg(self, rotation_deg, in_place=False):\\n '''\\n Rotates the current transformation matrix around the z-axis\\n\\n Parameters\\n ----------\\n rotation_rad : float\\n rotation in degrees\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_4x4 \\n self is returned if in_place is True\\n '''\\n return self.rotate_z_rad(rotation_deg * (pi/180), in_place)\\n\\n def invert(self, in_place=False):\\n '''\\n Inverts the current transformation matrix\\n\\n Parameters\\n ----------\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed \\n \\n Returns\\n -------\\n result : Matrix_4x4 \\n self is returned if in_place is True\\n '''\\n\\n if in_place:\\n self.m = np.linalg.inv(self.m)\\n return self\\n else:\\n return Matrix_4x4(np.linalg.inv(self.m))\\n\\n def multiply(self,mat, in_place=False):\\n '''\\n Multiplies the current transformation matrix by mat\\n\\n Parameters\\n ----------\\n mat : Matrix_4x4 or array_like\\n multiplier matrix or 3D vector\\n in_place: bool, optional\\n * True: the internal matrix is changed in-place (default)\\n * False: a new matrix is returned and the current one is not changed (if mat is a 4x4 matrix)\\n \\n Returns\\n -------\\n result : Matrix_4x4 | array_like\\n Matrix_4x4 is returned if mat is a matrix (self is returned if in_place is True); \\n a 3D vector is returned if mat is a vector\\n '''\\n if type(mat) == Matrix_4x4: \\n mat = mat.m\\n else:\\n mat = np.asarray(mat) # conversion to array, if needed\\n if mat.ndim == 1: # multiplication by 3D vector\\n vec = np.append(mat,1) # conversion to 4D vector\\n return np.matmul(self.m, vec)[0:3] # conversion to 3D vector\\n\\n if in_place:\\n np.matmul(self.m, mat, self.m)\\n return self\\n else:\\n return Matrix_4x4(np.matmul(self.m, mat))\\n\\n def __call__(self,mat, is_spherical=False):\\n '''\\n Multiplies the current transformation matrix by mat and returns a new matrix or vector\\n\\n Parameters\\n ----------\\n mat : Matrix_4x4 or array_like\\n multiplier matrix or 3D vector\\n is_spherical : bool\\n only relevant if mat is a 3D vector, True if it uses spherical coordinates\\n \\n Returns\\n -------\\n result : Matrix_4x4 | array_like\\n Matrix_4x4 is returned if mat is a matrix; \\n a 3D vector is returned if mat is a vector\\n '''\\n\\n if is_spherical and mat.ndim == 1: mat = M.deg_sph2cart(mat)\\n return self.multiply(mat,False)\"\n },\n {\n \"identifier\": \"Body_Part\",\n \"path\": \"world/commons/Body_Part.py\",\n \"snippet\": \"class Body_Part():\\n def __init__(self, mass) -> None:\\n self.mass = float(mass)\\n self.joints = []\\n self.transform = Matrix_4x4() # body part to head transformation matrix\"\n },\n {\n \"identifier\": \"Joint_Info\",\n \"path\": \"world/commons/Joint_Info.py\",\n \"snippet\": \"class Joint_Info():\\n def __init__(self, xml_element) -> None:\\n self.perceptor = xml_element.attrib['perceptor']\\n self.effector = xml_element.attrib['effector']\\n self.axes = np.array([\\n float(xml_element.attrib['xaxis']), \\n float(xml_element.attrib['yaxis']), \\n float(xml_element.attrib['zaxis'])])\\n self.min = int(xml_element.attrib['min'])\\n self.max = int(xml_element.attrib['max'])\\n\\n self.anchor0_part = xml_element[0].attrib['part']\\n self.anchor0_axes = np.array([\\n float(xml_element[0].attrib['y']), \\n float(xml_element[0].attrib['x']), \\n float(xml_element[0].attrib['z'])]) #x and y axes are switched\\n\\n self.anchor1_part = xml_element[1].attrib['part']\\n self.anchor1_axes_neg = np.array([\\n -float(xml_element[1].attrib['y']), \\n -float(xml_element[1].attrib['x']), \\n -float(xml_element[1].attrib['z'])]) #x and y axes are switched\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"from collections import deque\nfrom math import atan, pi, sqrt, tan\nfrom math_ops.Math_Ops import Math_Ops as M\nfrom math_ops.Matrix_3x3 import Matrix_3x3\nfrom math_ops.Matrix_4x4 import Matrix_4x4\nfrom world.commons.Body_Part import Body_Part\nfrom world.commons.Joint_Info import Joint_Info\nimport numpy as np\nimport xml.etree.ElementTree as xmlp"},"token_num":{"kind":"number","value":12934,"string":"12,934"},"cropped_code":{"kind":"string","value":"\nclass Robot():\n STEPTIME = 0.02 # Fixed step time\n VISUALSTEP = 0.04 # Fixed visual step time\n SQ_STEPTIME = STEPTIME * STEPTIME\n GRAVITY = np.array([0,0,-9.81])\n IMU_DECAY = 0.996 #IMU's velocity decay\n \n #------------------ constants to force symmetry in joints/effectors\n\n MAP_PERCEPTOR_TO_INDEX = {\"hj1\":0, \"hj2\":1, \"llj1\":2, \"rlj1\":3,\n \"llj2\":4, \"rlj2\":5, \"llj3\":6, \"rlj3\":7,\n \"llj4\":8, \"rlj4\":9, \"llj5\":10,\"rlj5\":11,\n \"llj6\":12,\"rlj6\":13,\"laj1\":14,\"raj1\":15,\n \"laj2\":16,\"raj2\":17,\"laj3\":18,\"raj3\":19,\n \"laj4\":20,\"raj4\":21,\"llj7\":22,\"rlj7\":23 }\n\n # Fix symmetry issues 1a/4 (identification) \n FIX_PERCEPTOR_SET = {'rlj2','rlj6','raj2','laj3','laj4'}\n FIX_INDICES_LIST = [5,13,17,18,20]\n\n # Recommended height for unofficial beam (near ground)\n BEAM_HEIGHTS = [0.4, 0.43, 0.4, 0.46, 0.4]\n\n\n def __init__(self, unum:int, robot_type:int) -> None:\n robot_xml = \"nao\"+str(robot_type)+\".xml\" # Typical NAO file name\n self.type = robot_type\n self.beam_height = Robot.BEAM_HEIGHTS[robot_type]\n self.no_of_joints = 24 if robot_type == 4 else 22 \n\n #Fix symmetry issues 1b/4 (identification) \n self.FIX_EFFECTOR_MASK = np.ones(self.no_of_joints)\n self.FIX_EFFECTOR_MASK[Robot.FIX_INDICES_LIST] = -1\n\n self.body_parts = dict() # keys='body part names' (given by the robot's XML), values='Body_Part objects'\n self.unum = unum # Robot's uniform number\n self.gyro = np.zeros(3) # Angular velocity along the three axes of freedom of the robot's torso (deg/s)\n self.acc = np.zeros(3) # Proper acceleration along the three axes of freedom of the robot's torso (m/s2)\n self.frp = dict() # foot \"lf\"/\"rf\", toe \"lf1\"/\"rf1\" resistance perceptor (relative [p]oint of origin + [f]orce vector) e.g. {\"lf\":(px,py,pz,fx,fy,fz)}\n self.feet_toes_last_touch = {\"lf\":0,\"rf\":0,\"lf1\":0,\"rf1\":0} # foot \"lf\"/\"rf\", toe \"lf1\"/\"rf1\" World.time_local_ms when foot/toe last touched any surface\n self.feet_toes_are_touching = {\"lf\":False,\"rf\":False,\"lf1\":False,\"rf1\":False} # foot \"lf\"/\"rf\", toe \"lf1\"/\"rf1\" True if touching in last received server message\n self.fwd_kinematics_list = None # List of body parts, ordered according to dependencies\n self.rel_cart_CoM_position = np.zeros(3) # Center of Mass position, relative to head, in cartesian coordinates (m)\n\n # Joint variables are optimized for performance / array operations\n self.joints_position = np.zeros(self.no_of_joints) # Joints' angular position (deg)\n self.joints_speed = np.zeros(self.no_of_joints) # Joints' angular speed (rad/s)\n self.joints_target_speed = np.zeros(self.no_of_joints) # Joints' target speed (rad/s) (max: 6.1395 rad/s, see rcssserver3d/data/rsg/agent/nao/hingejoint.rsg)\n self.joints_target_last_speed = np.zeros(self.no_of_joints) # Joints' last target speed (rad/s) (max: 6.1395 rad/s, see rcssserver3d/data/rsg/agent/nao/hingejoint.rsg)\n self.joints_info = [None] * self.no_of_joints # Joints' constant information (see class Joint_Info)\n self.joints_transform = [Matrix_4x4() for _ in range(self.no_of_joints)] # Joints' transformation matrix\n\n # Localization variables relative to head\n self.loc_head_to_field_transform = Matrix_4x4() # Transformation matrix from head to field\n self.loc_field_to_head_transform = Matrix_4x4() # Transformation matrix from field to head\n"},"all_code":{"kind":"string","value":"\nclass Robot():\n STEPTIME = 0.02 # Fixed step time\n VISUALSTEP = 0.04 # Fixed visual step time\n SQ_STEPTIME = STEPTIME * STEPTIME\n GRAVITY = np.array([0,0,-9.81])\n IMU_DECAY = 0.996 #IMU's velocity decay\n \n #------------------ constants to force symmetry in joints/effectors\n\n MAP_PERCEPTOR_TO_INDEX = {\"hj1\":0, \"hj2\":1, \"llj1\":2, \"rlj1\":3,\n \"llj2\":4, \"rlj2\":5, \"llj3\":6, \"rlj3\":7,\n \"llj4\":8, \"rlj4\":9, \"llj5\":10,\"rlj5\":11,\n \"llj6\":12,\"rlj6\":13,\"laj1\":14,\"raj1\":15,\n \"laj2\":16,\"raj2\":17,\"laj3\":18,\"raj3\":19,\n \"laj4\":20,\"raj4\":21,\"llj7\":22,\"rlj7\":23 }\n\n # Fix symmetry issues 1a/4 (identification) \n FIX_PERCEPTOR_SET = {'rlj2','rlj6','raj2','laj3','laj4'}\n FIX_INDICES_LIST = [5,13,17,18,20]\n\n # Recommended height for unofficial beam (near ground)\n BEAM_HEIGHTS = [0.4, 0.43, 0.4, 0.46, 0.4]\n\n\n def __init__(self, unum:int, robot_type:int) -> None:\n robot_xml = \"nao\"+str(robot_type)+\".xml\" # Typical NAO file name\n self.type = robot_type\n self.beam_height = Robot.BEAM_HEIGHTS[robot_type]\n self.no_of_joints = 24 if robot_type == 4 else 22 \n\n #Fix symmetry issues 1b/4 (identification) \n self.FIX_EFFECTOR_MASK = np.ones(self.no_of_joints)\n self.FIX_EFFECTOR_MASK[Robot.FIX_INDICES_LIST] = -1\n\n self.body_parts = dict() # keys='body part names' (given by the robot's XML), values='Body_Part objects'\n self.unum = unum # Robot's uniform number\n self.gyro = np.zeros(3) # Angular velocity along the three axes of freedom of the robot's torso (deg/s)\n self.acc = np.zeros(3) # Proper acceleration along the three axes of freedom of the robot's torso (m/s2)\n self.frp = dict() # foot \"lf\"/\"rf\", toe \"lf1\"/\"rf1\" resistance perceptor (relative [p]oint of origin + [f]orce vector) e.g. {\"lf\":(px,py,pz,fx,fy,fz)}\n self.feet_toes_last_touch = {\"lf\":0,\"rf\":0,\"lf1\":0,\"rf1\":0} # foot \"lf\"/\"rf\", toe \"lf1\"/\"rf1\" World.time_local_ms when foot/toe last touched any surface\n self.feet_toes_are_touching = {\"lf\":False,\"rf\":False,\"lf1\":False,\"rf1\":False} # foot \"lf\"/\"rf\", toe \"lf1\"/\"rf1\" True if touching in last received server message\n self.fwd_kinematics_list = None # List of body parts, ordered according to dependencies\n self.rel_cart_CoM_position = np.zeros(3) # Center of Mass position, relative to head, in cartesian coordinates (m)\n\n # Joint variables are optimized for performance / array operations\n self.joints_position = np.zeros(self.no_of_joints) # Joints' angular position (deg)\n self.joints_speed = np.zeros(self.no_of_joints) # Joints' angular speed (rad/s)\n self.joints_target_speed = np.zeros(self.no_of_joints) # Joints' target speed (rad/s) (max: 6.1395 rad/s, see rcssserver3d/data/rsg/agent/nao/hingejoint.rsg)\n self.joints_target_last_speed = np.zeros(self.no_of_joints) # Joints' last target speed (rad/s) (max: 6.1395 rad/s, see rcssserver3d/data/rsg/agent/nao/hingejoint.rsg)\n self.joints_info = [None] * self.no_of_joints # Joints' constant information (see class Joint_Info)\n self.joints_transform = [Matrix_4x4() for _ in range(self.no_of_joints)] # Joints' transformation matrix\n\n # Localization variables relative to head\n self.loc_head_to_field_transform = Matrix_4x4() # Transformation matrix from head to field\n self.loc_field_to_head_transform = Matrix_4x4() # Transformation matrix from field to head"},"next_line":{"kind":"string","value":" self.loc_rotation_head_to_field = Matrix_3x3() # Rotation matrix from head to field"},"gold_snippet_index":{"kind":"number","value":1,"string":"1"},"created_at":{"kind":"string","value":"2023-12-16 23:40:23+00:00"},"level":{"kind":"string","value":"16k"}}},{"rowIdx":4197,"cells":{"repo_name":{"kind":"string","value":"Sam-Izdat/tinycio"},"file_path":{"kind":"string","value":"src/tinycio/util/colorutil.py"},"context":{"kind":"list like","value":[{"identifier":"Float2","path":"src/tinycio/numerics/vector.py","snippet":"class Float2(np.ndarray):\n \"\"\"\n Float2 type using numpy.ndarray.\n \"\"\"\n def __new__(cls, *args):\n if len(args) == 1:\n if isinstance(args[0], list) or isinstance(args[0], tuple):\n assert len(args[0]) == 2, \"list/tuple must have 2 components\"\n arr = np.asarray([args[0][0], args[0][1]], dtype=np.float32).view(cls)\n elif isinstance(args[0], np.ndarray):\n assert len(args[0].squeeze().shape) == 1 and args[0].shape[0] == 2, \\\n \"numpy array must be sized [C=2] or [C=2, H=1, W=1]\"\n arr = np.asarray(args[0].squeeze(), dtype=np.float32).view(cls)\n elif torch.is_tensor(args[0]):\n assert len(args[0].squeeze().size()) == 1 and args[0].size(0) == 2, \\\n \"torch tensor must be sized [C=2] or [C=2, H=1, W=1]\"\n value = args[0].squeeze().float().cpu()\n arr = np.asarray([value[0].item(), value[1].item()], dtype=np.float32).view(cls)\n else:\n value = float(args[0])\n arr = np.asarray([value, value], dtype=np.float32).view(cls)\n elif len(args) == 2:\n arr = np.asarray(args, dtype=np.float32).view(cls)\n else: \n raise TypeError(\"Float2 only accepts 1 or 2 arguments.\")\n return arr\n\n def list(self) -> list:\n \"\"\"Returns values as Python list\"\"\"\n return [self[0], self[1]]\n\n def tuple(self) -> tuple:\n \"\"\"Returns values as Python tuple\"\"\"\n return (self[0], self[1])\n\n @property\n def x(self) -> float:\n return self[0]\n @x.setter\n def x(self, value):\n self[0] = value\n @property\n def y(self) -> float:\n return self[1]\n @y.setter\n def y(self, value):\n self[1] = value\n @property\n def r(self) -> float:\n return self[0]\n @r.setter\n def r(self, value):\n self[0] = value\n @property\n def g(self) -> float:\n return self[1]\n @g.setter\n def g(self, value):\n self[1] = value\n\n @staticmethod\n def zero():\n \"\"\"Returns numeric type filled with zero values\"\"\"\n return Float2(0., 0.)\n @staticmethod\n def one():\n \"\"\"Returns numeric type filled with one values\"\"\"\n return Float2(1., 1.)\n @staticmethod\n def x_axis():\n \"\"\"Returns numeric type with x-axis set to 1 and all others to 0\"\"\"\n return Float2(1., 0.)\n @staticmethod\n def y_axis():\n \"\"\"Returns numeric type with y-axis set to 1 and all others to 0\"\"\"\n return Float2(0., 1.)\n\n @property\n def xx(self): return Float2(self.x, self.x)\n @property\n def xy(self): return self\n @property\n def yx(self): return Float2(self.y, self.x)\n @property\n def yy(self): return Float2(self.y, self.y)\n\n @property\n def rr(self): return Float2(self.r, self.r)\n @property\n def rg(self): return self\n @property\n def gr(self): return Float2(self.g, self.r)\n @property\n def gg(self): return Float2(self.g, self.g)\n\n @property\n def xxx(self): return Float3(self.x, self.x, self.x)\n @property\n def xxy(self): return Float3(self.x, self.x, self.y)\n @property\n def xyx(self): return Float3(self.x, self.y, self.x)\n @property\n def xyy(self): return Float3(self.x, self.y, self.y)\n @property\n def yxx(self): return Float3(self.y, self.x, self.x)\n @property\n def yxy(self): return Float3(self.y, self.x, self.y)\n @property\n def yyx(self): return Float3(self.y, self.y, self.x)\n @property\n def yyy(self): return Float3(self.y, self.y, self.y)\n\n @property\n def rrr(self): return Float3(self.r, self.r, self.r)\n @property\n def rrg(self): return Float3(self.r, self.r, self.g)\n @property\n def rgr(self): return Float3(self.r, self.g, self.r)\n @property\n def rgg(self): return Float3(self.r, self.g, self.g)\n @property\n def grr(self): return Float3(self.g, self.r, self.r)\n @property\n def grg(self): return Float3(self.g, self.r, self.g)\n @property\n def ggr(self): return Float3(self.g, self.g, self.r)\n @property\n def ggg(self): return Float3(self.g, self.g, self.g)\n\n @property\n def xxxx(self): return Float4(self.x, self.x, self.x, self.x)\n @property\n def xxxy(self): return Float4(self.x, self.x, self.x, self.y)\n @property\n def xxyx(self): return Float4(self.x, self.x, self.y, self.x)\n @property\n def xxyy(self): return Float4(self.x, self.x, self.y, self.y)\n @property\n def xyxx(self): return Float4(self.x, self.y, self.x, self.x)\n @property\n def xyxy(self): return Float4(self.x, self.y, self.x, self.y)\n @property\n def xyyx(self): return Float4(self.x, self.y, self.y, self.x)\n @property\n def xyyy(self): return Float4(self.x, self.y, self.y, self.y)\n @property\n def yxxx(self): return Float4(self.y, self.x, self.x, self.x)\n @property\n def yxxy(self): return Float4(self.y, self.x, self.x, self.y)\n @property\n def yxyx(self): return Float4(self.y, self.x, self.y, self.x)\n @property\n def yxyy(self): return Float4(self.y, self.x, self.y, self.y)\n @property\n def yyxx(self): return Float4(self.y, self.y, self.x, self.x)\n @property\n def yyxy(self): return Float4(self.y, self.y, self.x, self.y)\n @property\n def yyyx(self): return Float4(self.y, self.y, self.y, self.x)\n @property\n def yyyy(self): return Float4(self.y, self.y, self.y, self.y)\n\n @property\n def rrrr(self): return Float4(self.r, self.r, self.r, self.r)\n @property\n def rrrg(self): return Float4(self.r, self.r, self.r, self.g)\n @property\n def rrgr(self): return Float4(self.r, self.r, self.g, self.r)\n @property\n def rrgg(self): return Float4(self.r, self.r, self.g, self.g)\n @property\n def rgrr(self): return Float4(self.r, self.g, self.r, self.r)\n @property\n def rgrg(self): return Float4(self.r, self.g, self.r, self.g)\n @property\n def rggr(self): return Float4(self.r, self.g, self.g, self.r)\n @property\n def rggg(self): return Float4(self.r, self.g, self.g, self.g)\n @property\n def grrr(self): return Float4(self.g, self.r, self.r, self.r)\n @property\n def grrg(self): return Float4(self.g, self.r, self.r, self.g)\n @property\n def grgr(self): return Float4(self.g, self.r, self.g, self.r)\n @property\n def grgg(self): return Float4(self.g, self.r, self.g, self.g)\n @property\n def ggrr(self): return Float4(self.g, self.g, self.r, self.r)\n @property\n def ggrg(self): return Float4(self.g, self.g, self.r, self.g)\n @property\n def gggr(self): return Float4(self.g, self.g, self.g, self.r)\n @property\n def gggg(self): return Float4(self.g, self.g, self.g, self.g)"},{"identifier":"Float3","path":"src/tinycio/numerics/vector.py","snippet":"class Float3(np.ndarray):\n \"\"\"\n Float3 type using numpy.ndarray.\n \"\"\"\n def __new__(cls, *args):\n if len(args) == 1:\n if isinstance(args[0], list) or isinstance(args[0], tuple):\n assert len(args[0]) == 3, \"list/tuple must have 3 components\"\n arr = np.asarray([args[0][0], args[0][1], args[0][2]], dtype=np.float32).view(cls)\n elif isinstance(args[0], np.ndarray):\n assert len(args[0].squeeze().shape) == 1 and args[0].shape[0] == 3, \\\n \"numpy array must be sized [C=3] or [C=3, H=1, W=1]\"\n arr = np.asarray(args[0].squeeze(), dtype=np.float32).view(cls)\n elif torch.is_tensor(args[0]):\n assert len(args[0].squeeze().size()) == 1 and args[0].size(0) == 3, \\\n \"torch tensor must be sized [C=3] or [C=3, H=1, W=1]\"\n value = args[0].squeeze().float().cpu()\n arr = np.asarray([value[0].item(), value[1].item(), value[2].item()], dtype=np.float32).view(cls)\n else:\n value = float(args[0])\n arr = np.asarray([value, value, value], dtype=np.float32).view(cls)\n elif len(args) == 3:\n arr = np.asarray(args, dtype=np.float32).view(cls)\n else: \n raise TypeError(\"Float3 only accepts 1 or 3 arguments.\")\n return arr\n\n def list(self) -> list:\n \"\"\"Returns values as Python list\"\"\"\n return [self[0], self[1], self[2]]\n\n def tuple(self) -> tuple:\n \"\"\"Returns values as Python tuple\"\"\"\n return (self[0], self[1], self[2])\n\n @property\n def x(self) -> float:\n return self[0]\n @x.setter\n def x(self, value):\n self[0] = value\n @property\n def y(self) -> float:\n return self[1]\n @y.setter\n def y(self, value):\n self[1] = value\n @property\n def z(self) -> float:\n return self[2]\n @z.setter\n def z(self, value):\n self[2] = value\n @property\n def r(self) -> float:\n return self[0]\n @r.setter\n def r(self, value):\n self[0] = value\n @property\n def g(self) -> float:\n return self[1]\n @g.setter\n def g(self, value):\n self[1] = value\n @property\n def b(self) -> float:\n return self[2]\n @b.setter\n def b(self, value):\n self[2] = value\n @staticmethod\n def zero():\n \"\"\"Returns numeric type filled with zero values\"\"\"\n return Float3(0., 0., 0.)\n @staticmethod\n def one():\n \"\"\"Returns numeric type filled with one values\"\"\"\n return Float3(1., 1., 1.)\n @staticmethod\n def x_axis():\n \"\"\"Returns numeric type with x-axis set to 1 and all others to 0\"\"\"\n return Float3(1., 0., 0.)\n @staticmethod\n def y_axis():\n \"\"\"Returns numeric type with y-axis set to 1 and all others to 0\"\"\"\n return Float3(0., 1., 0.)\n @staticmethod\n def z_axis():\n \"\"\"Returns numeric type with z-axis set to 1 and all others to 0\"\"\"\n return Float3(0., 0., 1.)\n\n @property\n def xx(self): return Float2(self.x, self.x)\n @property\n def xy(self): return Float2(self.x, self.y)\n @property\n def xz(self): return Float2(self.x, self.z)\n @property\n def yx(self): return Float2(self.y, self.x)\n @property\n def yy(self): return Float2(self.y, self.y)\n @property\n def yz(self): return Float2(self.y, self.z)\n @property\n def zx(self): return Float2(self.z, self.x)\n @property\n def zy(self): return Float2(self.z, self.y)\n @property\n def zz(self): return Float2(self.z, self.z)\n\n @property\n def rr(self): return Float2(self.r, self.r)\n @property\n def rg(self): return Float2(self.r, self.g)\n @property\n def rb(self): return Float2(self.r, self.b)\n @property\n def gr(self): return Float2(self.g, self.r)\n @property\n def gg(self): return Float2(self.g, self.g)\n @property\n def gb(self): return Float2(self.g, self.b)\n @property\n def br(self): return Float2(self.b, self.r)\n @property\n def bg(self): return Float2(self.b, self.g)\n @property\n def bb(self): return Float2(self.b, self.b)\n\n @property\n def xxx(self): return Float3(self.x, self.x, self.x)\n @property\n def xxy(self): return Float3(self.x, self.x, self.y)\n @property\n def xxz(self): return Float3(self.x, self.x, self.z)\n @property\n def xyx(self): return Float3(self.x, self.y, self.x)\n @property\n def xyy(self): return Float3(self.x, self.y, self.y)\n @property\n def xyz(self): return self\n @property\n def xzx(self): return Float3(self.x, self.z, self.x)\n @property\n def xzy(self): return Float3(self.x, self.z, self.y)\n @property\n def xzz(self): return Float3(self.x, self.z, self.z)\n @property\n def yxx(self): return Float3(self.y, self.x, self.x)\n @property\n def yxy(self): return Float3(self.y, self.x, self.y)\n @property\n def yxz(self): return Float3(self.y, self.x, self.z)\n @property\n def yyx(self): return Float3(self.y, self.y, self.x)\n @property\n def yyy(self): return Float3(self.y, self.y, self.y)\n @property\n def yyz(self): return Float3(self.y, self.y, self.z)\n @property\n def yzx(self): return Float3(self.y, self.z, self.x)\n @property\n def yzy(self): return Float3(self.y, self.z, self.y)\n @property\n def yzz(self): return Float3(self.y, self.z, self.z)\n @property\n def zxx(self): return Float3(self.z, self.x, self.x)\n @property\n def zxy(self): return Float3(self.z, self.x, self.y)\n @property\n def zxz(self): return Float3(self.z, self.x, self.z)\n @property\n def zyx(self): return Float3(self.z, self.y, self.x)\n @property\n def zyy(self): return Float3(self.z, self.y, self.y)\n @property\n def zyz(self): return Float3(self.z, self.y, self.z)\n @property\n def zzx(self): return Float3(self.z, self.z, self.x)\n @property\n def zzy(self): return Float3(self.z, self.z, self.y)\n @property\n def zzz(self): return Float3(self.z, self.z, self.z)\n\n @property\n def rrr(self): return Float3(self.r, self.r, self.r)\n @property\n def rrg(self): return Float3(self.r, self.r, self.g)\n @property\n def rrb(self): return Float3(self.r, self.r, self.b)\n @property\n def rgr(self): return Float3(self.r, self.g, self.r)\n @property\n def rgg(self): return Float3(self.r, self.g, self.g)\n @property\n def rgb(self): return self\n @property\n def rbr(self): return Float3(self.r, self.b, self.r)\n @property\n def rbg(self): return Float3(self.r, self.b, self.g)\n @property\n def rbb(self): return Float3(self.r, self.b, self.b)\n @property\n def grr(self): return Float3(self.g, self.r, self.r)\n @property\n def grg(self): return Float3(self.g, self.r, self.g)\n @property\n def grb(self): return Float3(self.g, self.r, self.b)\n @property\n def ggr(self): return Float3(self.g, self.g, self.r)\n @property\n def ggg(self): return Float3(self.g, self.g, self.g)\n @property\n def ggb(self): return Float3(self.g, self.g, self.b)\n @property\n def gbr(self): return Float3(self.g, self.b, self.r)\n @property\n def gbg(self): return Float3(self.g, self.b, self.g)\n @property\n def gbb(self): return Float3(self.g, self.b, self.b)\n @property\n def brr(self): return Float3(self.b, self.r, self.r)\n @property\n def brg(self): return Float3(self.b, self.r, self.g)\n @property\n def brb(self): return Float3(self.b, self.r, self.b)\n @property\n def bgr(self): return Float3(self.b, self.g, self.r)\n @property\n def bgg(self): return Float3(self.b, self.g, self.g)\n @property\n def bgb(self): return Float3(self.b, self.g, self.b)\n @property\n def bbr(self): return Float3(self.b, self.b, self.r)\n @property\n def bbg(self): return Float3(self.b, self.b, self.g)\n @property\n def bbb(self): return Float3(self.b, self.b, self.b)\n\n @property\n def xxxx(self): return Float4(self.x, self.x, self.x, self.x)\n @property\n def xxxy(self): return Float4(self.x, self.x, self.x, self.y)\n @property\n def xxxz(self): return Float4(self.x, self.x, self.x, self.z)\n @property\n def xxyx(self): return Float4(self.x, self.x, self.y, self.x)\n @property\n def xxyy(self): return Float4(self.x, self.x, self.y, self.y)\n @property\n def xxyz(self): return Float4(self.x, self.x, self.y, self.z)\n @property\n def xxzx(self): return Float4(self.x, self.x, self.z, self.x)\n @property\n def xxzy(self): return Float4(self.x, self.x, self.z, self.y)\n @property\n def xxzz(self): return Float4(self.x, self.x, self.z, self.z)\n @property\n def xyxx(self): return Float4(self.x, self.y, self.x, self.x)\n @property\n def xyxy(self): return Float4(self.x, self.y, self.x, self.y)\n @property\n def xyxz(self): return Float4(self.x, self.y, self.x, self.z)\n @property\n def xyyx(self): return Float4(self.x, self.y, self.y, self.x)\n @property\n def xyyy(self): return Float4(self.x, self.y, self.y, self.y)\n @property\n def xyyz(self): return Float4(self.x, self.y, self.y, self.z)\n @property\n def xyzx(self): return Float4(self.x, self.y, self.z, self.x)\n @property\n def xyzy(self): return Float4(self.x, self.y, self.z, self.y)\n @property\n def xyzz(self): return Float4(self.x, self.y, self.z, self.z)\n @property\n def xzxx(self): return Float4(self.x, self.z, self.x, self.x)\n @property\n def xzxy(self): return Float4(self.x, self.z, self.x, self.y)\n @property\n def xzxz(self): return Float4(self.x, self.z, self.x, self.z)\n @property\n def xzyx(self): return Float4(self.x, self.z, self.y, self.x)\n @property\n def xzyy(self): return Float4(self.x, self.z, self.y, self.y)\n @property\n def xzyz(self): return Float4(self.x, self.z, self.y, self.z)\n @property\n def xzzx(self): return Float4(self.x, self.z, self.z, self.x)\n @property\n def xzzy(self): return Float4(self.x, self.z, self.z, self.y)\n @property\n def xzzz(self): return Float4(self.x, self.z, self.z, self.z)\n @property\n def yxxx(self): return Float4(self.y, self.x, self.x, self.x)\n @property\n def yxxy(self): return Float4(self.y, self.x, self.x, self.y)\n @property\n def yxxz(self): return Float4(self.y, self.x, self.x, self.z)\n @property\n def yxyx(self): return Float4(self.y, self.x, self.y, self.x)\n @property\n def yxyy(self): return Float4(self.y, self.x, self.y, self.y)\n @property\n def yxyz(self): return Float4(self.y, self.x, self.y, self.z)\n @property\n def yxzx(self): return Float4(self.y, self.x, self.z, self.x)\n @property\n def yxzy(self): return Float4(self.y, self.x, self.z, self.y)\n @property\n def yxzz(self): return Float4(self.y, self.x, self.z, self.z)\n @property\n def yyxx(self): return Float4(self.y, self.y, self.x, self.x)\n @property\n def yyxy(self): return Float4(self.y, self.y, self.x, self.y)\n @property\n def yyxz(self): return Float4(self.y, self.y, self.x, self.z)\n @property\n def yyyx(self): return Float4(self.y, self.y, self.y, self.x)\n @property\n def yyyy(self): return Float4(self.y, self.y, self.y, self.y)\n @property\n def yyyz(self): return Float4(self.y, self.y, self.y, self.z)\n @property\n def yyzx(self): return Float4(self.y, self.y, self.z, self.x)\n @property\n def yyzy(self): return Float4(self.y, self.y, self.z, self.y)\n @property\n def yyzz(self): return Float4(self.y, self.y, self.z, self.z)\n @property\n def yzxx(self): return Float4(self.y, self.z, self.x, self.x)\n @property\n def yzxy(self): return Float4(self.y, self.z, self.x, self.y)\n @property\n def yzxz(self): return Float4(self.y, self.z, self.x, self.z)\n @property\n def yzyx(self): return Float4(self.y, self.z, self.y, self.x)\n @property\n def yzyy(self): return Float4(self.y, self.z, self.y, self.y)\n @property\n def yzyz(self): return Float4(self.y, self.z, self.y, self.z)\n @property\n def yzzx(self): return Float4(self.y, self.z, self.z, self.x)\n @property\n def yzzy(self): return Float4(self.y, self.z, self.z, self.y)\n @property\n def yzzz(self): return Float4(self.y, self.z, self.z, self.z)\n @property\n def zxxx(self): return Float4(self.z, self.x, self.x, self.x)\n @property\n def zxxy(self): return Float4(self.z, self.x, self.x, self.y)\n @property\n def zxxz(self): return Float4(self.z, self.x, self.x, self.z)\n @property\n def zxyx(self): return Float4(self.z, self.x, self.y, self.x)\n @property\n def zxyy(self): return Float4(self.z, self.x, self.y, self.y)\n @property\n def zxyz(self): return Float4(self.z, self.x, self.y, self.z)\n @property\n def zxzx(self): return Float4(self.z, self.x, self.z, self.x)\n @property\n def zxzy(self): return Float4(self.z, self.x, self.z, self.y)\n @property\n def zxzz(self): return Float4(self.z, self.x, self.z, self.z)\n @property\n def zyxx(self): return Float4(self.z, self.y, self.x, self.x)\n @property\n def zyxy(self): return Float4(self.z, self.y, self.x, self.y)\n @property\n def zyxz(self): return Float4(self.z, self.y, self.x, self.z)\n @property\n def zyyx(self): return Float4(self.z, self.y, self.y, self.x)\n @property\n def zyyy(self): return Float4(self.z, self.y, self.y, self.y)\n @property\n def zyyz(self): return Float4(self.z, self.y, self.y, self.z)\n @property\n def zyzx(self): return Float4(self.z, self.y, self.z, self.x)\n @property\n def zyzy(self): return Float4(self.z, self.y, self.z, self.y)\n @property\n def zyzz(self): return Float4(self.z, self.y, self.z, self.z)\n @property\n def zzxx(self): return Float4(self.z, self.z, self.x, self.x)\n @property\n def zzxy(self): return Float4(self.z, self.z, self.x, self.y)\n @property\n def zzxz(self): return Float4(self.z, self.z, self.x, self.z)\n @property\n def zzyx(self): return Float4(self.z, self.z, self.y, self.x)\n @property\n def zzyy(self): return Float4(self.z, self.z, self.y, self.y)\n @property\n def zzyz(self): return Float4(self.z, self.z, self.y, self.z)\n @property\n def zzzx(self): return Float4(self.z, self.z, self.z, self.x)\n @property\n def zzzy(self): return Float4(self.z, self.z, self.z, self.y)\n @property\n def zzzz(self): return Float4(self.z, self.z, self.z, self.z)\n\n @property\n def rrrr(self): return Float4(self.r, self.r, self.r, self.r)\n @property\n def rrrg(self): return Float4(self.r, self.r, self.r, self.g)\n @property\n def rrrb(self): return Float4(self.r, self.r, self.r, self.b)\n @property\n def rrgr(self): return Float4(self.r, self.r, self.g, self.r)\n @property\n def rrgg(self): return Float4(self.r, self.r, self.g, self.g)\n @property\n def rrgb(self): return Float4(self.r, self.r, self.g, self.b)\n @property\n def rrbr(self): return Float4(self.r, self.r, self.b, self.r)\n @property\n def rrbg(self): return Float4(self.r, self.r, self.b, self.g)\n @property\n def rrbb(self): return Float4(self.r, self.r, self.b, self.b)\n @property\n def rgrr(self): return Float4(self.r, self.g, self.r, self.r)\n @property\n def rgrg(self): return Float4(self.r, self.g, self.r, self.g)\n @property\n def rgrb(self): return Float4(self.r, self.g, self.r, self.b)\n @property\n def rggr(self): return Float4(self.r, self.g, self.g, self.r)\n @property\n def rggg(self): return Float4(self.r, self.g, self.g, self.g)\n @property\n def rggb(self): return Float4(self.r, self.g, self.g, self.b)\n @property\n def rgbr(self): return Float4(self.r, self.g, self.b, self.r)\n @property\n def rgbg(self): return Float4(self.r, self.g, self.b, self.g)\n @property\n def rgbb(self): return Float4(self.r, self.g, self.b, self.b)\n @property\n def rbrr(self): return Float4(self.r, self.b, self.r, self.r)\n @property\n def rbrg(self): return Float4(self.r, self.b, self.r, self.g)\n @property\n def rbrb(self): return Float4(self.r, self.b, self.r, self.b)\n @property\n def rbgr(self): return Float4(self.r, self.b, self.g, self.r)\n @property\n def rbgg(self): return Float4(self.r, self.b, self.g, self.g)\n @property\n def rbgb(self): return Float4(self.r, self.b, self.g, self.b)\n @property\n def rbbr(self): return Float4(self.r, self.b, self.b, self.r)\n @property\n def rbbg(self): return Float4(self.r, self.b, self.b, self.g)\n @property\n def rbbb(self): return Float4(self.r, self.b, self.b, self.b)\n @property\n def grrr(self): return Float4(self.g, self.r, self.r, self.r)\n @property\n def grrg(self): return Float4(self.g, self.r, self.r, self.g)\n @property\n def grrb(self): return Float4(self.g, self.r, self.r, self.b)\n @property\n def grgr(self): return Float4(self.g, self.r, self.g, self.r)\n @property\n def grgg(self): return Float4(self.g, self.r, self.g, self.g)\n @property\n def grgb(self): return Float4(self.g, self.r, self.g, self.b)\n @property\n def grbr(self): return Float4(self.g, self.r, self.b, self.r)\n @property\n def grbg(self): return Float4(self.g, self.r, self.b, self.g)\n @property\n def grbb(self): return Float4(self.g, self.r, self.b, self.b)\n @property\n def ggrr(self): return Float4(self.g, self.g, self.r, self.r)\n @property\n def ggrg(self): return Float4(self.g, self.g, self.r, self.g)\n @property\n def ggrb(self): return Float4(self.g, self.g, self.r, self.b)\n @property\n def gggr(self): return Float4(self.g, self.g, self.g, self.r)\n @property\n def gggg(self): return Float4(self.g, self.g, self.g, self.g)\n @property\n def gggb(self): return Float4(self.g, self.g, self.g, self.b)\n @property\n def ggbr(self): return Float4(self.g, self.g, self.b, self.r)\n @property\n def ggbg(self): return Float4(self.g, self.g, self.b, self.g)\n @property\n def ggbb(self): return Float4(self.g, self.g, self.b, self.b)\n @property\n def gbrr(self): return Float4(self.g, self.b, self.r, self.r)\n @property\n def gbrg(self): return Float4(self.g, self.b, self.r, self.g)\n @property\n def gbrb(self): return Float4(self.g, self.b, self.r, self.b)\n @property\n def gbgr(self): return Float4(self.g, self.b, self.g, self.r)\n @property\n def gbgg(self): return Float4(self.g, self.b, self.g, self.g)\n @property\n def gbgb(self): return Float4(self.g, self.b, self.g, self.b)\n @property\n def gbbr(self): return Float4(self.g, self.b, self.b, self.r)\n @property\n def gbbg(self): return Float4(self.g, self.b, self.b, self.g)\n @property\n def gbbb(self): return Float4(self.g, self.b, self.b, self.b)\n @property\n def brrr(self): return Float4(self.b, self.r, self.r, self.r)\n @property\n def brrg(self): return Float4(self.b, self.r, self.r, self.g)\n @property\n def brrb(self): return Float4(self.b, self.r, self.r, self.b)\n @property\n def brgr(self): return Float4(self.b, self.r, self.g, self.r)\n @property\n def brgg(self): return Float4(self.b, self.r, self.g, self.g)\n @property\n def brgb(self): return Float4(self.b, self.r, self.g, self.b)\n @property\n def brbr(self): return Float4(self.b, self.r, self.b, self.r)\n @property\n def brbg(self): return Float4(self.b, self.r, self.b, self.g)\n @property\n def brbb(self): return Float4(self.b, self.r, self.b, self.b)\n @property\n def bgrr(self): return Float4(self.b, self.g, self.r, self.r)\n @property\n def bgrg(self): return Float4(self.b, self.g, self.r, self.g)\n @property\n def bgrb(self): return Float4(self.b, self.g, self.r, self.b)\n @property\n def bggr(self): return Float4(self.b, self.g, self.g, self.r)\n @property\n def bggg(self): return Float4(self.b, self.g, self.g, self.g)\n @property\n def bggb(self): return Float4(self.b, self.g, self.g, self.b)\n @property\n def bgbr(self): return Float4(self.b, self.g, self.b, self.r)\n @property\n def bgbg(self): return Float4(self.b, self.g, self.b, self.g)\n @property\n def bgbb(self): return Float4(self.b, self.g, self.b, self.b)\n @property\n def bbrr(self): return Float4(self.b, self.b, self.r, self.r)\n @property\n def bbrg(self): return Float4(self.b, self.b, self.r, self.g)\n @property\n def bbrb(self): return Float4(self.b, self.b, self.r, self.b)\n @property\n def bbgr(self): return Float4(self.b, self.b, self.g, self.r)\n @property\n def bbgg(self): return Float4(self.b, self.b, self.g, self.g)\n @property\n def bbgb(self): return Float4(self.b, self.b, self.g, self.b)\n @property\n def bbbr(self): return Float4(self.b, self.b, self.b, self.r)\n @property\n def bbbg(self): return Float4(self.b, self.b, self.b, self.g)\n @property\n def bbbb(self): return Float4(self.b, self.b, self.b, self.b)"}],"string":"[\n {\n \"identifier\": \"Float2\",\n \"path\": \"src/tinycio/numerics/vector.py\",\n \"snippet\": \"class Float2(np.ndarray):\\n \\\"\\\"\\\"\\n Float2 type using numpy.ndarray.\\n \\\"\\\"\\\"\\n def __new__(cls, *args):\\n if len(args) == 1:\\n if isinstance(args[0], list) or isinstance(args[0], tuple):\\n assert len(args[0]) == 2, \\\"list/tuple must have 2 components\\\"\\n arr = np.asarray([args[0][0], args[0][1]], dtype=np.float32).view(cls)\\n elif isinstance(args[0], np.ndarray):\\n assert len(args[0].squeeze().shape) == 1 and args[0].shape[0] == 2, \\\\\\n \\\"numpy array must be sized [C=2] or [C=2, H=1, W=1]\\\"\\n arr = np.asarray(args[0].squeeze(), dtype=np.float32).view(cls)\\n elif torch.is_tensor(args[0]):\\n assert len(args[0].squeeze().size()) == 1 and args[0].size(0) == 2, \\\\\\n \\\"torch tensor must be sized [C=2] or [C=2, H=1, W=1]\\\"\\n value = args[0].squeeze().float().cpu()\\n arr = np.asarray([value[0].item(), value[1].item()], dtype=np.float32).view(cls)\\n else:\\n value = float(args[0])\\n arr = np.asarray([value, value], dtype=np.float32).view(cls)\\n elif len(args) == 2:\\n arr = np.asarray(args, dtype=np.float32).view(cls)\\n else: \\n raise TypeError(\\\"Float2 only accepts 1 or 2 arguments.\\\")\\n return arr\\n\\n def list(self) -> list:\\n \\\"\\\"\\\"Returns values as Python list\\\"\\\"\\\"\\n return [self[0], self[1]]\\n\\n def tuple(self) -> tuple:\\n \\\"\\\"\\\"Returns values as Python tuple\\\"\\\"\\\"\\n return (self[0], self[1])\\n\\n @property\\n def x(self) -> float:\\n return self[0]\\n @x.setter\\n def x(self, value):\\n self[0] = value\\n @property\\n def y(self) -> float:\\n return self[1]\\n @y.setter\\n def y(self, value):\\n self[1] = value\\n @property\\n def r(self) -> float:\\n return self[0]\\n @r.setter\\n def r(self, value):\\n self[0] = value\\n @property\\n def g(self) -> float:\\n return self[1]\\n @g.setter\\n def g(self, value):\\n self[1] = value\\n\\n @staticmethod\\n def zero():\\n \\\"\\\"\\\"Returns numeric type filled with zero values\\\"\\\"\\\"\\n return Float2(0., 0.)\\n @staticmethod\\n def one():\\n \\\"\\\"\\\"Returns numeric type filled with one values\\\"\\\"\\\"\\n return Float2(1., 1.)\\n @staticmethod\\n def x_axis():\\n \\\"\\\"\\\"Returns numeric type with x-axis set to 1 and all others to 0\\\"\\\"\\\"\\n return Float2(1., 0.)\\n @staticmethod\\n def y_axis():\\n \\\"\\\"\\\"Returns numeric type with y-axis set to 1 and all others to 0\\\"\\\"\\\"\\n return Float2(0., 1.)\\n\\n @property\\n def xx(self): return Float2(self.x, self.x)\\n @property\\n def xy(self): return self\\n @property\\n def yx(self): return Float2(self.y, self.x)\\n @property\\n def yy(self): return Float2(self.y, self.y)\\n\\n @property\\n def rr(self): return Float2(self.r, self.r)\\n @property\\n def rg(self): return self\\n @property\\n def gr(self): return Float2(self.g, self.r)\\n @property\\n def gg(self): return Float2(self.g, self.g)\\n\\n @property\\n def xxx(self): return Float3(self.x, self.x, self.x)\\n @property\\n def xxy(self): return Float3(self.x, self.x, self.y)\\n @property\\n def xyx(self): return Float3(self.x, self.y, self.x)\\n @property\\n def xyy(self): return Float3(self.x, self.y, self.y)\\n @property\\n def yxx(self): return Float3(self.y, self.x, self.x)\\n @property\\n def yxy(self): return Float3(self.y, self.x, self.y)\\n @property\\n def yyx(self): return Float3(self.y, self.y, self.x)\\n @property\\n def yyy(self): return Float3(self.y, self.y, self.y)\\n\\n @property\\n def rrr(self): return Float3(self.r, self.r, self.r)\\n @property\\n def rrg(self): return Float3(self.r, self.r, self.g)\\n @property\\n def rgr(self): return Float3(self.r, self.g, self.r)\\n @property\\n def rgg(self): return Float3(self.r, self.g, self.g)\\n @property\\n def grr(self): return Float3(self.g, self.r, self.r)\\n @property\\n def grg(self): return Float3(self.g, self.r, self.g)\\n @property\\n def ggr(self): return Float3(self.g, self.g, self.r)\\n @property\\n def ggg(self): return Float3(self.g, self.g, self.g)\\n\\n @property\\n def xxxx(self): return Float4(self.x, self.x, self.x, self.x)\\n @property\\n def xxxy(self): return Float4(self.x, self.x, self.x, self.y)\\n @property\\n def xxyx(self): return Float4(self.x, self.x, self.y, self.x)\\n @property\\n def xxyy(self): return Float4(self.x, self.x, self.y, self.y)\\n @property\\n def xyxx(self): return Float4(self.x, self.y, self.x, self.x)\\n @property\\n def xyxy(self): return Float4(self.x, self.y, self.x, self.y)\\n @property\\n def xyyx(self): return Float4(self.x, self.y, self.y, self.x)\\n @property\\n def xyyy(self): return Float4(self.x, self.y, self.y, self.y)\\n @property\\n def yxxx(self): return Float4(self.y, self.x, self.x, self.x)\\n @property\\n def yxxy(self): return Float4(self.y, self.x, self.x, self.y)\\n @property\\n def yxyx(self): return Float4(self.y, self.x, self.y, self.x)\\n @property\\n def yxyy(self): return Float4(self.y, self.x, self.y, self.y)\\n @property\\n def yyxx(self): return Float4(self.y, self.y, self.x, self.x)\\n @property\\n def yyxy(self): return Float4(self.y, self.y, self.x, self.y)\\n @property\\n def yyyx(self): return Float4(self.y, self.y, self.y, self.x)\\n @property\\n def yyyy(self): return Float4(self.y, self.y, self.y, self.y)\\n\\n @property\\n def rrrr(self): return Float4(self.r, self.r, self.r, self.r)\\n @property\\n def rrrg(self): return Float4(self.r, self.r, self.r, self.g)\\n @property\\n def rrgr(self): return Float4(self.r, self.r, self.g, self.r)\\n @property\\n def rrgg(self): return Float4(self.r, self.r, self.g, self.g)\\n @property\\n def rgrr(self): return Float4(self.r, self.g, self.r, self.r)\\n @property\\n def rgrg(self): return Float4(self.r, self.g, self.r, self.g)\\n @property\\n def rggr(self): return Float4(self.r, self.g, self.g, self.r)\\n @property\\n def rggg(self): return Float4(self.r, self.g, self.g, self.g)\\n @property\\n def grrr(self): return Float4(self.g, self.r, self.r, self.r)\\n @property\\n def grrg(self): return Float4(self.g, self.r, self.r, self.g)\\n @property\\n def grgr(self): return Float4(self.g, self.r, self.g, self.r)\\n @property\\n def grgg(self): return Float4(self.g, self.r, self.g, self.g)\\n @property\\n def ggrr(self): return Float4(self.g, self.g, self.r, self.r)\\n @property\\n def ggrg(self): return Float4(self.g, self.g, self.r, self.g)\\n @property\\n def gggr(self): return Float4(self.g, self.g, self.g, self.r)\\n @property\\n def gggg(self): return Float4(self.g, self.g, self.g, self.g)\"\n },\n {\n \"identifier\": \"Float3\",\n \"path\": \"src/tinycio/numerics/vector.py\",\n \"snippet\": \"class Float3(np.ndarray):\\n \\\"\\\"\\\"\\n Float3 type using numpy.ndarray.\\n \\\"\\\"\\\"\\n def __new__(cls, *args):\\n if len(args) == 1:\\n if isinstance(args[0], list) or isinstance(args[0], tuple):\\n assert len(args[0]) == 3, \\\"list/tuple must have 3 components\\\"\\n arr = np.asarray([args[0][0], args[0][1], args[0][2]], dtype=np.float32).view(cls)\\n elif isinstance(args[0], np.ndarray):\\n assert len(args[0].squeeze().shape) == 1 and args[0].shape[0] == 3, \\\\\\n \\\"numpy array must be sized [C=3] or [C=3, H=1, W=1]\\\"\\n arr = np.asarray(args[0].squeeze(), dtype=np.float32).view(cls)\\n elif torch.is_tensor(args[0]):\\n assert len(args[0].squeeze().size()) == 1 and args[0].size(0) == 3, \\\\\\n \\\"torch tensor must be sized [C=3] or [C=3, H=1, W=1]\\\"\\n value = args[0].squeeze().float().cpu()\\n arr = np.asarray([value[0].item(), value[1].item(), value[2].item()], dtype=np.float32).view(cls)\\n else:\\n value = float(args[0])\\n arr = np.asarray([value, value, value], dtype=np.float32).view(cls)\\n elif len(args) == 3:\\n arr = np.asarray(args, dtype=np.float32).view(cls)\\n else: \\n raise TypeError(\\\"Float3 only accepts 1 or 3 arguments.\\\")\\n return arr\\n\\n def list(self) -> list:\\n \\\"\\\"\\\"Returns values as Python list\\\"\\\"\\\"\\n return [self[0], self[1], self[2]]\\n\\n def tuple(self) -> tuple:\\n \\\"\\\"\\\"Returns values as Python tuple\\\"\\\"\\\"\\n return (self[0], self[1], self[2])\\n\\n @property\\n def x(self) -> float:\\n return self[0]\\n @x.setter\\n def x(self, value):\\n self[0] = value\\n @property\\n def y(self) -> float:\\n return self[1]\\n @y.setter\\n def y(self, value):\\n self[1] = value\\n @property\\n def z(self) -> float:\\n return self[2]\\n @z.setter\\n def z(self, value):\\n self[2] = value\\n @property\\n def r(self) -> float:\\n return self[0]\\n @r.setter\\n def r(self, value):\\n self[0] = value\\n @property\\n def g(self) -> float:\\n return self[1]\\n @g.setter\\n def g(self, value):\\n self[1] = value\\n @property\\n def b(self) -> float:\\n return self[2]\\n @b.setter\\n def b(self, value):\\n self[2] = value\\n @staticmethod\\n def zero():\\n \\\"\\\"\\\"Returns numeric type filled with zero values\\\"\\\"\\\"\\n return Float3(0., 0., 0.)\\n @staticmethod\\n def one():\\n \\\"\\\"\\\"Returns numeric type filled with one values\\\"\\\"\\\"\\n return Float3(1., 1., 1.)\\n @staticmethod\\n def x_axis():\\n \\\"\\\"\\\"Returns numeric type with x-axis set to 1 and all others to 0\\\"\\\"\\\"\\n return Float3(1., 0., 0.)\\n @staticmethod\\n def y_axis():\\n \\\"\\\"\\\"Returns numeric type with y-axis set to 1 and all others to 0\\\"\\\"\\\"\\n return Float3(0., 1., 0.)\\n @staticmethod\\n def z_axis():\\n \\\"\\\"\\\"Returns numeric type with z-axis set to 1 and all others to 0\\\"\\\"\\\"\\n return Float3(0., 0., 1.)\\n\\n @property\\n def xx(self): return Float2(self.x, self.x)\\n @property\\n def xy(self): return Float2(self.x, self.y)\\n @property\\n def xz(self): return Float2(self.x, self.z)\\n @property\\n def yx(self): return Float2(self.y, self.x)\\n @property\\n def yy(self): return Float2(self.y, self.y)\\n @property\\n def yz(self): return Float2(self.y, self.z)\\n @property\\n def zx(self): return Float2(self.z, self.x)\\n @property\\n def zy(self): return Float2(self.z, self.y)\\n @property\\n def zz(self): return Float2(self.z, self.z)\\n\\n @property\\n def rr(self): return Float2(self.r, self.r)\\n @property\\n def rg(self): return Float2(self.r, self.g)\\n @property\\n def rb(self): return Float2(self.r, self.b)\\n @property\\n def gr(self): return Float2(self.g, self.r)\\n @property\\n def gg(self): return Float2(self.g, self.g)\\n @property\\n def gb(self): return Float2(self.g, self.b)\\n @property\\n def br(self): return Float2(self.b, self.r)\\n @property\\n def bg(self): return Float2(self.b, self.g)\\n @property\\n def bb(self): return Float2(self.b, self.b)\\n\\n @property\\n def xxx(self): return Float3(self.x, self.x, self.x)\\n @property\\n def xxy(self): return Float3(self.x, self.x, self.y)\\n @property\\n def xxz(self): return Float3(self.x, self.x, self.z)\\n @property\\n def xyx(self): return Float3(self.x, self.y, self.x)\\n @property\\n def xyy(self): return Float3(self.x, self.y, self.y)\\n @property\\n def xyz(self): return self\\n @property\\n def xzx(self): return Float3(self.x, self.z, self.x)\\n @property\\n def xzy(self): return Float3(self.x, self.z, self.y)\\n @property\\n def xzz(self): return Float3(self.x, self.z, self.z)\\n @property\\n def yxx(self): return Float3(self.y, self.x, self.x)\\n @property\\n def yxy(self): return Float3(self.y, self.x, self.y)\\n @property\\n def yxz(self): return Float3(self.y, self.x, self.z)\\n @property\\n def yyx(self): return Float3(self.y, self.y, self.x)\\n @property\\n def yyy(self): return Float3(self.y, self.y, self.y)\\n @property\\n def yyz(self): return Float3(self.y, self.y, self.z)\\n @property\\n def yzx(self): return Float3(self.y, self.z, self.x)\\n @property\\n def yzy(self): return Float3(self.y, self.z, self.y)\\n @property\\n def yzz(self): return Float3(self.y, self.z, self.z)\\n @property\\n def zxx(self): return Float3(self.z, self.x, self.x)\\n @property\\n def zxy(self): return Float3(self.z, self.x, self.y)\\n @property\\n def zxz(self): return Float3(self.z, self.x, self.z)\\n @property\\n def zyx(self): return Float3(self.z, self.y, self.x)\\n @property\\n def zyy(self): return Float3(self.z, self.y, self.y)\\n @property\\n def zyz(self): return Float3(self.z, self.y, self.z)\\n @property\\n def zzx(self): return Float3(self.z, self.z, self.x)\\n @property\\n def zzy(self): return Float3(self.z, self.z, self.y)\\n @property\\n def zzz(self): return Float3(self.z, self.z, self.z)\\n\\n @property\\n def rrr(self): return Float3(self.r, self.r, self.r)\\n @property\\n def rrg(self): return Float3(self.r, self.r, self.g)\\n @property\\n def rrb(self): return Float3(self.r, self.r, self.b)\\n @property\\n def rgr(self): return Float3(self.r, self.g, self.r)\\n @property\\n def rgg(self): return Float3(self.r, self.g, self.g)\\n @property\\n def rgb(self): return self\\n @property\\n def rbr(self): return Float3(self.r, self.b, self.r)\\n @property\\n def rbg(self): return Float3(self.r, self.b, self.g)\\n @property\\n def rbb(self): return Float3(self.r, self.b, self.b)\\n @property\\n def grr(self): return Float3(self.g, self.r, self.r)\\n @property\\n def grg(self): return Float3(self.g, self.r, self.g)\\n @property\\n def grb(self): return Float3(self.g, self.r, self.b)\\n @property\\n def ggr(self): return Float3(self.g, self.g, self.r)\\n @property\\n def ggg(self): return Float3(self.g, self.g, self.g)\\n @property\\n def ggb(self): return Float3(self.g, self.g, self.b)\\n @property\\n def gbr(self): return Float3(self.g, self.b, self.r)\\n @property\\n def gbg(self): return Float3(self.g, self.b, self.g)\\n @property\\n def gbb(self): return Float3(self.g, self.b, self.b)\\n @property\\n def brr(self): return Float3(self.b, self.r, self.r)\\n @property\\n def brg(self): return Float3(self.b, self.r, self.g)\\n @property\\n def brb(self): return Float3(self.b, self.r, self.b)\\n @property\\n def bgr(self): return Float3(self.b, self.g, self.r)\\n @property\\n def bgg(self): return Float3(self.b, self.g, self.g)\\n @property\\n def bgb(self): return Float3(self.b, self.g, self.b)\\n @property\\n def bbr(self): return Float3(self.b, self.b, self.r)\\n @property\\n def bbg(self): return Float3(self.b, self.b, self.g)\\n @property\\n def bbb(self): return Float3(self.b, self.b, self.b)\\n\\n @property\\n def xxxx(self): return Float4(self.x, self.x, self.x, self.x)\\n @property\\n def xxxy(self): return Float4(self.x, self.x, self.x, self.y)\\n @property\\n def xxxz(self): return Float4(self.x, self.x, self.x, self.z)\\n @property\\n def xxyx(self): return Float4(self.x, self.x, self.y, self.x)\\n @property\\n def xxyy(self): return Float4(self.x, self.x, self.y, self.y)\\n @property\\n def xxyz(self): return Float4(self.x, self.x, self.y, self.z)\\n @property\\n def xxzx(self): return Float4(self.x, self.x, self.z, self.x)\\n @property\\n def xxzy(self): return Float4(self.x, self.x, self.z, self.y)\\n @property\\n def xxzz(self): return Float4(self.x, self.x, self.z, self.z)\\n @property\\n def xyxx(self): return Float4(self.x, self.y, self.x, self.x)\\n @property\\n def xyxy(self): return Float4(self.x, self.y, self.x, self.y)\\n @property\\n def xyxz(self): return Float4(self.x, self.y, self.x, self.z)\\n @property\\n def xyyx(self): return Float4(self.x, self.y, self.y, self.x)\\n @property\\n def xyyy(self): return Float4(self.x, self.y, self.y, self.y)\\n @property\\n def xyyz(self): return Float4(self.x, self.y, self.y, self.z)\\n @property\\n def xyzx(self): return Float4(self.x, self.y, self.z, self.x)\\n @property\\n def xyzy(self): return Float4(self.x, self.y, self.z, self.y)\\n @property\\n def xyzz(self): return Float4(self.x, self.y, self.z, self.z)\\n @property\\n def xzxx(self): return Float4(self.x, self.z, self.x, self.x)\\n @property\\n def xzxy(self): return Float4(self.x, self.z, self.x, self.y)\\n @property\\n def xzxz(self): return Float4(self.x, self.z, self.x, self.z)\\n @property\\n def xzyx(self): return Float4(self.x, self.z, self.y, self.x)\\n @property\\n def xzyy(self): return Float4(self.x, self.z, self.y, self.y)\\n @property\\n def xzyz(self): return Float4(self.x, self.z, self.y, self.z)\\n @property\\n def xzzx(self): return Float4(self.x, self.z, self.z, self.x)\\n @property\\n def xzzy(self): return Float4(self.x, self.z, self.z, self.y)\\n @property\\n def xzzz(self): return Float4(self.x, self.z, self.z, self.z)\\n @property\\n def yxxx(self): return Float4(self.y, self.x, self.x, self.x)\\n @property\\n def yxxy(self): return Float4(self.y, self.x, self.x, self.y)\\n @property\\n def yxxz(self): return Float4(self.y, self.x, self.x, self.z)\\n @property\\n def yxyx(self): return Float4(self.y, self.x, self.y, self.x)\\n @property\\n def yxyy(self): return Float4(self.y, self.x, self.y, self.y)\\n @property\\n def yxyz(self): return Float4(self.y, self.x, self.y, self.z)\\n @property\\n def yxzx(self): return Float4(self.y, self.x, self.z, self.x)\\n @property\\n def yxzy(self): return Float4(self.y, self.x, self.z, self.y)\\n @property\\n def yxzz(self): return Float4(self.y, self.x, self.z, self.z)\\n @property\\n def yyxx(self): return Float4(self.y, self.y, self.x, self.x)\\n @property\\n def yyxy(self): return Float4(self.y, self.y, self.x, self.y)\\n @property\\n def yyxz(self): return Float4(self.y, self.y, self.x, self.z)\\n @property\\n def yyyx(self): return Float4(self.y, self.y, self.y, self.x)\\n @property\\n def yyyy(self): return Float4(self.y, self.y, self.y, self.y)\\n @property\\n def yyyz(self): return Float4(self.y, self.y, self.y, self.z)\\n @property\\n def yyzx(self): return Float4(self.y, self.y, self.z, self.x)\\n @property\\n def yyzy(self): return Float4(self.y, self.y, self.z, self.y)\\n @property\\n def yyzz(self): return Float4(self.y, self.y, self.z, self.z)\\n @property\\n def yzxx(self): return Float4(self.y, self.z, self.x, self.x)\\n @property\\n def yzxy(self): return Float4(self.y, self.z, self.x, self.y)\\n @property\\n def yzxz(self): return Float4(self.y, self.z, self.x, self.z)\\n @property\\n def yzyx(self): return Float4(self.y, self.z, self.y, self.x)\\n @property\\n def yzyy(self): return Float4(self.y, self.z, self.y, self.y)\\n @property\\n def yzyz(self): return Float4(self.y, self.z, self.y, self.z)\\n @property\\n def yzzx(self): return Float4(self.y, self.z, self.z, self.x)\\n @property\\n def yzzy(self): return Float4(self.y, self.z, self.z, self.y)\\n @property\\n def yzzz(self): return Float4(self.y, self.z, self.z, self.z)\\n @property\\n def zxxx(self): return Float4(self.z, self.x, self.x, self.x)\\n @property\\n def zxxy(self): return Float4(self.z, self.x, self.x, self.y)\\n @property\\n def zxxz(self): return Float4(self.z, self.x, self.x, self.z)\\n @property\\n def zxyx(self): return Float4(self.z, self.x, self.y, self.x)\\n @property\\n def zxyy(self): return Float4(self.z, self.x, self.y, self.y)\\n @property\\n def zxyz(self): return Float4(self.z, self.x, self.y, self.z)\\n @property\\n def zxzx(self): return Float4(self.z, self.x, self.z, self.x)\\n @property\\n def zxzy(self): return Float4(self.z, self.x, self.z, self.y)\\n @property\\n def zxzz(self): return Float4(self.z, self.x, self.z, self.z)\\n @property\\n def zyxx(self): return Float4(self.z, self.y, self.x, self.x)\\n @property\\n def zyxy(self): return Float4(self.z, self.y, self.x, self.y)\\n @property\\n def zyxz(self): return Float4(self.z, self.y, self.x, self.z)\\n @property\\n def zyyx(self): return Float4(self.z, self.y, self.y, self.x)\\n @property\\n def zyyy(self): return Float4(self.z, self.y, self.y, self.y)\\n @property\\n def zyyz(self): return Float4(self.z, self.y, self.y, self.z)\\n @property\\n def zyzx(self): return Float4(self.z, self.y, self.z, self.x)\\n @property\\n def zyzy(self): return Float4(self.z, self.y, self.z, self.y)\\n @property\\n def zyzz(self): return Float4(self.z, self.y, self.z, self.z)\\n @property\\n def zzxx(self): return Float4(self.z, self.z, self.x, self.x)\\n @property\\n def zzxy(self): return Float4(self.z, self.z, self.x, self.y)\\n @property\\n def zzxz(self): return Float4(self.z, self.z, self.x, self.z)\\n @property\\n def zzyx(self): return Float4(self.z, self.z, self.y, self.x)\\n @property\\n def zzyy(self): return Float4(self.z, self.z, self.y, self.y)\\n @property\\n def zzyz(self): return Float4(self.z, self.z, self.y, self.z)\\n @property\\n def zzzx(self): return Float4(self.z, self.z, self.z, self.x)\\n @property\\n def zzzy(self): return Float4(self.z, self.z, self.z, self.y)\\n @property\\n def zzzz(self): return Float4(self.z, self.z, self.z, self.z)\\n\\n @property\\n def rrrr(self): return Float4(self.r, self.r, self.r, self.r)\\n @property\\n def rrrg(self): return Float4(self.r, self.r, self.r, self.g)\\n @property\\n def rrrb(self): return Float4(self.r, self.r, self.r, self.b)\\n @property\\n def rrgr(self): return Float4(self.r, self.r, self.g, self.r)\\n @property\\n def rrgg(self): return Float4(self.r, self.r, self.g, self.g)\\n @property\\n def rrgb(self): return Float4(self.r, self.r, self.g, self.b)\\n @property\\n def rrbr(self): return Float4(self.r, self.r, self.b, self.r)\\n @property\\n def rrbg(self): return Float4(self.r, self.r, self.b, self.g)\\n @property\\n def rrbb(self): return Float4(self.r, self.r, self.b, self.b)\\n @property\\n def rgrr(self): return Float4(self.r, self.g, self.r, self.r)\\n @property\\n def rgrg(self): return Float4(self.r, self.g, self.r, self.g)\\n @property\\n def rgrb(self): return Float4(self.r, self.g, self.r, self.b)\\n @property\\n def rggr(self): return Float4(self.r, self.g, self.g, self.r)\\n @property\\n def rggg(self): return Float4(self.r, self.g, self.g, self.g)\\n @property\\n def rggb(self): return Float4(self.r, self.g, self.g, self.b)\\n @property\\n def rgbr(self): return Float4(self.r, self.g, self.b, self.r)\\n @property\\n def rgbg(self): return Float4(self.r, self.g, self.b, self.g)\\n @property\\n def rgbb(self): return Float4(self.r, self.g, self.b, self.b)\\n @property\\n def rbrr(self): return Float4(self.r, self.b, self.r, self.r)\\n @property\\n def rbrg(self): return Float4(self.r, self.b, self.r, self.g)\\n @property\\n def rbrb(self): return Float4(self.r, self.b, self.r, self.b)\\n @property\\n def rbgr(self): return Float4(self.r, self.b, self.g, self.r)\\n @property\\n def rbgg(self): return Float4(self.r, self.b, self.g, self.g)\\n @property\\n def rbgb(self): return Float4(self.r, self.b, self.g, self.b)\\n @property\\n def rbbr(self): return Float4(self.r, self.b, self.b, self.r)\\n @property\\n def rbbg(self): return Float4(self.r, self.b, self.b, self.g)\\n @property\\n def rbbb(self): return Float4(self.r, self.b, self.b, self.b)\\n @property\\n def grrr(self): return Float4(self.g, self.r, self.r, self.r)\\n @property\\n def grrg(self): return Float4(self.g, self.r, self.r, self.g)\\n @property\\n def grrb(self): return Float4(self.g, self.r, self.r, self.b)\\n @property\\n def grgr(self): return Float4(self.g, self.r, self.g, self.r)\\n @property\\n def grgg(self): return Float4(self.g, self.r, self.g, self.g)\\n @property\\n def grgb(self): return Float4(self.g, self.r, self.g, self.b)\\n @property\\n def grbr(self): return Float4(self.g, self.r, self.b, self.r)\\n @property\\n def grbg(self): return Float4(self.g, self.r, self.b, self.g)\\n @property\\n def grbb(self): return Float4(self.g, self.r, self.b, self.b)\\n @property\\n def ggrr(self): return Float4(self.g, self.g, self.r, self.r)\\n @property\\n def ggrg(self): return Float4(self.g, self.g, self.r, self.g)\\n @property\\n def ggrb(self): return Float4(self.g, self.g, self.r, self.b)\\n @property\\n def gggr(self): return Float4(self.g, self.g, self.g, self.r)\\n @property\\n def gggg(self): return Float4(self.g, self.g, self.g, self.g)\\n @property\\n def gggb(self): return Float4(self.g, self.g, self.g, self.b)\\n @property\\n def ggbr(self): return Float4(self.g, self.g, self.b, self.r)\\n @property\\n def ggbg(self): return Float4(self.g, self.g, self.b, self.g)\\n @property\\n def ggbb(self): return Float4(self.g, self.g, self.b, self.b)\\n @property\\n def gbrr(self): return Float4(self.g, self.b, self.r, self.r)\\n @property\\n def gbrg(self): return Float4(self.g, self.b, self.r, self.g)\\n @property\\n def gbrb(self): return Float4(self.g, self.b, self.r, self.b)\\n @property\\n def gbgr(self): return Float4(self.g, self.b, self.g, self.r)\\n @property\\n def gbgg(self): return Float4(self.g, self.b, self.g, self.g)\\n @property\\n def gbgb(self): return Float4(self.g, self.b, self.g, self.b)\\n @property\\n def gbbr(self): return Float4(self.g, self.b, self.b, self.r)\\n @property\\n def gbbg(self): return Float4(self.g, self.b, self.b, self.g)\\n @property\\n def gbbb(self): return Float4(self.g, self.b, self.b, self.b)\\n @property\\n def brrr(self): return Float4(self.b, self.r, self.r, self.r)\\n @property\\n def brrg(self): return Float4(self.b, self.r, self.r, self.g)\\n @property\\n def brrb(self): return Float4(self.b, self.r, self.r, self.b)\\n @property\\n def brgr(self): return Float4(self.b, self.r, self.g, self.r)\\n @property\\n def brgg(self): return Float4(self.b, self.r, self.g, self.g)\\n @property\\n def brgb(self): return Float4(self.b, self.r, self.g, self.b)\\n @property\\n def brbr(self): return Float4(self.b, self.r, self.b, self.r)\\n @property\\n def brbg(self): return Float4(self.b, self.r, self.b, self.g)\\n @property\\n def brbb(self): return Float4(self.b, self.r, self.b, self.b)\\n @property\\n def bgrr(self): return Float4(self.b, self.g, self.r, self.r)\\n @property\\n def bgrg(self): return Float4(self.b, self.g, self.r, self.g)\\n @property\\n def bgrb(self): return Float4(self.b, self.g, self.r, self.b)\\n @property\\n def bggr(self): return Float4(self.b, self.g, self.g, self.r)\\n @property\\n def bggg(self): return Float4(self.b, self.g, self.g, self.g)\\n @property\\n def bggb(self): return Float4(self.b, self.g, self.g, self.b)\\n @property\\n def bgbr(self): return Float4(self.b, self.g, self.b, self.r)\\n @property\\n def bgbg(self): return Float4(self.b, self.g, self.b, self.g)\\n @property\\n def bgbb(self): return Float4(self.b, self.g, self.b, self.b)\\n @property\\n def bbrr(self): return Float4(self.b, self.b, self.r, self.r)\\n @property\\n def bbrg(self): return Float4(self.b, self.b, self.r, self.g)\\n @property\\n def bbrb(self): return Float4(self.b, self.b, self.r, self.b)\\n @property\\n def bbgr(self): return Float4(self.b, self.b, self.g, self.r)\\n @property\\n def bbgg(self): return Float4(self.b, self.b, self.g, self.g)\\n @property\\n def bbgb(self): return Float4(self.b, self.b, self.g, self.b)\\n @property\\n def bbbr(self): return Float4(self.b, self.b, self.b, self.r)\\n @property\\n def bbbg(self): return Float4(self.b, self.b, self.b, self.g)\\n @property\\n def bbbb(self): return Float4(self.b, self.b, self.b, self.b)\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import typing\nimport torch\nimport numpy as np\nfrom typing import Union\nfrom ..numerics import Float2, Float3"},"token_num":{"kind":"number","value":11196,"string":"11,196"},"cropped_code":{"kind":"string","value":"\ndef col_hsv_to_rgb(hsv:Union[Float3, Color]) -> Float3:\n \"\"\"\n Convert HSV color to RGB.\n\n :param hsv: HSV color\n :type hsv: Float3 | Color\n \"\"\"\n h, s, v = hsv.x, hsv.y, hsv.z\n i = np.floor(h * 6)\n f = h * 6 - i\n p = v * (1 - s)\n q = v * (1 - f * s)\n t = v * (1 - (1 - f) * s)\n r, g, b = [\n (v, t, p),\n (q, v, p),\n (p, v, t),\n (p, q, v),\n (t, p, v),\n (v, p, q),\n ][int(i%6)]\n return Float3(r, g, b)\n\ndef col_rgb_to_hsv(rgb:Union[Float3, Color]) -> Float3:\n \"\"\"\n Convert RGB color to HSV.\n\n :param rgb: RGB color\n :type rgb: Float3 | Color\n \"\"\"\n r, g, b = rgb.r, rgb.g, rgb.b\n high = np.max([r, g, b])\n low = np.min([r, g, b])\n h, s, v = high, high, high\n d = high - low\n s = 0 if high == 0 else d/high\n if high == low:\n h = 0.0\n else:\n h = {\n r: (g - b) / d + (6 if g < b else 0),\n g: (b - r) / d + 2,\n b: (r - g) / d + 4,\n }[high]\n h /= 6\n return Float3(h, s, v)\n\n# My fairly lazy OKHSV/OKHSL \"port\" of:\n# https://github.com/holbrookdev/ok-color-picker\n# MIT License\n\n# Copyright (c) 2022 Brian Holbrook\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\n__ok_eps = 1e-7\n\ndef col_oklab_to_linear_srgb(lab:Union[Float3, Color]) -> Float3:\n \"\"\"\n Convert OKLAB color to linear sRGB.\n\n :param lab: OKLAB color\n :type lab: Float3 | Color\n \"\"\"\n L, a, b = lab.x, lab.y, lab.z\n l_ = L + 0.3963377774 * a + 0.2158037573 * b\n m_ = L - 0.1055613458 * a - 0.0638541728 * b\n s_ = L - 0.0894841775 * a - 1.291485548 * b\n\n l = l_ * l_ * l_\n m = m_ * m_ * m_\n s = s_ * s_ * s_\n\n res = Float3(\n +4.0767416621 * l - 3.3077115913 * m + 0.2309699292 * s,\n -1.2684380046 * l + 2.6097574011 * m - 0.3413193965 * s,\n -0.0041960863 * l - 0.7034186147 * m + 1.707614701 * s\n )\n return res\n\ndef col_linear_srgb_to_oklab(srgb:Union[Float3, Color]) -> Float3:\n \"\"\"\n Convert linear sRGB color to OKLAB.\n\n :param srgb: linear sRGB color\n :type srgb: Float3 | Color\n \"\"\"\n r, g, b = srgb.r, srgb.g, srgb.b\n l = 0.4122214708 * r + 0.5363325363 * g + 0.0514459929 * b\n m = 0.2119034982 * r + 0.6806995451 * g + 0.1073969566 * b\n s = 0.0883024619 * r + 0.2817188376 * g + 0.6299787005 * b\n\n l_ = np.cbrt(l)\n m_ = np.cbrt(m)\n s_ = np.cbrt(s)\n\n res = Float3(\n 0.2104542553 * l_ + 0.793617785 * m_ - 0.0040720468 * s_,\n 1.9779984951 * l_ - 2.428592205 * m_ + 0.4505937099 * s_,\n 0.0259040371 * l_ + 0.7827717662 * m_ - 0.808675766 * s_\n )\n return res\n\n"},"all_code":{"kind":"string","value":"from __future__ import annotations\n\n\n\ndef srgb_luminance(im_srgb:Union[torch.Tensor, ColorImage]) -> torch.Tensor:\n \"\"\"\n Return relative luminance of linear sRGB image.\n\n :param im_srgb: [C=3, H, W] color image tensor in sRGB color space\n :type im_srgb: torch.Tensor | ColorImage\n :return: [C=1, H, W] image tensor\n \"\"\"\n lum_r, lum_g, lum_b = 0.2126, 0.7152, 0.0722\n return lum_r * im_srgb[0:1,...] + lum_g * im_srgb[1:2,...] + lum_b * im_srgb[2:3,...]\n\ndef apply_gamma(im:Union[torch.Tensor, ColorImage], gamma:float) -> torch.Tensor:\n \"\"\"\n Apply arbitrary gamma correction.\n\n :param im: Image tensor\n :type im: torch.Tensor | ColorImage\n :param gamma: Gamma correction (should be in the range [0.1, 10.0])\n :return: Gamma-corrected image tensor\n \"\"\"\n if gamma == 1.: return im\n assert 0.1 <= gamma <= 10.0, \"gamma value should be in range [0.1, 10.0]\"\n im = torch.pow(im, gamma)\n\ndef apply_hue_oklab(im_oklab:Union[torch.Tensor, ColorImage], hue_delta:float) -> torch.Tensor:\n \"\"\"\n Manually shift hue of an image by a -1 to +1 delta value.\n\n :param im_oklab: Image tensor in OKLAB color space\n :type im_oklab: torch.Tensor | ColorImage\n :param hue_delta: Hue shift value in the range [-1., 1.]\n :return: Image tensor in OKLAB color space with adjusted hue\n \"\"\"\n assert -1. <= hue_delta <= 1., \"hue_delta value should be in range [-1., 1.]\"\n L, a, b = im_oklab[0:1], im_oklab[1:2], im_oklab[2:3]\n\n hue_delta = ((hue_delta * 0.5) % 1.) * 2. * torch.pi\n\n # Calculate angle and magnitude in the a-b plane\n angle = torch.atan2(b, a)\n magnitude = torch.sqrt(a**2 + b**2)\n\n # Apply hue correction\n angle += hue_delta\n\n # Convert back to Cartesian coordinates\n a_corrected = magnitude * torch.cos(angle)\n b_corrected = magnitude * torch.sin(angle)\n\n corrected = torch.cat([L, a_corrected, b_corrected], dim=0)\n return corrected\n\n return im\n\ndef col_hsv_to_rgb(hsv:Union[Float3, Color]) -> Float3:\n \"\"\"\n Convert HSV color to RGB.\n\n :param hsv: HSV color\n :type hsv: Float3 | Color\n \"\"\"\n h, s, v = hsv.x, hsv.y, hsv.z\n i = np.floor(h * 6)\n f = h * 6 - i\n p = v * (1 - s)\n q = v * (1 - f * s)\n t = v * (1 - (1 - f) * s)\n r, g, b = [\n (v, t, p),\n (q, v, p),\n (p, v, t),\n (p, q, v),\n (t, p, v),\n (v, p, q),\n ][int(i%6)]\n return Float3(r, g, b)\n\ndef col_rgb_to_hsv(rgb:Union[Float3, Color]) -> Float3:\n \"\"\"\n Convert RGB color to HSV.\n\n :param rgb: RGB color\n :type rgb: Float3 | Color\n \"\"\"\n r, g, b = rgb.r, rgb.g, rgb.b\n high = np.max([r, g, b])\n low = np.min([r, g, b])\n h, s, v = high, high, high\n d = high - low\n s = 0 if high == 0 else d/high\n if high == low:\n h = 0.0\n else:\n h = {\n r: (g - b) / d + (6 if g < b else 0),\n g: (b - r) / d + 2,\n b: (r - g) / d + 4,\n }[high]\n h /= 6\n return Float3(h, s, v)\n\n# My fairly lazy OKHSV/OKHSL \"port\" of:\n# https://github.com/holbrookdev/ok-color-picker\n# MIT License\n\n# Copyright (c) 2022 Brian Holbrook\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\n__ok_eps = 1e-7\n\ndef col_oklab_to_linear_srgb(lab:Union[Float3, Color]) -> Float3:\n \"\"\"\n Convert OKLAB color to linear sRGB.\n\n :param lab: OKLAB color\n :type lab: Float3 | Color\n \"\"\"\n L, a, b = lab.x, lab.y, lab.z\n l_ = L + 0.3963377774 * a + 0.2158037573 * b\n m_ = L - 0.1055613458 * a - 0.0638541728 * b\n s_ = L - 0.0894841775 * a - 1.291485548 * b\n\n l = l_ * l_ * l_\n m = m_ * m_ * m_\n s = s_ * s_ * s_\n\n res = Float3(\n +4.0767416621 * l - 3.3077115913 * m + 0.2309699292 * s,\n -1.2684380046 * l + 2.6097574011 * m - 0.3413193965 * s,\n -0.0041960863 * l - 0.7034186147 * m + 1.707614701 * s\n )\n return res\n\ndef col_linear_srgb_to_oklab(srgb:Union[Float3, Color]) -> Float3:\n \"\"\"\n Convert linear sRGB color to OKLAB.\n\n :param srgb: linear sRGB color\n :type srgb: Float3 | Color\n \"\"\"\n r, g, b = srgb.r, srgb.g, srgb.b\n l = 0.4122214708 * r + 0.5363325363 * g + 0.0514459929 * b\n m = 0.2119034982 * r + 0.6806995451 * g + 0.1073969566 * b\n s = 0.0883024619 * r + 0.2817188376 * g + 0.6299787005 * b\n\n l_ = np.cbrt(l)\n m_ = np.cbrt(m)\n s_ = np.cbrt(s)\n\n res = Float3(\n 0.2104542553 * l_ + 0.793617785 * m_ - 0.0040720468 * s_,\n 1.9779984951 * l_ - 2.428592205 * m_ + 0.4505937099 * s_,\n 0.0259040371 * l_ + 0.7827717662 * m_ - 0.808675766 * s_\n )\n return res\n"},"next_line":{"kind":"string","value":"def __ok_compute_max_saturation(ab:Float2) -> float:"},"gold_snippet_index":{"kind":"number","value":0,"string":"0"},"created_at":{"kind":"string","value":"2023-12-15 15:39:08+00:00"},"level":{"kind":"string","value":"16k"}}},{"rowIdx":4198,"cells":{"repo_name":{"kind":"string","value":"quocanh34/magic-animate-modified"},"file_path":{"kind":"string","value":"magicanimate/pipelines/pipeline_animation.py"},"context":{"kind":"list like","value":[{"identifier":"UNet3DConditionModel","path":"magicanimate/models/unet_controlnet.py","snippet":"class UNet3DConditionModel(ModelMixin, ConfigMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,\n in_channels: int = 4,\n out_channels: int = 4,\n center_input_sample: bool = False,\n flip_sin_to_cos: bool = True,\n freq_shift: int = 0, \n down_block_types: Tuple[str] = (\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"DownBlock3D\",\n ),\n mid_block_type: str = \"UNetMidBlock3DCrossAttn\",\n up_block_types: Tuple[str] = (\n \"UpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\"\n ),\n only_cross_attention: Union[bool, Tuple[bool]] = False,\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n layers_per_block: int = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n act_fn: str = \"silu\",\n norm_num_groups: int = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: int = 1280,\n attention_head_dim: Union[int, Tuple[int]] = 8,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n class_embed_type: Optional[str] = None,\n num_class_embeds: Optional[int] = None,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n \n # Additional\n use_motion_module = False,\n motion_module_resolutions = ( 1,2,4,8 ),\n motion_module_mid_block = False,\n motion_module_decoder_only = False,\n motion_module_type = None,\n motion_module_kwargs = {},\n unet_use_cross_frame_attention = None,\n unet_use_temporal_attention = None,\n ):\n super().__init__()\n\n self.sample_size = sample_size\n time_embed_dim = block_out_channels[0] * 4\n\n # input\n self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))\n\n # time\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\n timestep_input_dim = block_out_channels[0]\n\n self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\n\n # class embedding\n if class_embed_type is None and num_class_embeds is not None:\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\n elif class_embed_type == \"timestep\":\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\n elif class_embed_type == \"identity\":\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\n else:\n self.class_embedding = None\n\n self.down_blocks = nn.ModuleList([])\n self.mid_block = None\n self.up_blocks = nn.ModuleList([])\n\n if isinstance(only_cross_attention, bool):\n only_cross_attention = [only_cross_attention] * len(down_block_types)\n\n if isinstance(attention_head_dim, int):\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n res = 2 ** i\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block,\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attention_head_dim[i],\n downsample_padding=downsample_padding,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n \n use_motion_module=use_motion_module and (res in motion_module_resolutions) and (not motion_module_decoder_only),\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n self.down_blocks.append(down_block)\n\n # mid\n if mid_block_type == \"UNetMidBlock3DCrossAttn\":\n self.mid_block = UNetMidBlock3DCrossAttn(\n in_channels=block_out_channels[-1],\n temb_channels=time_embed_dim,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n resnet_time_scale_shift=resnet_time_scale_shift,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attention_head_dim[-1],\n resnet_groups=norm_num_groups,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n \n use_motion_module=use_motion_module and motion_module_mid_block,\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n else:\n raise ValueError(f\"unknown mid_block_type : {mid_block_type}\")\n \n # count how many layers upsample the videos\n self.num_upsamplers = 0\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n reversed_attention_head_dim = list(reversed(attention_head_dim))\n only_cross_attention = list(reversed(only_cross_attention))\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n res = 2 ** (3 - i)\n is_final_block = i == len(block_out_channels) - 1\n\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n\n # add upsample block for all BUT final layer\n if not is_final_block:\n add_upsample = True\n self.num_upsamplers += 1\n else:\n add_upsample = False\n\n up_block = get_up_block(\n up_block_type,\n num_layers=layers_per_block + 1,\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=time_embed_dim,\n add_upsample=add_upsample,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=reversed_attention_head_dim[i],\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n\n use_motion_module=use_motion_module and (res in motion_module_resolutions),\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)\n self.conv_act = nn.SiLU()\n self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)\n\n def set_attention_slice(self, slice_size):\n r\"\"\"\n Enable sliced attention computation.\n\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\n\n Args:\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\"auto\"`):\n When `\"auto\"`, halves the input to the attention heads, so attention will be computed in two steps. If\n `\"max\"`, maxium amount of memory will be saved by running only one slice at a time. If a number is\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\n must be a multiple of `slice_size`.\n \"\"\"\n sliceable_head_dims = []\n\n def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):\n if hasattr(module, \"set_attention_slice\"):\n sliceable_head_dims.append(module.sliceable_head_dim)\n\n for child in module.children():\n fn_recursive_retrieve_slicable_dims(child)\n\n # retrieve number of attention layers\n for module in self.children():\n fn_recursive_retrieve_slicable_dims(module)\n\n num_slicable_layers = len(sliceable_head_dims)\n\n if slice_size == \"auto\":\n # half the attention head size is usually a good trade-off between\n # speed and memory\n slice_size = [dim // 2 for dim in sliceable_head_dims]\n elif slice_size == \"max\":\n # make smallest slice possible\n slice_size = num_slicable_layers * [1]\n\n slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\n\n if len(slice_size) != len(sliceable_head_dims):\n raise ValueError(\n f\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\"\n f\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\"\n )\n\n for i in range(len(slice_size)):\n size = slice_size[i]\n dim = sliceable_head_dims[i]\n if size is not None and size > dim:\n raise ValueError(f\"size {size} has to be smaller or equal to {dim}.\")\n\n # Recursively walk through all the children.\n # Any children which exposes the set_attention_slice method\n # gets the message\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\n if hasattr(module, \"set_attention_slice\"):\n module.set_attention_slice(slice_size.pop())\n\n for child in module.children():\n fn_recursive_set_attention_slice(child, slice_size)\n\n reversed_slice_size = list(reversed(slice_size))\n for module in self.children():\n fn_recursive_set_attention_slice(module, reversed_slice_size)\n\n def _set_gradient_checkpointing(self, module, value=False):\n if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):\n module.gradient_checkpointing = value\n\n def forward(\n self,\n sample: torch.FloatTensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n class_labels: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n # for controlnet\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n mid_block_additional_residual: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[UNet3DConditionOutput, Tuple]:\n r\"\"\"\n Args:\n sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor\n timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps\n encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.\n\n Returns:\n [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:\n [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When\n returning a tuple, the first element is the sample tensor.\n \"\"\"\n # By default samples have to be AT least a multiple of the overall upsampling factor.\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\n # on the fly if necessary.\n default_overall_up_factor = 2**self.num_upsamplers\n\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\n forward_upsample_size = False\n upsample_size = None\n\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\n logger.info(\"Forward upsample size to force interpolation output size.\")\n forward_upsample_size = True\n\n # prepare attention_mask\n if attention_mask is not None:\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # center input if necessary\n if self.config.center_input_sample:\n sample = 2 * sample - 1.0\n\n # time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=self.dtype)\n emb = self.time_embedding(t_emb)\n\n if self.class_embedding is not None:\n if class_labels is None:\n raise ValueError(\"class_labels should be provided when num_class_embeds > 0\")\n\n if self.config.class_embed_type == \"timestep\":\n class_labels = self.time_proj(class_labels)\n\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\n emb = emb + class_emb\n\n # pre-process\n sample = self.conv_in(sample)\n\n # down\n is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None\n\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states)\n\n down_block_res_samples += res_samples\n\n if is_controlnet:\n new_down_block_res_samples = ()\n\n for down_block_res_sample, down_block_additional_residual in zip(\n down_block_res_samples, down_block_additional_residuals\n ):\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\n new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)\n\n down_block_res_samples = new_down_block_res_samples\n\n # mid\n sample = self.mid_block(\n sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\n )\n\n if is_controlnet:\n sample = sample + mid_block_additional_residual\n\n # up\n for i, upsample_block in enumerate(self.up_blocks):\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\n\n # if we have not reached the final block and need to forward the\n # upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, \"has_cross_attention\") and upsample_block.has_cross_attention:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n )\n else:\n sample = upsample_block(\n hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, encoder_hidden_states=encoder_hidden_states,\n )\n\n # post-process\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n if not return_dict:\n return (sample,)\n\n return UNet3DConditionOutput(sample=sample)\n\n @classmethod\n def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None):\n if subfolder is not None:\n pretrained_model_path = os.path.join(pretrained_model_path, subfolder)\n print(f\"loaded temporal unet's pretrained weights from {pretrained_model_path} ...\")\n\n config_file = os.path.join(pretrained_model_path, 'config.json')\n if not os.path.isfile(config_file):\n raise RuntimeError(f\"{config_file} does not exist\")\n with open(config_file, \"r\") as f:\n config = json.load(f)\n config[\"_class_name\"] = cls.__name__\n config[\"down_block_types\"] = [\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"DownBlock3D\"\n ]\n config[\"up_block_types\"] = [\n \"UpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\"\n ]\n # config[\"mid_block_type\"] = \"UNetMidBlock3DCrossAttn\"\n\n from diffusers.utils import WEIGHTS_NAME\n model = cls.from_config(config, **unet_additional_kwargs)\n model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)\n if not os.path.isfile(model_file):\n raise RuntimeError(f\"{model_file} does not exist\")\n state_dict = torch.load(model_file, map_location=\"cpu\")\n\n m, u = model.load_state_dict(state_dict, strict=False)\n print(f\"### missing keys: {len(m)}; \\n### unexpected keys: {len(u)};\")\n # print(f\"### missing keys:\\n{m}\\n### unexpected keys:\\n{u}\\n\")\n \n params = [p.numel() if \"temporal\" in n else 0 for n, p in model.named_parameters()]\n print(f\"### Temporal Module Parameters: {sum(params) / 1e6} M\")\n \n return model"},{"identifier":"ControlNetProcessor","path":"magicanimate/models/multicontrolnet.py","snippet":"class ControlNetProcessor(object):\n def __init__(\n self,\n controlnet: ControlNetModel,\n # image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],\n # controlnet_cond = torch.FloatTensor, #fix\n # conditioning_scale: float = 1.0,\n ):\n self.controlnet = controlnet\n # self.image = image\n # self.controlnet_cond = controlnet_cond #fix\n # self.conditioning_scale = conditioning_scale\n\n # def _default_height_width(self, height, width, image):\n # if isinstance(image, list):\n # image = image[0]\n\n # if height is None:\n # if isinstance(image, PIL.Image.Image):\n # height = image.height\n # elif isinstance(image, torch.Tensor):\n # height = image.shape[3]\n\n # height = (height // 8) * 8 # round down to nearest multiple of 8\n\n # if width is None:\n # if isinstance(image, PIL.Image.Image):\n # width = image.width\n # elif isinstance(image, torch.Tensor):\n # width = image.shape[2]\n\n # width = (width // 8) * 8 # round down to nearest multiple of 8\n\n # return height, width\n\n # def default_height_width(self, height, width):\n # return self._default_height_width(height, width, self.image)\n\n # def _prepare_image(self, image, width, height, batch_size, num_images_per_prompt, device, dtype):\n # if not isinstance(image, torch.Tensor):\n # if isinstance(image, PIL.Image.Image):\n # image = [image]\n\n # if isinstance(image[0], PIL.Image.Image):\n # image = [\n # np.array(i.resize((width, height), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :] for i in image\n # ]\n # image = np.concatenate(image, axis=0)\n # image = np.array(image).astype(np.float32) / 255.0\n # image = image.transpose(0, 3, 1, 2)\n # image = torch.from_numpy(image)\n # elif isinstance(image[0], torch.Tensor):\n # image = torch.cat(image, dim=0)\n\n # image_batch_size = image.shape[0]\n\n # if image_batch_size == 1:\n # repeat_by = batch_size\n # else:\n # # image batch size is the same as prompt batch size\n # repeat_by = num_images_per_prompt\n\n # image = image.repeat_interleave(repeat_by, dim=0)\n\n # image = image.to(device=device, dtype=dtype)\n\n # return image\n\n # def _check_inputs(self, image, prompt, prompt_embeds):\n # image_is_pil = isinstance(image, PIL.Image.Image)\n # image_is_tensor = isinstance(image, torch.Tensor)\n # image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n # image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n\n # if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list:\n # raise TypeError(\n # \"image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors\"\n # )\n\n # if image_is_pil:\n # image_batch_size = 1\n # elif image_is_tensor:\n # image_batch_size = image.shape[0]\n # elif image_is_pil_list:\n # image_batch_size = len(image)\n # elif image_is_tensor_list:\n # image_batch_size = len(image)\n\n # if prompt is not None and isinstance(prompt, str):\n # prompt_batch_size = 1\n # elif prompt is not None and isinstance(prompt, list):\n # prompt_batch_size = len(prompt)\n # elif prompt_embeds is not None:\n # prompt_batch_size = prompt_embeds.shape[0]\n\n # if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n # raise ValueError(\n # f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n # )\n\n # def check_inputs(self, prompt, prompt_embeds):\n # self._check_inputs(self.image, prompt, prompt_embeds)\n\n # def prepare_image(self, width, height, batch_size, num_images_per_prompt, device, do_classifier_free_guidance):\n # self.image = self._prepare_image(\n # self.image, width, height, batch_size, num_images_per_prompt, device, self.controlnet.dtype\n # )\n # if do_classifier_free_guidance:\n # self.image = torch.cat([self.image] * 2)\n\n def __call__(\n self,\n controlnet_latent_input,\n t,\n encoder_hidden_states,\n controlnet_cond, #fix\n conditioning_scale,\n return_dict,\n ) -> Tuple:\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n controlnet_latent_input,\n t,\n encoder_hidden_states,\n controlnet_cond,\n conditioning_scale, \n return_dict=False,\n )\n down_block_res_samples = [\n down_block_res_sample * conditioning_scale for down_block_res_sample in down_block_res_samples\n ]\n mid_block_res_sample *= conditioning_scale\n return (down_block_res_samples, mid_block_res_sample)"},{"identifier":"ReferenceAttentionControl","path":"magicanimate/models/mutual_self_attention.py","snippet":"class ReferenceAttentionControl():\n \n def __init__(self, \n unet,\n mode=\"write\",\n do_classifier_free_guidance=False,\n attention_auto_machine_weight = float('inf'),\n gn_auto_machine_weight = 1.0,\n style_fidelity = 1.0,\n reference_attn=True,\n reference_adain=False,\n fusion_blocks=\"midup\",\n batch_size=1, \n ) -> None:\n # 10. Modify self attention and group norm\n self.unet = unet\n assert mode in [\"read\", \"write\"]\n assert fusion_blocks in [\"midup\", \"full\"]\n self.reference_attn = reference_attn\n self.reference_adain = reference_adain\n self.fusion_blocks = fusion_blocks\n self.register_reference_hooks(\n mode, \n do_classifier_free_guidance,\n attention_auto_machine_weight,\n gn_auto_machine_weight,\n style_fidelity,\n reference_attn,\n reference_adain,\n fusion_blocks,\n batch_size=batch_size, \n )\n\n def register_reference_hooks(\n self, \n mode, \n do_classifier_free_guidance,\n attention_auto_machine_weight,\n gn_auto_machine_weight,\n style_fidelity,\n reference_attn,\n reference_adain,\n dtype=torch.float16,\n batch_size=1, \n num_images_per_prompt=1, \n device=torch.device(\"cpu\"), \n fusion_blocks='midup',\n ):\n MODE = mode\n do_classifier_free_guidance = do_classifier_free_guidance\n attention_auto_machine_weight = attention_auto_machine_weight\n gn_auto_machine_weight = gn_auto_machine_weight\n style_fidelity = style_fidelity\n reference_attn = reference_attn\n reference_adain = reference_adain\n fusion_blocks = fusion_blocks\n num_images_per_prompt = num_images_per_prompt\n dtype=dtype\n if do_classifier_free_guidance:\n uc_mask = (\n torch.Tensor([1] * batch_size * num_images_per_prompt * 16 + [0] * batch_size * num_images_per_prompt * 16)\n .to(device)\n .bool()\n )\n else:\n uc_mask = (\n torch.Tensor([0] * batch_size * num_images_per_prompt * 2)\n .to(device)\n .bool()\n )\n \n def hacked_basic_transformer_inner_forward(\n self,\n hidden_states: torch.FloatTensor,\n attention_mask: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n timestep: Optional[torch.LongTensor] = None,\n cross_attention_kwargs: Dict[str, Any] = None,\n class_labels: Optional[torch.LongTensor] = None,\n video_length=None,\n ):\n if self.use_ada_layer_norm:\n norm_hidden_states = self.norm1(hidden_states, timestep)\n elif self.use_ada_layer_norm_zero:\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(\n hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype\n )\n else:\n norm_hidden_states = self.norm1(hidden_states)\n\n # 1. Self-Attention\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n if self.only_cross_attention:\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n else:\n if MODE == \"write\":\n self.bank.append(norm_hidden_states.clone())\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n if MODE == \"read\":\n self.bank = [rearrange(d.unsqueeze(1).repeat(1, video_length, 1, 1), \"b t l c -> (b t) l c\")[:hidden_states.shape[0]] for d in self.bank]\n hidden_states_uc = self.attn1(norm_hidden_states, \n encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),\n attention_mask=attention_mask) + hidden_states\n hidden_states_c = hidden_states_uc.clone()\n _uc_mask = uc_mask.clone()\n if do_classifier_free_guidance:\n if hidden_states.shape[0] != _uc_mask.shape[0]:\n _uc_mask = (\n torch.Tensor([1] * (hidden_states.shape[0]//2) + [0] * (hidden_states.shape[0]//2))\n .to(device)\n .bool()\n )\n hidden_states_c[_uc_mask] = self.attn1(\n norm_hidden_states[_uc_mask],\n encoder_hidden_states=norm_hidden_states[_uc_mask],\n attention_mask=attention_mask,\n ) + hidden_states[_uc_mask]\n hidden_states = hidden_states_c.clone()\n \n self.bank.clear()\n if self.attn2 is not None:\n # Cross-Attention\n norm_hidden_states = (\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\n )\n hidden_states = (\n self.attn2(\n norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\n )\n + hidden_states\n )\n\n # Feed-forward\n hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states\n\n # Temporal-Attention\n if self.unet_use_temporal_attention:\n d = hidden_states.shape[1]\n hidden_states = rearrange(hidden_states, \"(b f) d c -> (b d) f c\", f=video_length)\n norm_hidden_states = (\n self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)\n )\n hidden_states = self.attn_temp(norm_hidden_states) + hidden_states\n hidden_states = rearrange(hidden_states, \"(b d) f c -> (b f) d c\", d=d)\n\n return hidden_states\n \n if self.use_ada_layer_norm_zero:\n attn_output = gate_msa.unsqueeze(1) * attn_output\n hidden_states = attn_output + hidden_states\n\n if self.attn2 is not None:\n norm_hidden_states = (\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\n )\n\n # 2. Cross-Attention\n attn_output = self.attn2(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=encoder_attention_mask,\n **cross_attention_kwargs,\n )\n hidden_states = attn_output + hidden_states\n\n # 3. Feed-forward\n norm_hidden_states = self.norm3(hidden_states)\n\n if self.use_ada_layer_norm_zero:\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]\n\n ff_output = self.ff(norm_hidden_states)\n\n if self.use_ada_layer_norm_zero:\n ff_output = gate_mlp.unsqueeze(1) * ff_output\n\n hidden_states = ff_output + hidden_states\n\n return hidden_states\n\n def hacked_mid_forward(self, *args, **kwargs):\n eps = 1e-6\n x = self.original_forward(*args, **kwargs)\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append(mean)\n self.var_bank.append(var)\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))\n var_acc = sum(self.var_bank) / float(len(self.var_bank))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n x_uc = (((x - mean) / std) * std_acc) + mean_acc\n x_c = x_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n x_c[uc_mask] = x[uc_mask]\n x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc\n self.mean_bank = []\n self.var_bank = []\n return x\n\n def hack_CrossAttnDownBlock2D_forward(\n self,\n hidden_states: torch.FloatTensor,\n temb: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n ):\n eps = 1e-6\n\n # TODO(Patrick, William) - attention mask is not used\n output_states = ()\n\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n output_states = output_states + (hidden_states,)\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n def hacked_DownBlock2D_forward(self, hidden_states, temb=None):\n eps = 1e-6\n\n output_states = ()\n\n for i, resnet in enumerate(self.resnets):\n hidden_states = resnet(hidden_states, temb)\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n output_states = output_states + (hidden_states,)\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n def hacked_CrossAttnUpBlock2D_forward(\n self,\n hidden_states: torch.FloatTensor,\n res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],\n temb: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n upsample_size: Optional[int] = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n ):\n eps = 1e-6\n # TODO(Patrick, William) - attention mask is not used\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):\n eps = 1e-6\n for i, resnet in enumerate(self.resnets):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n hidden_states = resnet(hidden_states, temb)\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n if self.reference_attn:\n if self.fusion_blocks == \"midup\":\n attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\n elif self.fusion_blocks == \"full\":\n attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)] \n attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\n\n for i, module in enumerate(attn_modules):\n module._original_inner_forward = module.forward\n module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)\n module.bank = []\n module.attn_weight = float(i) / float(len(attn_modules))\n\n if self.reference_adain:\n gn_modules = [self.unet.mid_block]\n self.unet.mid_block.gn_weight = 0\n\n down_blocks = self.unet.down_blocks\n for w, module in enumerate(down_blocks):\n module.gn_weight = 1.0 - float(w) / float(len(down_blocks))\n gn_modules.append(module)\n\n up_blocks = self.unet.up_blocks\n for w, module in enumerate(up_blocks):\n module.gn_weight = float(w) / float(len(up_blocks))\n gn_modules.append(module)\n\n for i, module in enumerate(gn_modules):\n if getattr(module, \"original_forward\", None) is None:\n module.original_forward = module.forward\n if i == 0:\n # mid_block\n module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)\n elif isinstance(module, CrossAttnDownBlock2D):\n module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)\n elif isinstance(module, DownBlock2D):\n module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)\n elif isinstance(module, CrossAttnUpBlock2D):\n module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)\n elif isinstance(module, UpBlock2D):\n module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)\n module.mean_bank = []\n module.var_bank = []\n module.gn_weight *= 2\n \n def update(self, writer, dtype=torch.float16):\n if self.reference_attn:\n if self.fusion_blocks == \"midup\":\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, _BasicTransformerBlock)]\n writer_attn_modules = [module for module in (torch_dfs(writer.unet.mid_block)+torch_dfs(writer.unet.up_blocks)) if isinstance(module, BasicTransformerBlock)]\n elif self.fusion_blocks == \"full\":\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, _BasicTransformerBlock)]\n writer_attn_modules = [module for module in torch_dfs(writer.unet) if isinstance(module, BasicTransformerBlock)]\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]) \n writer_attn_modules = sorted(writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\n for r, w in zip(reader_attn_modules, writer_attn_modules):\n r.bank = [v.clone().to(dtype) for v in w.bank]\n # w.bank.clear()\n if self.reference_adain:\n reader_gn_modules = [self.unet.mid_block]\n \n down_blocks = self.unet.down_blocks\n for w, module in enumerate(down_blocks):\n reader_gn_modules.append(module)\n\n up_blocks = self.unet.up_blocks\n for w, module in enumerate(up_blocks):\n reader_gn_modules.append(module)\n \n writer_gn_modules = [writer.unet.mid_block]\n \n down_blocks = writer.unet.down_blocks\n for w, module in enumerate(down_blocks):\n writer_gn_modules.append(module)\n\n up_blocks = writer.unet.up_blocks\n for w, module in enumerate(up_blocks):\n writer_gn_modules.append(module)\n \n for r, w in zip(reader_gn_modules, writer_gn_modules):\n if len(w.mean_bank) > 0 and isinstance(w.mean_bank[0], list):\n r.mean_bank = [[v.clone().to(dtype) for v in vl] for vl in w.mean_bank]\n r.var_bank = [[v.clone().to(dtype) for v in vl] for vl in w.var_bank]\n else:\n r.mean_bank = [v.clone().to(dtype) for v in w.mean_bank]\n r.var_bank = [v.clone().to(dtype) for v in w.var_bank]\n \n def clear(self):\n if self.reference_attn:\n if self.fusion_blocks == \"midup\":\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\n elif self.fusion_blocks == \"full\":\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\n for r in reader_attn_modules:\n r.bank.clear()\n if self.reference_adain:\n reader_gn_modules = [self.unet.mid_block]\n \n down_blocks = self.unet.down_blocks\n for w, module in enumerate(down_blocks):\n reader_gn_modules.append(module)\n\n up_blocks = self.unet.up_blocks\n for w, module in enumerate(up_blocks):\n reader_gn_modules.append(module)\n \n for r in reader_gn_modules:\n r.mean_bank.clear()\n r.var_bank.clear()"},{"identifier":"get_context_scheduler","path":"magicanimate/pipelines/context.py","snippet":"def get_context_scheduler(name: str) -> Callable:\n if name == \"uniform\":\n return uniform\n else:\n raise ValueError(f\"Unknown context_overlap policy {name}\")"},{"identifier":"get_total_steps","path":"magicanimate/pipelines/context.py","snippet":"def get_total_steps(\n scheduler,\n timesteps: List[int],\n num_steps: Optional[int] = None,\n num_frames: int = ...,\n context_size: Optional[int] = None,\n context_stride: int = 3,\n context_overlap: int = 4,\n closed_loop: bool = True,\n):\n return sum(\n len(\n list(\n scheduler(\n i,\n num_steps,\n num_frames,\n context_size,\n context_stride,\n context_overlap,\n )\n )\n )\n for i in range(len(timesteps))\n )"},{"identifier":"get_tensor_interpolation_method","path":"magicanimate/utils/util.py","snippet":"def get_tensor_interpolation_method():\n return tensor_interpolation"}],"string":"[\n {\n \"identifier\": \"UNet3DConditionModel\",\n \"path\": \"magicanimate/models/unet_controlnet.py\",\n \"snippet\": \"class UNet3DConditionModel(ModelMixin, ConfigMixin):\\n _supports_gradient_checkpointing = True\\n\\n @register_to_config\\n def __init__(\\n self,\\n sample_size: Optional[int] = None,\\n in_channels: int = 4,\\n out_channels: int = 4,\\n center_input_sample: bool = False,\\n flip_sin_to_cos: bool = True,\\n freq_shift: int = 0, \\n down_block_types: Tuple[str] = (\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"DownBlock3D\\\",\\n ),\\n mid_block_type: str = \\\"UNetMidBlock3DCrossAttn\\\",\\n up_block_types: Tuple[str] = (\\n \\\"UpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\"\\n ),\\n only_cross_attention: Union[bool, Tuple[bool]] = False,\\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\\n layers_per_block: int = 2,\\n downsample_padding: int = 1,\\n mid_block_scale_factor: float = 1,\\n act_fn: str = \\\"silu\\\",\\n norm_num_groups: int = 32,\\n norm_eps: float = 1e-5,\\n cross_attention_dim: int = 1280,\\n attention_head_dim: Union[int, Tuple[int]] = 8,\\n dual_cross_attention: bool = False,\\n use_linear_projection: bool = False,\\n class_embed_type: Optional[str] = None,\\n num_class_embeds: Optional[int] = None,\\n upcast_attention: bool = False,\\n resnet_time_scale_shift: str = \\\"default\\\",\\n \\n # Additional\\n use_motion_module = False,\\n motion_module_resolutions = ( 1,2,4,8 ),\\n motion_module_mid_block = False,\\n motion_module_decoder_only = False,\\n motion_module_type = None,\\n motion_module_kwargs = {},\\n unet_use_cross_frame_attention = None,\\n unet_use_temporal_attention = None,\\n ):\\n super().__init__()\\n\\n self.sample_size = sample_size\\n time_embed_dim = block_out_channels[0] * 4\\n\\n # input\\n self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))\\n\\n # time\\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\\n timestep_input_dim = block_out_channels[0]\\n\\n self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\\n\\n # class embedding\\n if class_embed_type is None and num_class_embeds is not None:\\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\\n elif class_embed_type == \\\"timestep\\\":\\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\\n elif class_embed_type == \\\"identity\\\":\\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\\n else:\\n self.class_embedding = None\\n\\n self.down_blocks = nn.ModuleList([])\\n self.mid_block = None\\n self.up_blocks = nn.ModuleList([])\\n\\n if isinstance(only_cross_attention, bool):\\n only_cross_attention = [only_cross_attention] * len(down_block_types)\\n\\n if isinstance(attention_head_dim, int):\\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\\n\\n # down\\n output_channel = block_out_channels[0]\\n for i, down_block_type in enumerate(down_block_types):\\n res = 2 ** i\\n input_channel = output_channel\\n output_channel = block_out_channels[i]\\n is_final_block = i == len(block_out_channels) - 1\\n\\n down_block = get_down_block(\\n down_block_type,\\n num_layers=layers_per_block,\\n in_channels=input_channel,\\n out_channels=output_channel,\\n temb_channels=time_embed_dim,\\n add_downsample=not is_final_block,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n resnet_groups=norm_num_groups,\\n cross_attention_dim=cross_attention_dim,\\n attn_num_head_channels=attention_head_dim[i],\\n downsample_padding=downsample_padding,\\n dual_cross_attention=dual_cross_attention,\\n use_linear_projection=use_linear_projection,\\n only_cross_attention=only_cross_attention[i],\\n upcast_attention=upcast_attention,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n\\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\\n unet_use_temporal_attention=unet_use_temporal_attention,\\n \\n use_motion_module=use_motion_module and (res in motion_module_resolutions) and (not motion_module_decoder_only),\\n motion_module_type=motion_module_type,\\n motion_module_kwargs=motion_module_kwargs,\\n )\\n self.down_blocks.append(down_block)\\n\\n # mid\\n if mid_block_type == \\\"UNetMidBlock3DCrossAttn\\\":\\n self.mid_block = UNetMidBlock3DCrossAttn(\\n in_channels=block_out_channels[-1],\\n temb_channels=time_embed_dim,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n output_scale_factor=mid_block_scale_factor,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n cross_attention_dim=cross_attention_dim,\\n attn_num_head_channels=attention_head_dim[-1],\\n resnet_groups=norm_num_groups,\\n dual_cross_attention=dual_cross_attention,\\n use_linear_projection=use_linear_projection,\\n upcast_attention=upcast_attention,\\n\\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\\n unet_use_temporal_attention=unet_use_temporal_attention,\\n \\n use_motion_module=use_motion_module and motion_module_mid_block,\\n motion_module_type=motion_module_type,\\n motion_module_kwargs=motion_module_kwargs,\\n )\\n else:\\n raise ValueError(f\\\"unknown mid_block_type : {mid_block_type}\\\")\\n \\n # count how many layers upsample the videos\\n self.num_upsamplers = 0\\n\\n # up\\n reversed_block_out_channels = list(reversed(block_out_channels))\\n reversed_attention_head_dim = list(reversed(attention_head_dim))\\n only_cross_attention = list(reversed(only_cross_attention))\\n output_channel = reversed_block_out_channels[0]\\n for i, up_block_type in enumerate(up_block_types):\\n res = 2 ** (3 - i)\\n is_final_block = i == len(block_out_channels) - 1\\n\\n prev_output_channel = output_channel\\n output_channel = reversed_block_out_channels[i]\\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\\n\\n # add upsample block for all BUT final layer\\n if not is_final_block:\\n add_upsample = True\\n self.num_upsamplers += 1\\n else:\\n add_upsample = False\\n\\n up_block = get_up_block(\\n up_block_type,\\n num_layers=layers_per_block + 1,\\n in_channels=input_channel,\\n out_channels=output_channel,\\n prev_output_channel=prev_output_channel,\\n temb_channels=time_embed_dim,\\n add_upsample=add_upsample,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n resnet_groups=norm_num_groups,\\n cross_attention_dim=cross_attention_dim,\\n attn_num_head_channels=reversed_attention_head_dim[i],\\n dual_cross_attention=dual_cross_attention,\\n use_linear_projection=use_linear_projection,\\n only_cross_attention=only_cross_attention[i],\\n upcast_attention=upcast_attention,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n\\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\\n unet_use_temporal_attention=unet_use_temporal_attention,\\n\\n use_motion_module=use_motion_module and (res in motion_module_resolutions),\\n motion_module_type=motion_module_type,\\n motion_module_kwargs=motion_module_kwargs,\\n )\\n self.up_blocks.append(up_block)\\n prev_output_channel = output_channel\\n\\n # out\\n self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)\\n self.conv_act = nn.SiLU()\\n self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)\\n\\n def set_attention_slice(self, slice_size):\\n r\\\"\\\"\\\"\\n Enable sliced attention computation.\\n\\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\\n\\n Args:\\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\\\"auto\\\"`):\\n When `\\\"auto\\\"`, halves the input to the attention heads, so attention will be computed in two steps. If\\n `\\\"max\\\"`, maxium amount of memory will be saved by running only one slice at a time. If a number is\\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\\n must be a multiple of `slice_size`.\\n \\\"\\\"\\\"\\n sliceable_head_dims = []\\n\\n def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n sliceable_head_dims.append(module.sliceable_head_dim)\\n\\n for child in module.children():\\n fn_recursive_retrieve_slicable_dims(child)\\n\\n # retrieve number of attention layers\\n for module in self.children():\\n fn_recursive_retrieve_slicable_dims(module)\\n\\n num_slicable_layers = len(sliceable_head_dims)\\n\\n if slice_size == \\\"auto\\\":\\n # half the attention head size is usually a good trade-off between\\n # speed and memory\\n slice_size = [dim // 2 for dim in sliceable_head_dims]\\n elif slice_size == \\\"max\\\":\\n # make smallest slice possible\\n slice_size = num_slicable_layers * [1]\\n\\n slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\\n\\n if len(slice_size) != len(sliceable_head_dims):\\n raise ValueError(\\n f\\\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\\\"\\n f\\\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\\\"\\n )\\n\\n for i in range(len(slice_size)):\\n size = slice_size[i]\\n dim = sliceable_head_dims[i]\\n if size is not None and size > dim:\\n raise ValueError(f\\\"size {size} has to be smaller or equal to {dim}.\\\")\\n\\n # Recursively walk through all the children.\\n # Any children which exposes the set_attention_slice method\\n # gets the message\\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n module.set_attention_slice(slice_size.pop())\\n\\n for child in module.children():\\n fn_recursive_set_attention_slice(child, slice_size)\\n\\n reversed_slice_size = list(reversed(slice_size))\\n for module in self.children():\\n fn_recursive_set_attention_slice(module, reversed_slice_size)\\n\\n def _set_gradient_checkpointing(self, module, value=False):\\n if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):\\n module.gradient_checkpointing = value\\n\\n def forward(\\n self,\\n sample: torch.FloatTensor,\\n timestep: Union[torch.Tensor, float, int],\\n encoder_hidden_states: torch.Tensor,\\n class_labels: Optional[torch.Tensor] = None,\\n attention_mask: Optional[torch.Tensor] = None,\\n # for controlnet\\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\\n mid_block_additional_residual: Optional[torch.Tensor] = None,\\n return_dict: bool = True,\\n ) -> Union[UNet3DConditionOutput, Tuple]:\\n r\\\"\\\"\\\"\\n Args:\\n sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor\\n timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps\\n encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states\\n return_dict (`bool`, *optional*, defaults to `True`):\\n Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.\\n\\n Returns:\\n [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:\\n [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When\\n returning a tuple, the first element is the sample tensor.\\n \\\"\\\"\\\"\\n # By default samples have to be AT least a multiple of the overall upsampling factor.\\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).\\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\\n # on the fly if necessary.\\n default_overall_up_factor = 2**self.num_upsamplers\\n\\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\\n forward_upsample_size = False\\n upsample_size = None\\n\\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\\n logger.info(\\\"Forward upsample size to force interpolation output size.\\\")\\n forward_upsample_size = True\\n\\n # prepare attention_mask\\n if attention_mask is not None:\\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\\n attention_mask = attention_mask.unsqueeze(1)\\n\\n # center input if necessary\\n if self.config.center_input_sample:\\n sample = 2 * sample - 1.0\\n\\n # time\\n timesteps = timestep\\n if not torch.is_tensor(timesteps):\\n # This would be a good case for the `match` statement (Python 3.10+)\\n is_mps = sample.device.type == \\\"mps\\\"\\n if isinstance(timestep, float):\\n dtype = torch.float32 if is_mps else torch.float64\\n else:\\n dtype = torch.int32 if is_mps else torch.int64\\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\\n elif len(timesteps.shape) == 0:\\n timesteps = timesteps[None].to(sample.device)\\n\\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\\n timesteps = timesteps.expand(sample.shape[0])\\n\\n t_emb = self.time_proj(timesteps)\\n\\n # timesteps does not contain any weights and will always return f32 tensors\\n # but time_embedding might actually be running in fp16. so we need to cast here.\\n # there might be better ways to encapsulate this.\\n t_emb = t_emb.to(dtype=self.dtype)\\n emb = self.time_embedding(t_emb)\\n\\n if self.class_embedding is not None:\\n if class_labels is None:\\n raise ValueError(\\\"class_labels should be provided when num_class_embeds > 0\\\")\\n\\n if self.config.class_embed_type == \\\"timestep\\\":\\n class_labels = self.time_proj(class_labels)\\n\\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\\n emb = emb + class_emb\\n\\n # pre-process\\n sample = self.conv_in(sample)\\n\\n # down\\n is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None\\n\\n down_block_res_samples = (sample,)\\n for downsample_block in self.down_blocks:\\n if hasattr(downsample_block, \\\"has_cross_attention\\\") and downsample_block.has_cross_attention:\\n sample, res_samples = downsample_block(\\n hidden_states=sample,\\n temb=emb,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=attention_mask,\\n )\\n else:\\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states)\\n\\n down_block_res_samples += res_samples\\n\\n if is_controlnet:\\n new_down_block_res_samples = ()\\n\\n for down_block_res_sample, down_block_additional_residual in zip(\\n down_block_res_samples, down_block_additional_residuals\\n ):\\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\\n new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)\\n\\n down_block_res_samples = new_down_block_res_samples\\n\\n # mid\\n sample = self.mid_block(\\n sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\\n )\\n\\n if is_controlnet:\\n sample = sample + mid_block_additional_residual\\n\\n # up\\n for i, upsample_block in enumerate(self.up_blocks):\\n is_final_block = i == len(self.up_blocks) - 1\\n\\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\\n\\n # if we have not reached the final block and need to forward the\\n # upsample size, we do it here\\n if not is_final_block and forward_upsample_size:\\n upsample_size = down_block_res_samples[-1].shape[2:]\\n\\n if hasattr(upsample_block, \\\"has_cross_attention\\\") and upsample_block.has_cross_attention:\\n sample = upsample_block(\\n hidden_states=sample,\\n temb=emb,\\n res_hidden_states_tuple=res_samples,\\n encoder_hidden_states=encoder_hidden_states,\\n upsample_size=upsample_size,\\n attention_mask=attention_mask,\\n )\\n else:\\n sample = upsample_block(\\n hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, encoder_hidden_states=encoder_hidden_states,\\n )\\n\\n # post-process\\n sample = self.conv_norm_out(sample)\\n sample = self.conv_act(sample)\\n sample = self.conv_out(sample)\\n\\n if not return_dict:\\n return (sample,)\\n\\n return UNet3DConditionOutput(sample=sample)\\n\\n @classmethod\\n def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None):\\n if subfolder is not None:\\n pretrained_model_path = os.path.join(pretrained_model_path, subfolder)\\n print(f\\\"loaded temporal unet's pretrained weights from {pretrained_model_path} ...\\\")\\n\\n config_file = os.path.join(pretrained_model_path, 'config.json')\\n if not os.path.isfile(config_file):\\n raise RuntimeError(f\\\"{config_file} does not exist\\\")\\n with open(config_file, \\\"r\\\") as f:\\n config = json.load(f)\\n config[\\\"_class_name\\\"] = cls.__name__\\n config[\\\"down_block_types\\\"] = [\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"DownBlock3D\\\"\\n ]\\n config[\\\"up_block_types\\\"] = [\\n \\\"UpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\"\\n ]\\n # config[\\\"mid_block_type\\\"] = \\\"UNetMidBlock3DCrossAttn\\\"\\n\\n from diffusers.utils import WEIGHTS_NAME\\n model = cls.from_config(config, **unet_additional_kwargs)\\n model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)\\n if not os.path.isfile(model_file):\\n raise RuntimeError(f\\\"{model_file} does not exist\\\")\\n state_dict = torch.load(model_file, map_location=\\\"cpu\\\")\\n\\n m, u = model.load_state_dict(state_dict, strict=False)\\n print(f\\\"### missing keys: {len(m)}; \\\\n### unexpected keys: {len(u)};\\\")\\n # print(f\\\"### missing keys:\\\\n{m}\\\\n### unexpected keys:\\\\n{u}\\\\n\\\")\\n \\n params = [p.numel() if \\\"temporal\\\" in n else 0 for n, p in model.named_parameters()]\\n print(f\\\"### Temporal Module Parameters: {sum(params) / 1e6} M\\\")\\n \\n return model\"\n },\n {\n \"identifier\": \"ControlNetProcessor\",\n \"path\": \"magicanimate/models/multicontrolnet.py\",\n \"snippet\": \"class ControlNetProcessor(object):\\n def __init__(\\n self,\\n controlnet: ControlNetModel,\\n # image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],\\n # controlnet_cond = torch.FloatTensor, #fix\\n # conditioning_scale: float = 1.0,\\n ):\\n self.controlnet = controlnet\\n # self.image = image\\n # self.controlnet_cond = controlnet_cond #fix\\n # self.conditioning_scale = conditioning_scale\\n\\n # def _default_height_width(self, height, width, image):\\n # if isinstance(image, list):\\n # image = image[0]\\n\\n # if height is None:\\n # if isinstance(image, PIL.Image.Image):\\n # height = image.height\\n # elif isinstance(image, torch.Tensor):\\n # height = image.shape[3]\\n\\n # height = (height // 8) * 8 # round down to nearest multiple of 8\\n\\n # if width is None:\\n # if isinstance(image, PIL.Image.Image):\\n # width = image.width\\n # elif isinstance(image, torch.Tensor):\\n # width = image.shape[2]\\n\\n # width = (width // 8) * 8 # round down to nearest multiple of 8\\n\\n # return height, width\\n\\n # def default_height_width(self, height, width):\\n # return self._default_height_width(height, width, self.image)\\n\\n # def _prepare_image(self, image, width, height, batch_size, num_images_per_prompt, device, dtype):\\n # if not isinstance(image, torch.Tensor):\\n # if isinstance(image, PIL.Image.Image):\\n # image = [image]\\n\\n # if isinstance(image[0], PIL.Image.Image):\\n # image = [\\n # np.array(i.resize((width, height), resample=PIL_INTERPOLATION[\\\"lanczos\\\"]))[None, :] for i in image\\n # ]\\n # image = np.concatenate(image, axis=0)\\n # image = np.array(image).astype(np.float32) / 255.0\\n # image = image.transpose(0, 3, 1, 2)\\n # image = torch.from_numpy(image)\\n # elif isinstance(image[0], torch.Tensor):\\n # image = torch.cat(image, dim=0)\\n\\n # image_batch_size = image.shape[0]\\n\\n # if image_batch_size == 1:\\n # repeat_by = batch_size\\n # else:\\n # # image batch size is the same as prompt batch size\\n # repeat_by = num_images_per_prompt\\n\\n # image = image.repeat_interleave(repeat_by, dim=0)\\n\\n # image = image.to(device=device, dtype=dtype)\\n\\n # return image\\n\\n # def _check_inputs(self, image, prompt, prompt_embeds):\\n # image_is_pil = isinstance(image, PIL.Image.Image)\\n # image_is_tensor = isinstance(image, torch.Tensor)\\n # image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\\n # image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\\n\\n # if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list:\\n # raise TypeError(\\n # \\\"image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors\\\"\\n # )\\n\\n # if image_is_pil:\\n # image_batch_size = 1\\n # elif image_is_tensor:\\n # image_batch_size = image.shape[0]\\n # elif image_is_pil_list:\\n # image_batch_size = len(image)\\n # elif image_is_tensor_list:\\n # image_batch_size = len(image)\\n\\n # if prompt is not None and isinstance(prompt, str):\\n # prompt_batch_size = 1\\n # elif prompt is not None and isinstance(prompt, list):\\n # prompt_batch_size = len(prompt)\\n # elif prompt_embeds is not None:\\n # prompt_batch_size = prompt_embeds.shape[0]\\n\\n # if image_batch_size != 1 and image_batch_size != prompt_batch_size:\\n # raise ValueError(\\n # f\\\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\\\"\\n # )\\n\\n # def check_inputs(self, prompt, prompt_embeds):\\n # self._check_inputs(self.image, prompt, prompt_embeds)\\n\\n # def prepare_image(self, width, height, batch_size, num_images_per_prompt, device, do_classifier_free_guidance):\\n # self.image = self._prepare_image(\\n # self.image, width, height, batch_size, num_images_per_prompt, device, self.controlnet.dtype\\n # )\\n # if do_classifier_free_guidance:\\n # self.image = torch.cat([self.image] * 2)\\n\\n def __call__(\\n self,\\n controlnet_latent_input,\\n t,\\n encoder_hidden_states,\\n controlnet_cond, #fix\\n conditioning_scale,\\n return_dict,\\n ) -> Tuple:\\n down_block_res_samples, mid_block_res_sample = self.controlnet(\\n controlnet_latent_input,\\n t,\\n encoder_hidden_states,\\n controlnet_cond,\\n conditioning_scale, \\n return_dict=False,\\n )\\n down_block_res_samples = [\\n down_block_res_sample * conditioning_scale for down_block_res_sample in down_block_res_samples\\n ]\\n mid_block_res_sample *= conditioning_scale\\n return (down_block_res_samples, mid_block_res_sample)\"\n },\n {\n \"identifier\": \"ReferenceAttentionControl\",\n \"path\": \"magicanimate/models/mutual_self_attention.py\",\n \"snippet\": \"class ReferenceAttentionControl():\\n \\n def __init__(self, \\n unet,\\n mode=\\\"write\\\",\\n do_classifier_free_guidance=False,\\n attention_auto_machine_weight = float('inf'),\\n gn_auto_machine_weight = 1.0,\\n style_fidelity = 1.0,\\n reference_attn=True,\\n reference_adain=False,\\n fusion_blocks=\\\"midup\\\",\\n batch_size=1, \\n ) -> None:\\n # 10. Modify self attention and group norm\\n self.unet = unet\\n assert mode in [\\\"read\\\", \\\"write\\\"]\\n assert fusion_blocks in [\\\"midup\\\", \\\"full\\\"]\\n self.reference_attn = reference_attn\\n self.reference_adain = reference_adain\\n self.fusion_blocks = fusion_blocks\\n self.register_reference_hooks(\\n mode, \\n do_classifier_free_guidance,\\n attention_auto_machine_weight,\\n gn_auto_machine_weight,\\n style_fidelity,\\n reference_attn,\\n reference_adain,\\n fusion_blocks,\\n batch_size=batch_size, \\n )\\n\\n def register_reference_hooks(\\n self, \\n mode, \\n do_classifier_free_guidance,\\n attention_auto_machine_weight,\\n gn_auto_machine_weight,\\n style_fidelity,\\n reference_attn,\\n reference_adain,\\n dtype=torch.float16,\\n batch_size=1, \\n num_images_per_prompt=1, \\n device=torch.device(\\\"cpu\\\"), \\n fusion_blocks='midup',\\n ):\\n MODE = mode\\n do_classifier_free_guidance = do_classifier_free_guidance\\n attention_auto_machine_weight = attention_auto_machine_weight\\n gn_auto_machine_weight = gn_auto_machine_weight\\n style_fidelity = style_fidelity\\n reference_attn = reference_attn\\n reference_adain = reference_adain\\n fusion_blocks = fusion_blocks\\n num_images_per_prompt = num_images_per_prompt\\n dtype=dtype\\n if do_classifier_free_guidance:\\n uc_mask = (\\n torch.Tensor([1] * batch_size * num_images_per_prompt * 16 + [0] * batch_size * num_images_per_prompt * 16)\\n .to(device)\\n .bool()\\n )\\n else:\\n uc_mask = (\\n torch.Tensor([0] * batch_size * num_images_per_prompt * 2)\\n .to(device)\\n .bool()\\n )\\n \\n def hacked_basic_transformer_inner_forward(\\n self,\\n hidden_states: torch.FloatTensor,\\n attention_mask: Optional[torch.FloatTensor] = None,\\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\\n timestep: Optional[torch.LongTensor] = None,\\n cross_attention_kwargs: Dict[str, Any] = None,\\n class_labels: Optional[torch.LongTensor] = None,\\n video_length=None,\\n ):\\n if self.use_ada_layer_norm:\\n norm_hidden_states = self.norm1(hidden_states, timestep)\\n elif self.use_ada_layer_norm_zero:\\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(\\n hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype\\n )\\n else:\\n norm_hidden_states = self.norm1(hidden_states)\\n\\n # 1. Self-Attention\\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\\n if self.only_cross_attention:\\n attn_output = self.attn1(\\n norm_hidden_states,\\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\\n attention_mask=attention_mask,\\n **cross_attention_kwargs,\\n )\\n else:\\n if MODE == \\\"write\\\":\\n self.bank.append(norm_hidden_states.clone())\\n attn_output = self.attn1(\\n norm_hidden_states,\\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\\n attention_mask=attention_mask,\\n **cross_attention_kwargs,\\n )\\n if MODE == \\\"read\\\":\\n self.bank = [rearrange(d.unsqueeze(1).repeat(1, video_length, 1, 1), \\\"b t l c -> (b t) l c\\\")[:hidden_states.shape[0]] for d in self.bank]\\n hidden_states_uc = self.attn1(norm_hidden_states, \\n encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),\\n attention_mask=attention_mask) + hidden_states\\n hidden_states_c = hidden_states_uc.clone()\\n _uc_mask = uc_mask.clone()\\n if do_classifier_free_guidance:\\n if hidden_states.shape[0] != _uc_mask.shape[0]:\\n _uc_mask = (\\n torch.Tensor([1] * (hidden_states.shape[0]//2) + [0] * (hidden_states.shape[0]//2))\\n .to(device)\\n .bool()\\n )\\n hidden_states_c[_uc_mask] = self.attn1(\\n norm_hidden_states[_uc_mask],\\n encoder_hidden_states=norm_hidden_states[_uc_mask],\\n attention_mask=attention_mask,\\n ) + hidden_states[_uc_mask]\\n hidden_states = hidden_states_c.clone()\\n \\n self.bank.clear()\\n if self.attn2 is not None:\\n # Cross-Attention\\n norm_hidden_states = (\\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\\n )\\n hidden_states = (\\n self.attn2(\\n norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\\n )\\n + hidden_states\\n )\\n\\n # Feed-forward\\n hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states\\n\\n # Temporal-Attention\\n if self.unet_use_temporal_attention:\\n d = hidden_states.shape[1]\\n hidden_states = rearrange(hidden_states, \\\"(b f) d c -> (b d) f c\\\", f=video_length)\\n norm_hidden_states = (\\n self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)\\n )\\n hidden_states = self.attn_temp(norm_hidden_states) + hidden_states\\n hidden_states = rearrange(hidden_states, \\\"(b d) f c -> (b f) d c\\\", d=d)\\n\\n return hidden_states\\n \\n if self.use_ada_layer_norm_zero:\\n attn_output = gate_msa.unsqueeze(1) * attn_output\\n hidden_states = attn_output + hidden_states\\n\\n if self.attn2 is not None:\\n norm_hidden_states = (\\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\\n )\\n\\n # 2. Cross-Attention\\n attn_output = self.attn2(\\n norm_hidden_states,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=encoder_attention_mask,\\n **cross_attention_kwargs,\\n )\\n hidden_states = attn_output + hidden_states\\n\\n # 3. Feed-forward\\n norm_hidden_states = self.norm3(hidden_states)\\n\\n if self.use_ada_layer_norm_zero:\\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]\\n\\n ff_output = self.ff(norm_hidden_states)\\n\\n if self.use_ada_layer_norm_zero:\\n ff_output = gate_mlp.unsqueeze(1) * ff_output\\n\\n hidden_states = ff_output + hidden_states\\n\\n return hidden_states\\n\\n def hacked_mid_forward(self, *args, **kwargs):\\n eps = 1e-6\\n x = self.original_forward(*args, **kwargs)\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append(mean)\\n self.var_bank.append(var)\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))\\n var_acc = sum(self.var_bank) / float(len(self.var_bank))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n x_uc = (((x - mean) / std) * std_acc) + mean_acc\\n x_c = x_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n x_c[uc_mask] = x[uc_mask]\\n x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc\\n self.mean_bank = []\\n self.var_bank = []\\n return x\\n\\n def hack_CrossAttnDownBlock2D_forward(\\n self,\\n hidden_states: torch.FloatTensor,\\n temb: Optional[torch.FloatTensor] = None,\\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\\n attention_mask: Optional[torch.FloatTensor] = None,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\\n ):\\n eps = 1e-6\\n\\n # TODO(Patrick, William) - attention mask is not used\\n output_states = ()\\n\\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\\n hidden_states = resnet(hidden_states, temb)\\n hidden_states = attn(\\n hidden_states,\\n encoder_hidden_states=encoder_hidden_states,\\n cross_attention_kwargs=cross_attention_kwargs,\\n attention_mask=attention_mask,\\n encoder_attention_mask=encoder_attention_mask,\\n return_dict=False,\\n )[0]\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n output_states = output_states + (hidden_states,)\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.downsamplers is not None:\\n for downsampler in self.downsamplers:\\n hidden_states = downsampler(hidden_states)\\n\\n output_states = output_states + (hidden_states,)\\n\\n return hidden_states, output_states\\n\\n def hacked_DownBlock2D_forward(self, hidden_states, temb=None):\\n eps = 1e-6\\n\\n output_states = ()\\n\\n for i, resnet in enumerate(self.resnets):\\n hidden_states = resnet(hidden_states, temb)\\n\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n output_states = output_states + (hidden_states,)\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.downsamplers is not None:\\n for downsampler in self.downsamplers:\\n hidden_states = downsampler(hidden_states)\\n\\n output_states = output_states + (hidden_states,)\\n\\n return hidden_states, output_states\\n\\n def hacked_CrossAttnUpBlock2D_forward(\\n self,\\n hidden_states: torch.FloatTensor,\\n res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],\\n temb: Optional[torch.FloatTensor] = None,\\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n upsample_size: Optional[int] = None,\\n attention_mask: Optional[torch.FloatTensor] = None,\\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\\n ):\\n eps = 1e-6\\n # TODO(Patrick, William) - attention mask is not used\\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\\n # pop res hidden states\\n res_hidden_states = res_hidden_states_tuple[-1]\\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\\n hidden_states = resnet(hidden_states, temb)\\n hidden_states = attn(\\n hidden_states,\\n encoder_hidden_states=encoder_hidden_states,\\n cross_attention_kwargs=cross_attention_kwargs,\\n attention_mask=attention_mask,\\n encoder_attention_mask=encoder_attention_mask,\\n return_dict=False,\\n )[0]\\n\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.upsamplers is not None:\\n for upsampler in self.upsamplers:\\n hidden_states = upsampler(hidden_states, upsample_size)\\n\\n return hidden_states\\n\\n def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):\\n eps = 1e-6\\n for i, resnet in enumerate(self.resnets):\\n # pop res hidden states\\n res_hidden_states = res_hidden_states_tuple[-1]\\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\\n hidden_states = resnet(hidden_states, temb)\\n\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.upsamplers is not None:\\n for upsampler in self.upsamplers:\\n hidden_states = upsampler(hidden_states, upsample_size)\\n\\n return hidden_states\\n\\n if self.reference_attn:\\n if self.fusion_blocks == \\\"midup\\\":\\n attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\\n elif self.fusion_blocks == \\\"full\\\":\\n attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)] \\n attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\\n\\n for i, module in enumerate(attn_modules):\\n module._original_inner_forward = module.forward\\n module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)\\n module.bank = []\\n module.attn_weight = float(i) / float(len(attn_modules))\\n\\n if self.reference_adain:\\n gn_modules = [self.unet.mid_block]\\n self.unet.mid_block.gn_weight = 0\\n\\n down_blocks = self.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n module.gn_weight = 1.0 - float(w) / float(len(down_blocks))\\n gn_modules.append(module)\\n\\n up_blocks = self.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n module.gn_weight = float(w) / float(len(up_blocks))\\n gn_modules.append(module)\\n\\n for i, module in enumerate(gn_modules):\\n if getattr(module, \\\"original_forward\\\", None) is None:\\n module.original_forward = module.forward\\n if i == 0:\\n # mid_block\\n module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)\\n elif isinstance(module, CrossAttnDownBlock2D):\\n module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)\\n elif isinstance(module, DownBlock2D):\\n module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)\\n elif isinstance(module, CrossAttnUpBlock2D):\\n module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)\\n elif isinstance(module, UpBlock2D):\\n module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)\\n module.mean_bank = []\\n module.var_bank = []\\n module.gn_weight *= 2\\n \\n def update(self, writer, dtype=torch.float16):\\n if self.reference_attn:\\n if self.fusion_blocks == \\\"midup\\\":\\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, _BasicTransformerBlock)]\\n writer_attn_modules = [module for module in (torch_dfs(writer.unet.mid_block)+torch_dfs(writer.unet.up_blocks)) if isinstance(module, BasicTransformerBlock)]\\n elif self.fusion_blocks == \\\"full\\\":\\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, _BasicTransformerBlock)]\\n writer_attn_modules = [module for module in torch_dfs(writer.unet) if isinstance(module, BasicTransformerBlock)]\\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]) \\n writer_attn_modules = sorted(writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\\n for r, w in zip(reader_attn_modules, writer_attn_modules):\\n r.bank = [v.clone().to(dtype) for v in w.bank]\\n # w.bank.clear()\\n if self.reference_adain:\\n reader_gn_modules = [self.unet.mid_block]\\n \\n down_blocks = self.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n reader_gn_modules.append(module)\\n\\n up_blocks = self.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n reader_gn_modules.append(module)\\n \\n writer_gn_modules = [writer.unet.mid_block]\\n \\n down_blocks = writer.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n writer_gn_modules.append(module)\\n\\n up_blocks = writer.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n writer_gn_modules.append(module)\\n \\n for r, w in zip(reader_gn_modules, writer_gn_modules):\\n if len(w.mean_bank) > 0 and isinstance(w.mean_bank[0], list):\\n r.mean_bank = [[v.clone().to(dtype) for v in vl] for vl in w.mean_bank]\\n r.var_bank = [[v.clone().to(dtype) for v in vl] for vl in w.var_bank]\\n else:\\n r.mean_bank = [v.clone().to(dtype) for v in w.mean_bank]\\n r.var_bank = [v.clone().to(dtype) for v in w.var_bank]\\n \\n def clear(self):\\n if self.reference_attn:\\n if self.fusion_blocks == \\\"midup\\\":\\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\\n elif self.fusion_blocks == \\\"full\\\":\\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\\n for r in reader_attn_modules:\\n r.bank.clear()\\n if self.reference_adain:\\n reader_gn_modules = [self.unet.mid_block]\\n \\n down_blocks = self.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n reader_gn_modules.append(module)\\n\\n up_blocks = self.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n reader_gn_modules.append(module)\\n \\n for r in reader_gn_modules:\\n r.mean_bank.clear()\\n r.var_bank.clear()\"\n },\n {\n \"identifier\": \"get_context_scheduler\",\n \"path\": \"magicanimate/pipelines/context.py\",\n \"snippet\": \"def get_context_scheduler(name: str) -> Callable:\\n if name == \\\"uniform\\\":\\n return uniform\\n else:\\n raise ValueError(f\\\"Unknown context_overlap policy {name}\\\")\"\n },\n {\n \"identifier\": \"get_total_steps\",\n \"path\": \"magicanimate/pipelines/context.py\",\n \"snippet\": \"def get_total_steps(\\n scheduler,\\n timesteps: List[int],\\n num_steps: Optional[int] = None,\\n num_frames: int = ...,\\n context_size: Optional[int] = None,\\n context_stride: int = 3,\\n context_overlap: int = 4,\\n closed_loop: bool = True,\\n):\\n return sum(\\n len(\\n list(\\n scheduler(\\n i,\\n num_steps,\\n num_frames,\\n context_size,\\n context_stride,\\n context_overlap,\\n )\\n )\\n )\\n for i in range(len(timesteps))\\n )\"\n },\n {\n \"identifier\": \"get_tensor_interpolation_method\",\n \"path\": \"magicanimate/utils/util.py\",\n \"snippet\": \"def get_tensor_interpolation_method():\\n return tensor_interpolation\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import inspect, math\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nfrom typing import Callable, List, Optional, Union\nfrom dataclasses import dataclass\nfrom PIL import Image\nfrom tqdm import tqdm\nfrom diffusers.utils import is_accelerate_available\nfrom packaging import version\nfrom transformers import CLIPTextModel, CLIPTokenizer\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.models import AutoencoderKL\nfrom diffusers.pipeline_utils import DiffusionPipeline\nfrom diffusers.schedulers import (\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n)\nfrom diffusers.utils import deprecate, logging, BaseOutput\nfrom einops import rearrange\nfrom magicanimate.models.unet_controlnet import UNet3DConditionModel\nfrom magicanimate.models.multicontrolnet import ControlNetProcessor #fix\nfrom magicanimate.models.mutual_self_attention import ReferenceAttentionControl\nfrom magicanimate.pipelines.context import (\n get_context_scheduler,\n get_total_steps\n)\nfrom magicanimate.utils.util import get_tensor_interpolation_method\n from accelerate import cpu_offload"},"token_num":{"kind":"number","value":13110,"string":"13,110"},"cropped_code":{"kind":"string","value":"# *************************************************************************\n# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-\n# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-\n# ytedance Inc.. \n# *************************************************************************\n\n# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py\n\n# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nTODO:\n1. support multi-controlnet\n2. [DONE] support DDIM inversion\n3. support Prompt-to-prompt\n\"\"\"\n\n\n\n\n# from magicanimate.models.controlnet import ControlNetModel\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass AnimationPipelineOutput(BaseOutput):\n videos: Union[torch.Tensor, np.ndarray]\n\n\nclass AnimationPipeline(DiffusionPipeline):\n _optional_components = []\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n"},"all_code":{"kind":"string","value":"# *************************************************************************\n# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-\n# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-\n# ytedance Inc.. \n# *************************************************************************\n\n# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py\n\n# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nTODO:\n1. support multi-controlnet\n2. [DONE] support DDIM inversion\n3. support Prompt-to-prompt\n\"\"\"\n\n\n\n\n# from magicanimate.models.controlnet import ControlNetModel\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass AnimationPipelineOutput(BaseOutput):\n videos: Union[torch.Tensor, np.ndarray]\n\n\nclass AnimationPipeline(DiffusionPipeline):\n _optional_components = []\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,"},"next_line":{"kind":"string","value":" unet: UNet3DConditionModel,"},"gold_snippet_index":{"kind":"number","value":0,"string":"0"},"created_at":{"kind":"string","value":"2023-12-15 01:22:37+00:00"},"level":{"kind":"string","value":"16k"}}},{"rowIdx":4199,"cells":{"repo_name":{"kind":"string","value":"daihaojun554/biliscrapy"},"file_path":{"kind":"string","value":"biliscrapy/network/bilibili_danmu.py"},"context":{"kind":"list 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bili_utils:\n def __init__(self):\n self.logger = logging.getLogger('log')\n self.header = headers\n self.script_dir = os.path.dirname(os.path.abspath(__file__))\n file_path = os.path.join(self.script_dir, 'bilibili_cookies.json')\n with open(file_path, 'r') as file:\n self.cookies_data = json.load(file)\n self.cookies = {cookie['name']: cookie['value'] for cookie in self.cookies_data}\n\n def bv_get(self, bvorurl):\n # https://api.bilibili.com/x/web-interface/view?bvid=BV1uG41197Tf\n # 将bv提取出来\n bv_identifier = \"BV\" # BV号的标识符\n if \"http://\" in bvorurl or \"https://\" in bvorurl: # 检查是否是一个URL\n self.logger.info(\"你输入的是http链接，正在解析...\")\n bv_index = bvorurl.find(bv_identifier)\n if bv_index != -1: # 如果找到了BV号\n bv = bvorurl[bv_index:bv_index + len(bv_identifier) + 10] # 提取BV号\n self.logger.info(f\"BV号为......: {bv}\")\n return bv\n else:\n self.logger.info(\"你输入的链接地址有误！\")\n return\n elif bv_identifier in bvorurl: # 如果输入的是BV号\n self.logger.info(f\"你输入的是BV号{bvorurl}，正在解析...\")\n bv = bvorurl\n return bv\n else:\n self.logger.info(f\"请输入正确的链接地址或BV号！,{bvorurl}\")\n return \"BV1111111111\"\n\n '''\n av 就是 oid 评论里面的参数\n '''\n\n def bv2av(self, bv):\n bv2av_url = 'https://api.bilibili.com/x/web-interface/view?bvid='\n if bv.startswith(\"BV\"):\n url = bv2av_url + str(bv)\n retry_count = 0\n max_retries = 10\n retry_delay = 1 # seconds\n while retry_count < max_retries:\n try:\n response = requests.get(url,headers=headers,cookies=self.cookies)\n response.raise_for_status() # 检查请求是否成功\n data = response.json()\n # self.logger.info(data)\n if 'data' in data and 'aid' in data['data']:\n avid = data['data']['aid']\n self.logger.info(f\"找到的avid{avid}\")\n return avid\n else:\n self.logger.info(\"未找到有效的aid值，正在重新尝试获取...\")\n retry_count += 1\n time.sleep(retry_delay)\n except (requests.RequestException, ValueError) as e:\n self.logger.info(f\"请求发生错误：{e}\")\n retry_count += 1\n self.logger.info(\"服务器返回错误！请稍后再试！\")\n self.logger.info(f\"正在重新尝试获取aid，尝试次数==>{retry_count}\")\n time.sleep(retry_delay)\n\n return None\n\n '''\n cid 是弹幕用的参数\n '''\n\n def bv2cid(self, bv):\n url = f\"https://api.bilibili.com/x/player/pagelist?bvid={str(bv)}&jsonp=jsonp\"\n retry_count = 1\n json_s = requests.get(url,headers=headers,cookies=self.cookies).json()\n self.logger.info(\"bv====》\"+bv)\n if json_s['code'] == 0:\n cid = json_s['data'][0]['cid']\n self.logger.info(\"提取出来的cid是：\" + str(cid))\n return cid\n else:\n self.logger.error(\"服务器返回错误！请稍后再试！\")\n retry_count+=1\n if retry_count > 10:\n self.logger.error(\"尝试次数过多，请稍后再试！\")\n return None\n else:\n self.logger.error(\"正在重新尝试获取cid，尝试次数==>\" + str(retry_count))\n return self.bv2cid(bv)\n\n def get_bilibili_cookies(self):\n options = webdriver.ChromeOptions()\n # options.add_argument('--headless')\n # options.add_argument('--disable-gpu')\n # 动态获取路径 不用每次都手动输入路径\n # chromedriver.exe 的路径\n # 获取当前脚本的绝对路径\n current_path = os.path.dirname(os.path.abspath(__file__))\n\n # 构建 chromedriver 的绝对路径\n driver_path = os.path.join(current_path, 'chromedriver.exe')\n\n # 创建 WebDriver 服务\n service = Service(driver_path)\n # service = Service('./chromedriver.exe')\n options.add_argument('--no-sandbox')\n options.binary_location='C:\\\\Program Files\\\\Google\\\\chrome-win64\\\\chrome.exe'\n driver = webdriver.Chrome(options=options, service=service)\n\n # 打开 Bilibili 网站\n driver.get('https://www.bilibili.com/')\n #\n login_btn = WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.CSS_SELECTOR,\n '#i_cecream > div.bili-feed4 > div.bili-header.large-header > div.bili-header__bar > ul.right-entry > li:nth-child(1) > li > div.right-entry__outside.go-login-btn')))\n login_btn.click()\n # 等待登录完成成\n time.sleep(10)\n driver.get('https://www.bilibili.com/')\n # 在这里，模拟登录流程（需要输入账号和密码）\n # 扫码登录然后，等待完成，完成的条件是屏幕上出现了某个\n\n search = WebDriverWait(driver, 20).until(\n EC.presence_of_element_located((By.CSS_SELECTOR, '#nav-searchform > div.nav-search-btn')))\n search.click()\n time.sleep(3)\n cookies = driver.get_cookies()\n # 获取当前脚本的路径\n current_path = os.path.dirname(os.path.abspath(__file__))\n with open(os.path.join(current_path, 'bilibili_cookies.json'), 'w') as f:\n # 写入当前文件\n f.write(json.dumps(cookies))\n # 写入成功\n self.logger.info('写入成功{}'.format(cookies))\n driver.quit()\n return\n\n def get_info_by_bv(self, bv):\n url = f\"https://api.bilibili.com/x/web-interface/view?bvid={str(bv)}\"\n\n def try_get(url):\n try:\n response = requests.get(url, headers=self.header, cookies=self.cookies)\n js_str = response.json()\n if js_str.get('code', 0) == 0:\n return js_str['data']\n else:\n # 可能需要根据API的设计，记录不同的错误\n self.logger.error(\n f\"Video API returned non-success code: {js_str.get('code', 'Unknown')} with message: {js_str.get('msg', 'Unknown')}\")\n except requests.exceptions.RequestException as e:\n self.logger.error(f\"An error occurred: {e}\")\n return None\n\n result = None\n retry_count = 10\n for _ in range(retry_count):\n result = try_get(url)\n if result:\n break\n\n 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},\n {\n \"identifier\": \"bili_utils\",\n \"path\": \"biliscrapy/network/bilibili_utils.py\",\n \"snippet\": \"class bili_utils:\\n def __init__(self):\\n self.logger = logging.getLogger('log')\\n self.header = headers\\n self.script_dir = os.path.dirname(os.path.abspath(__file__))\\n file_path = os.path.join(self.script_dir, 'bilibili_cookies.json')\\n with open(file_path, 'r') as file:\\n self.cookies_data = json.load(file)\\n self.cookies = {cookie['name']: cookie['value'] for cookie in self.cookies_data}\\n\\n def bv_get(self, bvorurl):\\n # https://api.bilibili.com/x/web-interface/view?bvid=BV1uG41197Tf\\n # 将bv提取出来\\n bv_identifier = \\\"BV\\\" # BV号的标识符\\n if \\\"http://\\\" in bvorurl or \\\"https://\\\" in bvorurl: # 检查是否是一个URL\\n self.logger.info(\\\"你输入的是http链接，正在解析...\\\")\\n bv_index = bvorurl.find(bv_identifier)\\n if bv_index != -1: # 如果找到了BV号\\n bv = bvorurl[bv_index:bv_index + len(bv_identifier) + 10] # 提取BV号\\n self.logger.info(f\\\"BV号为......: {bv}\\\")\\n return bv\\n else:\\n self.logger.info(\\\"你输入的链接地址有误！\\\")\\n return\\n elif bv_identifier in bvorurl: # 如果输入的是BV号\\n self.logger.info(f\\\"你输入的是BV号{bvorurl}，正在解析...\\\")\\n bv = bvorurl\\n return bv\\n else:\\n self.logger.info(f\\\"请输入正确的链接地址或BV号！,{bvorurl}\\\")\\n return \\\"BV1111111111\\\"\\n\\n '''\\n av 就是 oid 评论里面的参数\\n '''\\n\\n def bv2av(self, bv):\\n bv2av_url = 'https://api.bilibili.com/x/web-interface/view?bvid='\\n if bv.startswith(\\\"BV\\\"):\\n url = bv2av_url + str(bv)\\n retry_count = 0\\n max_retries = 10\\n retry_delay = 1 # seconds\\n while retry_count < max_retries:\\n try:\\n response = requests.get(url,headers=headers,cookies=self.cookies)\\n response.raise_for_status() # 检查请求是否成功\\n data = response.json()\\n # self.logger.info(data)\\n if 'data' in data and 'aid' in data['data']:\\n avid = data['data']['aid']\\n self.logger.info(f\\\"找到的avid{avid}\\\")\\n return avid\\n else:\\n self.logger.info(\\\"未找到有效的aid值，正在重新尝试获取...\\\")\\n retry_count += 1\\n time.sleep(retry_delay)\\n except (requests.RequestException, ValueError) as e:\\n self.logger.info(f\\\"请求发生错误：{e}\\\")\\n retry_count += 1\\n self.logger.info(\\\"服务器返回错误！请稍后再试！\\\")\\n self.logger.info(f\\\"正在重新尝试获取aid，尝试次数==>{retry_count}\\\")\\n time.sleep(retry_delay)\\n\\n return None\\n\\n '''\\n cid 是弹幕用的参数\\n '''\\n\\n def bv2cid(self, bv):\\n url = f\\\"https://api.bilibili.com/x/player/pagelist?bvid={str(bv)}&jsonp=jsonp\\\"\\n retry_count = 1\\n json_s = requests.get(url,headers=headers,cookies=self.cookies).json()\\n self.logger.info(\\\"bv====》\\\"+bv)\\n if json_s['code'] == 0:\\n cid = json_s['data'][0]['cid']\\n self.logger.info(\\\"提取出来的cid是：\\\" + str(cid))\\n return cid\\n else:\\n self.logger.error(\\\"服务器返回错误！请稍后再试！\\\")\\n retry_count+=1\\n if retry_count > 10:\\n self.logger.error(\\\"尝试次数过多，请稍后再试！\\\")\\n return None\\n else:\\n self.logger.error(\\\"正在重新尝试获取cid，尝试次数==>\\\" + str(retry_count))\\n return self.bv2cid(bv)\\n\\n def get_bilibili_cookies(self):\\n options = webdriver.ChromeOptions()\\n # options.add_argument('--headless')\\n # options.add_argument('--disable-gpu')\\n # 动态获取路径 不用每次都手动输入路径\\n # chromedriver.exe 的路径\\n # 获取当前脚本的绝对路径\\n current_path = os.path.dirname(os.path.abspath(__file__))\\n\\n # 构建 chromedriver 的绝对路径\\n driver_path = os.path.join(current_path, 'chromedriver.exe')\\n\\n # 创建 WebDriver 服务\\n service = Service(driver_path)\\n # service = Service('./chromedriver.exe')\\n options.add_argument('--no-sandbox')\\n options.binary_location='C:\\\\\\\\Program Files\\\\\\\\Google\\\\\\\\chrome-win64\\\\\\\\chrome.exe'\\n driver = webdriver.Chrome(options=options, service=service)\\n\\n # 打开 Bilibili 网站\\n driver.get('https://www.bilibili.com/')\\n #\\n login_btn = WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.CSS_SELECTOR,\\n '#i_cecream > div.bili-feed4 > div.bili-header.large-header > div.bili-header__bar > ul.right-entry > li:nth-child(1) > li > div.right-entry__outside.go-login-btn')))\\n login_btn.click()\\n # 等待登录完成成\\n time.sleep(10)\\n driver.get('https://www.bilibili.com/')\\n # 在这里，模拟登录流程（需要输入账号和密码）\\n # 扫码登录然后，等待完成，完成的条件是屏幕上出现了某个\\n\\n search = WebDriverWait(driver, 20).until(\\n EC.presence_of_element_located((By.CSS_SELECTOR, '#nav-searchform > div.nav-search-btn')))\\n search.click()\\n time.sleep(3)\\n cookies = driver.get_cookies()\\n # 获取当前脚本的路径\\n current_path = os.path.dirname(os.path.abspath(__file__))\\n with open(os.path.join(current_path, 'bilibili_cookies.json'), 'w') as f:\\n # 写入当前文件\\n f.write(json.dumps(cookies))\\n # 写入成功\\n self.logger.info('写入成功{}'.format(cookies))\\n driver.quit()\\n return\\n\\n def get_info_by_bv(self, bv):\\n url = f\\\"https://api.bilibili.com/x/web-interface/view?bvid={str(bv)}\\\"\\n\\n def try_get(url):\\n try:\\n response = requests.get(url, headers=self.header, cookies=self.cookies)\\n js_str = response.json()\\n if js_str.get('code', 0) == 0:\\n return js_str['data']\\n else:\\n # 可能需要根据API的设计，记录不同的错误\\n 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'message.bilibili.com',\n 'accept': 'application/json, text/plain, */*',\n 'accept-language': 'zh-CN,zh;q=0.9',\n 'cache-control': 'no-cache',\n 'origin': 'https://www.bilibili.com',\n 'pragma': 'no-cache',\n 'referer': 'https://www.bilibili.com/',\n 'sec-ch-ua': '\"Chromium\";v=\"118\", \"Google Chrome\";v=\"118\", \"Not=A?Brand\";v=\"99\"',\n 'sec-ch-ua-mobile': '?0',\n 'sec-ch-ua-platform': '\"Windows\"',\n 'sec-fetch-dest': 'empty',\n 'sec-fetch-mode': 'cors',\n 'sec-fetch-site': 'same-site',\n 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36',\n}\n\n\n# import bili_pb2 as Danmaku\n\nclass Danmu:\n def __init__(self):\n"},"all_code":{"kind":"string","value":"\nheaders = {\n 'authority': 'message.bilibili.com',\n 'accept': 'application/json, text/plain, */*',\n 'accept-language': 'zh-CN,zh;q=0.9',\n 'cache-control': 'no-cache',\n 'origin': 'https://www.bilibili.com',\n 'pragma': 'no-cache',\n 'referer': 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values |
|---|---|---|---|---|---|---|---|---|---|---|
timapage/pyqt6-yolov8 | main.py | [
{
"identifier": "CameraCaptureThread",
"path": "src/qt/stream/video_capture.py",
"snippet": "class CameraCaptureThread(QThread):\n send_video_info = pyqtSignal(dict)\n send_frame = pyqtSignal(list)\n def __init__(self):\n super(CameraCaptureThread, self).__init__()\n self.thread_n... | from src.qt.stream.video_capture import CameraCaptureThread
from src.qt.stream.visualize import VideoVisualizationThread
from src.qt.stream.ai_worker import AiWorkerThread
from src.ui.main_window import Ui_MainWindow
from src.qt.video.video_worker import FileProcessThread
from PyQt6 import QtGui, QtWidgets
from PyQt6.QtCore import Qt
import sys
import numpy as np | 12,247 |
class MainWindow(QtWidgets.QMainWindow, Ui_MainWindow):
def __init__(self, parent=None):
super(MainWindow, self).__init__(parent)
self.setupUi(self)
self.ai_thread = AiWorkerThread()
self.camera_thread = CameraCaptureThread()
self.display_thread = VideoVisualizationThread()
|
class MainWindow(QtWidgets.QMainWindow, Ui_MainWindow):
def __init__(self, parent=None):
super(MainWindow, self).__init__(parent)
self.setupUi(self)
self.ai_thread = AiWorkerThread()
self.camera_thread = CameraCaptureThread()
self.display_thread = VideoVisualizationThread() | self.file_process_thread = FileProcessThread() | 4 | 2023-10-18 09:21:01+00:00 | 16k |
S-LoRA/S-LoRA | slora/models/llama/model.py | [
{
"identifier": "LlamaPreLayerInfer",
"path": "slora/models/llama/layer_infer/pre_layer_infer.py",
"snippet": "class LlamaPreLayerInfer(PreLayerInferTpl):\n \"\"\"\n \"\"\"\n\n def __init__(self, tp_rank, world_size, network_config, mode):\n super().__init__(tp_rank, world_size, network_... | import os
import json
import torch
from slora.models.llama.layer_infer.pre_layer_infer import LlamaPreLayerInfer
from slora.models.llama.layer_infer.post_layer_infer import LlamaPostLayerInfer
from slora.models.llama.layer_infer.transformer_layer_infer import LlamaTransformerLayerInfer
from slora.models.llama.layer_weights.pre_and_post_layer_weight import LlamaPreAndPostLayerWeight
from slora.models.llama.layer_weights.transformer_layer_weight import LlamaTransformerLayerWeight
from slora.models.llama.infer_struct import LlamaInferStateInfo
from slora.common.mem_allocator import MemoryAllocator
from slora.common.int8kv_mem_manager import INT8KVMemoryManager
from slora.common.basemodel import TpPartBaseModel | 11,586 |
# from slora.common.mem_manager import MemoryManager
class LlamaTpPartModel(TpPartBaseModel):
# weight class
pre_and_post_weight_class = LlamaPreAndPostLayerWeight
transformer_weight_class = LlamaTransformerLayerWeight
# infer class
pre_layer_infer_class = LlamaPreLayerInfer
post_layer_infer_class = LlamaPostLayerInfer
|
# from slora.common.mem_manager import MemoryManager
class LlamaTpPartModel(TpPartBaseModel):
# weight class
pre_and_post_weight_class = LlamaPreAndPostLayerWeight
transformer_weight_class = LlamaTransformerLayerWeight
# infer class
pre_layer_infer_class = LlamaPreLayerInfer
post_layer_infer_class = LlamaPostLayerInfer | transformer_layer_infer_class = LlamaTransformerLayerInfer | 2 | 2023-11-05 04:08:36+00:00 | 16k |
fleet-ai/context | cli.py | [
{
"identifier": "print_markdown",
"path": "utils/utils.py",
"snippet": "def print_markdown(message):\n for line in message.split(\"\\n\"):\n line = line.strip()\n if line == \"\":\n print(\"\")\n elif line == \"---\":\n rprint(Rule(style=\"white\"))\n ... | import os
import openai
import sys
import argparse
import traceback
from getpass import getpass
from rich import print as rprint
from utils.utils import print_markdown, print_exception, extract_code_blocks, print_help
from utils.stream import TextStream
from utils.ai import (
retrieve_context,
construct_prompt,
get_remote_chat_response,
get_other_chat_response,
)
from constants.cli import ARGUMENTS, LIBRARIES, OPENAI_MODELS
from constants.ai import MODELS_TO_TOKENS | 13,073 | if library not in LIBRARIES:
rprint(
"Library not found. Please refer to the list of available libraries."
)
return
filters["library_name"] = args.libraries
# Get context window
if model in OPENAI_MODELS:
context_window = MODELS_TO_TOKENS[model]
else:
context_window = args.context_window
# If local model requested, use LMStudio
api_key = ""
if args.local:
model = "local-model"
print_markdown(
f"""---
**You are using a local model.**
We're working with LM Studio to provide access to local models for you. Download and start your model to get started.
Instructions:
1. Download LM Studio. You can find the download link here: https://lmstudio.ai
2. Open LM Studio and download your model of choice.
3. Click the **↔ icon** on the very left sidebar
4. Select your model and click "Start Server"
Note that your context window is set to {context_window}. To change this, run `context --context_window <context window>`.
---"""
)
else:
openrouter_key = os.environ.get("OPENROUTER_API_KEY")
openai_key = os.environ.get("OPENAI_API_KEY")
# Get the OpenAI API key, if not found
if model in OPENAI_MODELS and not openai_key:
print_markdown(
"""---
!!!**OpenAI API key not found.**
Please provide a key to proceed.
---
"""
)
openai_key = getpass("OpenAI API key: ")
os.environ["OPENAI_API_KEY"] = openai_key
print_markdown(
"""
---
**Tip**: To save this key for later, run `export OPENAI_API_KEY=<your key>` on mac/linux or `setx OPENAI_API_KEY <your key>` on windows.
For non-OpenAI models, you should set `OPENROUTER_API_KEY`, and optionally `OPENROUTER_APP_URL` and `OPENROUTER_APP_TITLE`.
---"""
)
# Otherwise, grab the openrouter key, if not found
elif model not in OPENAI_MODELS and not openrouter_key:
print_markdown(
"""---
!!!**OpenRouter API key not found.**
Please provide a key to proceed.
---
"""
)
api_key = getpass("OpenRouter API key: ")
os.environ["OPENROUTER_API_KEY"] = api_key
print_markdown(
f"""
---
**Tip**: To save this key for later, run `export OPENROUTER_API_KEY=<your key>` on mac/linux or `setx OPENROUTER_API_KEY <your key>` on windows.
You can optionally set `OPENROUTER_APP_URL` and `OPENROUTER_APP_TITLE`, too.
Note that your context window is set to {context_window}. To change this, run `context --context_window <context window>`.
---"""
)
if model == "gpt-4-1106-preview":
print_markdown(
"""!!!Welcome to Fleet Context!
Generate and run code using the most up-to-date libraries.
*Warning*: You are using gpt-4-turbo, which is not yet stable and is rate limited at 100 requests per day. Please use with caution.
"""
)
else:
print_markdown(
"""!!!Welcome to Fleet Context!
Generate and run code using the most up-to-date libraries.
"""
)
messages = []
while True:
try:
query = input("> ")
query = query.strip()
if not query:
continue
if query.lower() == "exit":
rprint("Exiting. Goodbye!")
break
messages.append({"role": "user", "content": query})
rag_context = retrieve_context(query, k=k, filters=filters)
| # pylint: disable=E0401
# pylint: disable=W0122
# pylint: disable=W0718
def main():
parser = argparse.ArgumentParser(description="Fleet Data Retriever", add_help=False)
parser.add_argument("help", nargs="?", default=argparse.SUPPRESS)
# Add arguments
for arg in ARGUMENTS:
if arg["type"] == bool:
default = arg["default"] if "default" in arg else None
parser.add_argument(
f'-{arg["nickname"]}',
f'--{arg["name"]}',
dest=arg["name"],
help=arg["help_text"],
action="store_true",
default=default,
)
elif arg["type"] == list:
choices = arg["choices"] if "choices" in arg else None
default = arg["default"] if "default" in arg else None
parser.add_argument(
f'-{arg["nickname"]}',
f'--{arg["name"]}',
dest=arg["name"],
help=arg["help_text"],
type=str,
nargs="+",
choices=choices,
default=default,
)
else:
choices = arg["choices"] if "choices" in arg else None
default = arg["default"] if "default" in arg else None
parser.add_argument(
f'-{arg["nickname"]}',
f'--{arg["name"]}',
dest=arg["name"],
help=arg["help_text"],
type=arg["type"],
choices=choices,
default=default,
)
# Hit the retrieve endpoint
args = parser.parse_args()
k = args.k_value
model = args.model
cite_sources = args.cite_sources
filters = {}
if getattr(args, "help", None) is not None:
print_help()
return
# If library specified, match library name to uuid
if args.libraries:
for library in args.libraries:
if library not in LIBRARIES:
rprint(
"Library not found. Please refer to the list of available libraries."
)
return
filters["library_name"] = args.libraries
# Get context window
if model in OPENAI_MODELS:
context_window = MODELS_TO_TOKENS[model]
else:
context_window = args.context_window
# If local model requested, use LMStudio
api_key = ""
if args.local:
model = "local-model"
print_markdown(
f"""---
**You are using a local model.**
We're working with LM Studio to provide access to local models for you. Download and start your model to get started.
Instructions:
1. Download LM Studio. You can find the download link here: https://lmstudio.ai
2. Open LM Studio and download your model of choice.
3. Click the **↔ icon** on the very left sidebar
4. Select your model and click "Start Server"
Note that your context window is set to {context_window}. To change this, run `context --context_window <context window>`.
---"""
)
else:
openrouter_key = os.environ.get("OPENROUTER_API_KEY")
openai_key = os.environ.get("OPENAI_API_KEY")
# Get the OpenAI API key, if not found
if model in OPENAI_MODELS and not openai_key:
print_markdown(
"""---
!!!**OpenAI API key not found.**
Please provide a key to proceed.
---
"""
)
openai_key = getpass("OpenAI API key: ")
os.environ["OPENAI_API_KEY"] = openai_key
print_markdown(
"""
---
**Tip**: To save this key for later, run `export OPENAI_API_KEY=<your key>` on mac/linux or `setx OPENAI_API_KEY <your key>` on windows.
For non-OpenAI models, you should set `OPENROUTER_API_KEY`, and optionally `OPENROUTER_APP_URL` and `OPENROUTER_APP_TITLE`.
---"""
)
# Otherwise, grab the openrouter key, if not found
elif model not in OPENAI_MODELS and not openrouter_key:
print_markdown(
"""---
!!!**OpenRouter API key not found.**
Please provide a key to proceed.
---
"""
)
api_key = getpass("OpenRouter API key: ")
os.environ["OPENROUTER_API_KEY"] = api_key
print_markdown(
f"""
---
**Tip**: To save this key for later, run `export OPENROUTER_API_KEY=<your key>` on mac/linux or `setx OPENROUTER_API_KEY <your key>` on windows.
You can optionally set `OPENROUTER_APP_URL` and `OPENROUTER_APP_TITLE`, too.
Note that your context window is set to {context_window}. To change this, run `context --context_window <context window>`.
---"""
)
if model == "gpt-4-1106-preview":
print_markdown(
"""!!!Welcome to Fleet Context!
Generate and run code using the most up-to-date libraries.
*Warning*: You are using gpt-4-turbo, which is not yet stable and is rate limited at 100 requests per day. Please use with caution.
"""
)
else:
print_markdown(
"""!!!Welcome to Fleet Context!
Generate and run code using the most up-to-date libraries.
"""
)
messages = []
while True:
try:
query = input("> ")
query = query.strip()
if not query:
continue
if query.lower() == "exit":
rprint("Exiting. Goodbye!")
break
messages.append({"role": "user", "content": query})
rag_context = retrieve_context(query, k=k, filters=filters) | prompts = construct_prompt( | 6 | 2023-11-02 07:07:13+00:00 | 16k |
ForceFledgling/proxyhub | proxyhub/api.py | [
{
"identifier": "Checker",
"path": "proxyhub/checker.py",
"snippet": "class Checker:\n \"\"\"Proxy checker.\"\"\"\n\n def __init__(\n self,\n judges,\n max_tries=3,\n timeout=8,\n verify_ssl=False,\n strict=False,\n dnsbl=None,\n real_ext_ip=... | import asyncio
import io
import signal
import warnings
from collections import Counter, defaultdict
from functools import partial
from pprint import pprint
from .checker import Checker
from .errors import ResolveError
from .providers import PROVIDERS, Provider
from .proxy import Proxy
from .resolver import Resolver
from .server import Server
from .utils import IPPortPatternLine, log | 12,780 |
# Pause between grabbing cycles; in seconds.
GRAB_PAUSE = 180
# The maximum number of providers that are parsed concurrently
MAX_CONCURRENT_PROVIDERS = 3
class Broker:
"""The Broker.
| One broker to rule them all, one broker to find them,
| One broker to bring them all and in the darkness bind them.
:param asyncio.Queue queue: (optional) Queue of found/checked proxies
:param int timeout: (optional) Timeout of a request in seconds
:param int max_conn:
(optional) The maximum number of concurrent checks of proxies
:param int max_tries:
(optional) The maximum number of attempts to check a proxy
:param list judges:
(optional) Urls of pages that show HTTP headers and IP address.
Or :class:`~proxyhub.judge.Judge` objects
:param list providers:
(optional) Urls of pages where to find proxies.
Or :class:`~proxyhub.providers.Provider` objects
:param bool verify_ssl:
(optional) Flag indicating whether to check the SSL certificates.
Set to True to check ssl certifications
:param loop: (optional) asyncio compatible event loop
:param stop_broker_on_sigint: (optional) whether set SIGINT signal on broker object.
Useful for a thread other than main thread.
.. deprecated:: 0.2.0
Use :attr:`max_conn` and :attr:`max_tries` instead of
:attr:`max_concurrent_conn` and :attr:`attempts_conn`.
"""
def __init__(
self,
queue=None,
timeout=8,
max_conn=200,
max_tries=3,
judges=None,
providers=None,
verify_ssl=False,
loop=None,
stop_broker_on_sigint=True,
**kwargs,
):
self._loop = loop or asyncio.get_event_loop_policy().get_event_loop()
self._proxies = queue or asyncio.Queue()
|
# Pause between grabbing cycles; in seconds.
GRAB_PAUSE = 180
# The maximum number of providers that are parsed concurrently
MAX_CONCURRENT_PROVIDERS = 3
class Broker:
"""The Broker.
| One broker to rule them all, one broker to find them,
| One broker to bring them all and in the darkness bind them.
:param asyncio.Queue queue: (optional) Queue of found/checked proxies
:param int timeout: (optional) Timeout of a request in seconds
:param int max_conn:
(optional) The maximum number of concurrent checks of proxies
:param int max_tries:
(optional) The maximum number of attempts to check a proxy
:param list judges:
(optional) Urls of pages that show HTTP headers and IP address.
Or :class:`~proxyhub.judge.Judge` objects
:param list providers:
(optional) Urls of pages where to find proxies.
Or :class:`~proxyhub.providers.Provider` objects
:param bool verify_ssl:
(optional) Flag indicating whether to check the SSL certificates.
Set to True to check ssl certifications
:param loop: (optional) asyncio compatible event loop
:param stop_broker_on_sigint: (optional) whether set SIGINT signal on broker object.
Useful for a thread other than main thread.
.. deprecated:: 0.2.0
Use :attr:`max_conn` and :attr:`max_tries` instead of
:attr:`max_concurrent_conn` and :attr:`attempts_conn`.
"""
def __init__(
self,
queue=None,
timeout=8,
max_conn=200,
max_tries=3,
judges=None,
providers=None,
verify_ssl=False,
loop=None,
stop_broker_on_sigint=True,
**kwargs,
):
self._loop = loop or asyncio.get_event_loop_policy().get_event_loop()
self._proxies = queue or asyncio.Queue() | self._resolver = Resolver(loop=self._loop) | 5 | 2023-11-05 13:28:57+00:00 | 16k |
TheFunny/ArisuAutoSweeper | module/device/method/minitouch.py | [
{
"identifier": "Config",
"path": "module/base/decorator.py",
"snippet": "class Config:\n \"\"\"\n Decorator that calls different function with a same name according to config.\n\n func_list likes:\n func_list = {\n 'func1': [\n {'options': {'ENABLE': True}, 'func': 1},\n ... | import asyncio
import json
import re
import socket
import time
import websockets
from functools import wraps
from typing import List
from adbutils.errors import AdbError
from uiautomator2 import _Service
from module.base.decorator import Config, cached_property, del_cached_property
from module.base.timer import Timer
from module.base.utils import *
from module.device.connection import Connection
from module.device.method.utils import RETRY_TRIES, retry_sleep, handle_adb_error
from module.exception import RequestHumanTakeover, ScriptError
from module.logger import logger | 12,343 | def to_atx_agent(self) -> List[str]:
return [command.to_atx_agent(self.max_x, self.max_y) for command in self.commands]
def send(self):
return self.device.minitouch_send(builder=self)
class MinitouchNotInstalledError(Exception):
pass
class MinitouchOccupiedError(Exception):
pass
class U2Service(_Service):
def __init__(self, name, u2obj):
self.name = name
self.u2obj = u2obj
self.service_url = self.u2obj.path2url("/services/" + name)
def retry(func):
@wraps(func)
def retry_wrapper(self, *args, **kwargs):
"""
Args:
self (Minitouch):
"""
init = None
for _ in range(RETRY_TRIES):
try:
if callable(init):
retry_sleep(_)
init()
return func(self, *args, **kwargs)
# Can't handle
except RequestHumanTakeover:
break
# When adb server was killed
except ConnectionResetError as e:
logger.error(e)
def init():
self.adb_reconnect()
# Emulator closed
except ConnectionAbortedError as e:
logger.error(e)
def init():
self.adb_reconnect()
# MinitouchNotInstalledError: Received empty data from minitouch
except MinitouchNotInstalledError as e:
logger.error(e)
def init():
self.install_uiautomator2()
if self._minitouch_port:
self.adb_forward_remove(f'tcp:{self._minitouch_port}')
del_cached_property(self, 'minitouch_builder')
# MinitouchOccupiedError: Timeout when connecting to minitouch
except MinitouchOccupiedError as e:
logger.error(e)
def init():
self.restart_atx()
if self._minitouch_port:
self.adb_forward_remove(f'tcp:{self._minitouch_port}')
del_cached_property(self, 'minitouch_builder')
# AdbError
except AdbError as e:
if handle_adb_error(e):
def init():
self.adb_reconnect()
else:
break
except BrokenPipeError as e:
logger.error(e)
def init():
del_cached_property(self, 'minitouch_builder')
# Unknown, probably a trucked image
except Exception as e:
logger.exception(e)
def init():
pass
logger.critical(f'Retry {func.__name__}() failed')
raise RequestHumanTakeover
return retry_wrapper
class Minitouch(Connection):
_minitouch_port: int = 0
_minitouch_client: socket.socket
_minitouch_pid: int
_minitouch_ws: websockets.WebSocketClientProtocol
max_x: int
max_y: int
@cached_property
def minitouch_builder(self):
self.minitouch_init()
return CommandBuilder(self)
@Config.when(DEVICE_OVER_HTTP=False)
def minitouch_init(self):
logger.hr('MiniTouch init')
max_x, max_y = 1280, 720
max_contacts = 2
max_pressure = 50
self.get_orientation()
self._minitouch_port = self.adb_forward("localabstract:minitouch")
# No need, minitouch already started by uiautomator2
# self.adb_shell([self.config.MINITOUCH_FILEPATH_REMOTE])
|
def random_normal_distribution(a, b, n=5):
output = np.mean(np.random.uniform(a, b, size=n))
return output
def random_theta():
theta = np.random.uniform(0, 2 * np.pi)
return np.array([np.sin(theta), np.cos(theta)])
def random_rho(dis):
return random_normal_distribution(-dis, dis)
def insert_swipe(p0, p3, speed=15, min_distance=10):
"""
Insert way point from start to end.
First generate a cubic bézier curve
Args:
p0: Start point.
p3: End point.
speed: Average move speed, pixels per 10ms.
min_distance:
Returns:
list[list[int]]: List of points.
Examples:
> insert_swipe((400, 400), (600, 600), speed=20)
[[400, 400], [406, 406], [416, 415], [429, 428], [444, 442], [462, 459], [481, 478], [504, 500], [527, 522],
[545, 540], [560, 557], [573, 570], [584, 582], [592, 590], [597, 596], [600, 600]]
"""
p0 = np.array(p0)
p3 = np.array(p3)
# Random control points in Bézier curve
distance = np.linalg.norm(p3 - p0)
p1 = 2 / 3 * p0 + 1 / 3 * p3 + random_theta() * random_rho(distance * 0.1)
p2 = 1 / 3 * p0 + 2 / 3 * p3 + random_theta() * random_rho(distance * 0.1)
# Random `t` on Bézier curve, sparse in the middle, dense at start and end
segments = max(int(distance / speed) + 1, 5)
lower = random_normal_distribution(-85, -60)
upper = random_normal_distribution(80, 90)
theta = np.arange(lower + 0., upper + 0.0001, (upper - lower) / segments)
ts = np.sin(theta / 180 * np.pi)
ts = np.sign(ts) * abs(ts) ** 0.9
ts = (ts - min(ts)) / (max(ts) - min(ts))
# Generate cubic Bézier curve
points = []
prev = (-100, -100)
for t in ts:
point = p0 * (1 - t) ** 3 + 3 * p1 * t * (1 - t) ** 2 + 3 * p2 * t ** 2 * (1 - t) + p3 * t ** 3
point = point.astype(int).tolist()
if np.linalg.norm(np.subtract(point, prev)) < min_distance:
continue
points.append(point)
prev = point
# Delete nearing points
if len(points[1:]):
distance = np.linalg.norm(np.subtract(points[1:], points[0]), axis=1)
mask = np.append(True, distance > min_distance)
points = np.array(points)[mask].tolist()
else:
points = [p0, p3]
return points
class Command:
def __init__(
self,
operation: str,
contact: int = 0,
x: int = 0,
y: int = 0,
ms: int = 10,
pressure: int = 100
):
"""
See https://github.com/openstf/minitouch#writable-to-the-socket
Args:
operation: c, r, d, m, u, w
contact:
x:
y:
ms:
pressure:
"""
self.operation = operation
self.contact = contact
self.x = x
self.y = y
self.ms = ms
self.pressure = pressure
def to_minitouch(self) -> str:
"""
String that write into minitouch socket
"""
if self.operation == 'c':
return f'{self.operation}\n'
elif self.operation == 'r':
return f'{self.operation}\n'
elif self.operation == 'd':
return f'{self.operation} {self.contact} {self.x} {self.y} {self.pressure}\n'
elif self.operation == 'm':
return f'{self.operation} {self.contact} {self.x} {self.y} {self.pressure}\n'
elif self.operation == 'u':
return f'{self.operation} {self.contact}\n'
elif self.operation == 'w':
return f'{self.operation} {self.ms}\n'
else:
return ''
def to_atx_agent(self, max_x=1280, max_y=720) -> str:
"""
Dict that send to atx-agent, $DEVICE_URL/minitouch
See https://github.com/openatx/atx-agent#minitouch%E6%93%8D%E4%BD%9C%E6%96%B9%E6%B3%95
"""
x, y = self.x / max_x, self.y / max_y
if self.operation == 'c':
out = dict(operation=self.operation)
elif self.operation == 'r':
out = dict(operation=self.operation)
elif self.operation == 'd':
out = dict(operation=self.operation, index=self.contact, pressure=self.pressure, xP=x, yP=y)
elif self.operation == 'm':
out = dict(operation=self.operation, index=self.contact, pressure=self.pressure, xP=x, yP=y)
elif self.operation == 'u':
out = dict(operation=self.operation, index=self.contact)
elif self.operation == 'w':
out = dict(operation=self.operation, milliseconds=self.ms)
else:
out = dict()
return json.dumps(out)
class CommandBuilder:
"""Build command str for minitouch.
You can use this, to custom actions as you wish::
with safe_connection(_DEVICE_ID) as connection:
builder = CommandBuilder()
builder.down(0, 400, 400, 50)
builder.commit()
builder.move(0, 500, 500, 50)
builder.commit()
builder.move(0, 800, 400, 50)
builder.commit()
builder.up(0)
builder.commit()
builder.publish(connection)
"""
DEFAULT_DELAY = 0.05
max_x = 1280
max_y = 720
def __init__(self, device, contact=0, handle_orientation=True):
"""
Args:
device:
"""
self.device = device
self.commands = []
self.delay = 0
self.contact = contact
self.handle_orientation = handle_orientation
@property
def orientation(self):
if self.handle_orientation:
return self.device.orientation
else:
return 0
def convert(self, x, y):
max_x, max_y = self.device.max_x, self.device.max_y
orientation = self.orientation
if orientation == 0:
pass
elif orientation == 1:
x, y = 720 - y, x
max_x, max_y = max_y, max_x
elif orientation == 2:
x, y = 1280 - x, 720 - y
elif orientation == 3:
x, y = y, 1280 - x
max_x, max_y = max_y, max_x
else:
raise ScriptError(f'Invalid device orientation: {orientation}')
self.max_x, self.max_y = max_x, max_y
if not self.device.config.DEVICE_OVER_HTTP:
# Maximum X and Y coordinates may, but usually do not, match the display size.
x, y = int(x / 1280 * max_x), int(y / 720 * max_y)
else:
# When over http, max_x and max_y are default to 1280 and 720, skip matching display size
x, y = int(x), int(y)
return x, y
def commit(self):
""" add minitouch command: 'c\n' """
self.commands.append(Command('c'))
return self
def reset(self):
""" add minitouch command: 'r\n' """
self.commands.append(Command('r'))
return self
def wait(self, ms=10):
""" add minitouch command: 'w <ms>\n' """
self.commands.append(Command('w', ms=ms))
self.delay += ms
return self
def up(self):
""" add minitouch command: 'u <contact>\n' """
self.commands.append(Command('u', contact=self.contact))
return self
def down(self, x, y, pressure=100):
""" add minitouch command: 'd <contact> <x> <y> <pressure>\n' """
x, y = self.convert(x, y)
self.commands.append(Command('d', x=x, y=y, contact=self.contact, pressure=pressure))
return self
def move(self, x, y, pressure=100):
""" add minitouch command: 'm <contact> <x> <y> <pressure>\n' """
x, y = self.convert(x, y)
self.commands.append(Command('m', x=x, y=y, contact=self.contact, pressure=pressure))
return self
def clear(self):
""" clear current commands """
self.commands = []
self.delay = 0
def to_minitouch(self) -> str:
return ''.join([command.to_minitouch() for command in self.commands])
def to_atx_agent(self) -> List[str]:
return [command.to_atx_agent(self.max_x, self.max_y) for command in self.commands]
def send(self):
return self.device.minitouch_send(builder=self)
class MinitouchNotInstalledError(Exception):
pass
class MinitouchOccupiedError(Exception):
pass
class U2Service(_Service):
def __init__(self, name, u2obj):
self.name = name
self.u2obj = u2obj
self.service_url = self.u2obj.path2url("/services/" + name)
def retry(func):
@wraps(func)
def retry_wrapper(self, *args, **kwargs):
"""
Args:
self (Minitouch):
"""
init = None
for _ in range(RETRY_TRIES):
try:
if callable(init):
retry_sleep(_)
init()
return func(self, *args, **kwargs)
# Can't handle
except RequestHumanTakeover:
break
# When adb server was killed
except ConnectionResetError as e:
logger.error(e)
def init():
self.adb_reconnect()
# Emulator closed
except ConnectionAbortedError as e:
logger.error(e)
def init():
self.adb_reconnect()
# MinitouchNotInstalledError: Received empty data from minitouch
except MinitouchNotInstalledError as e:
logger.error(e)
def init():
self.install_uiautomator2()
if self._minitouch_port:
self.adb_forward_remove(f'tcp:{self._minitouch_port}')
del_cached_property(self, 'minitouch_builder')
# MinitouchOccupiedError: Timeout when connecting to minitouch
except MinitouchOccupiedError as e:
logger.error(e)
def init():
self.restart_atx()
if self._minitouch_port:
self.adb_forward_remove(f'tcp:{self._minitouch_port}')
del_cached_property(self, 'minitouch_builder')
# AdbError
except AdbError as e:
if handle_adb_error(e):
def init():
self.adb_reconnect()
else:
break
except BrokenPipeError as e:
logger.error(e)
def init():
del_cached_property(self, 'minitouch_builder')
# Unknown, probably a trucked image
except Exception as e:
logger.exception(e)
def init():
pass
logger.critical(f'Retry {func.__name__}() failed')
raise RequestHumanTakeover
return retry_wrapper
class Minitouch(Connection):
_minitouch_port: int = 0
_minitouch_client: socket.socket
_minitouch_pid: int
_minitouch_ws: websockets.WebSocketClientProtocol
max_x: int
max_y: int
@cached_property
def minitouch_builder(self):
self.minitouch_init()
return CommandBuilder(self)
@Config.when(DEVICE_OVER_HTTP=False)
def minitouch_init(self):
logger.hr('MiniTouch init')
max_x, max_y = 1280, 720
max_contacts = 2
max_pressure = 50
self.get_orientation()
self._minitouch_port = self.adb_forward("localabstract:minitouch")
# No need, minitouch already started by uiautomator2
# self.adb_shell([self.config.MINITOUCH_FILEPATH_REMOTE])
| retry_timeout = Timer(2).start() | 3 | 2023-11-01 07:09:45+00:00 | 16k |
BrianPugh/cyclopts | tests/test_help.py | [
{
"identifier": "App",
"path": "cyclopts/core.py",
"snippet": "class App:\n _name: Optional[Tuple[str, ...]] = field(default=None, alias=\"name\", converter=optional_to_tuple_converter)\n\n _help: Optional[str] = field(default=None, alias=\"help\")\n\n usage: Optional[str] = field(default=None)... | import inspect
import sys
import attrs
import pytest
from enum import Enum
from textwrap import dedent
from typing import List, Literal, Optional, Union
from typing_extensions import Annotated
from typing import Annotated
from cyclopts import App, Group, Parameter
from cyclopts.help import (
HelpEntry,
HelpPanel,
create_parameter_help_panel,
format_command_entries,
format_doc,
format_usage,
)
from cyclopts.resolve import ResolvedCommand | 11,381 | """Docstring for foo.
This should not be shown.
"""
pass
panel = HelpPanel(title="Commands", format="command")
panel.entries.extend(format_command_entries((app["foo"],)))
with console.capture() as capture:
console.print(panel)
actual = capture.get()
assert actual == (
"╭─ Commands ─────────────────────────────────────────────────────────╮\n"
"│ foo Docstring for foo. │\n"
"╰────────────────────────────────────────────────────────────────────╯\n"
)
def test_format_commands_no_show(app, console):
@app.command
def foo():
"""Docstring for foo."""
pass
@app.command(show=False)
def bar():
"""Should not be shown."""
pass
panel = HelpPanel(title="Commands", format="command")
panel.entries.extend(format_command_entries((app,)))
with console.capture() as capture:
app.help_print([], console=console)
actual = capture.get()
expected = dedent(
"""\
Usage: app COMMAND
App Help String Line 1.
╭─ Commands ─────────────────────────────────────────────────────────╮
│ foo Docstring for foo. │
│ --help,-h Display this message and exit. │
│ --version Display application version. │
╰────────────────────────────────────────────────────────────────────╯
"""
)
assert actual == expected
def test_format_commands_explicit_help(app, console):
@app.command(help="Docstring for foo.")
def foo():
"""Should not be shown."""
pass
panel = HelpPanel(title="Commands", format="command")
panel.entries.extend(format_command_entries((app["foo"],)))
with console.capture() as capture:
console.print(panel)
actual = capture.get()
assert actual == (
"╭─ Commands ─────────────────────────────────────────────────────────╮\n"
"│ foo Docstring for foo. │\n"
"╰────────────────────────────────────────────────────────────────────╯\n"
)
def test_format_commands_explicit_name(app, console):
@app.command(name="bar")
def foo():
"""Docstring for bar.
This should not be shown.
"""
pass
panel = HelpPanel(title="Commands", format="command")
panel.entries.extend(format_command_entries((app["bar"],)))
with console.capture() as capture:
console.print(panel)
actual = capture.get()
assert actual == (
"╭─ Commands ─────────────────────────────────────────────────────────╮\n"
"│ bar Docstring for bar. │\n"
"╰────────────────────────────────────────────────────────────────────╯\n"
)
def test_help_empty(console):
app = App(name="foo", version_flags=[], help_flags=[])
with console.capture() as capture:
app.help_print(console=console)
actual = capture.get()
assert actual == "Usage: foo\n\n"
@pytest.fixture
def capture_format_group_parameters(console, default_function_groups):
def inner(cmd):
command = ResolvedCommand(cmd, *default_function_groups)
with console.capture() as capture:
group, iparams = command.groups_iparams[0]
cparams = [command.iparam_to_cparam[x] for x in iparams]
console.print(create_parameter_help_panel(group, iparams, cparams))
return capture.get()
return inner
def test_help_format_group_parameters(capture_format_group_parameters):
def cmd(
|
if sys.version_info < (3, 9):
else:
@pytest.fixture
def app():
return App(
name="app",
help="App Help String Line 1.",
)
def test_empty_help_panel_rich_silent(console):
help_panel = HelpPanel(format="command", title="test")
with console.capture() as capture:
console.print(help_panel)
actual = capture.get()
assert actual == ""
def test_help_default_action(app, console):
"""No command should default to help."""
with console.capture() as capture:
app([], console=console)
actual = capture.get()
expected = dedent(
"""\
Usage: app COMMAND
App Help String Line 1.
╭─ Commands ─────────────────────────────────────────────────────────╮
│ --help,-h Display this message and exit. │
│ --version Display application version. │
╰────────────────────────────────────────────────────────────────────╯
"""
)
assert actual == expected
def test_help_custom_usage(app, console):
app.usage = "My custom usage."
with console.capture() as capture:
app([], console=console)
actual = capture.get()
expected = dedent(
"""\
My custom usage.
App Help String Line 1.
╭─ Commands ─────────────────────────────────────────────────────────╮
│ --help,-h Display this message and exit. │
│ --version Display application version. │
╰────────────────────────────────────────────────────────────────────╯
"""
)
assert actual == expected
def test_help_custom_usage_subapp(app, console):
app.command(App(name="foo", usage="My custom usage."))
with console.capture() as capture:
app(["foo", "--help"], console=console)
actual = capture.get()
expected = dedent(
"""\
My custom usage.
╭─ Commands ─────────────────────────────────────────────────────────╮
│ --help,-h Display this message and exit. │
│ --version Display application version. │
╰────────────────────────────────────────────────────────────────────╯
"""
)
assert actual == expected
def test_help_default_help_flags(console):
"""Standard help flags."""
app = App(name="app", help="App Help String Line 1.")
with console.capture() as capture:
app(["--help"], console=console)
actual = capture.get()
expected = dedent(
"""\
Usage: app COMMAND
App Help String Line 1.
╭─ Commands ─────────────────────────────────────────────────────────╮
│ --help,-h Display this message and exit. │
│ --version Display application version. │
╰────────────────────────────────────────────────────────────────────╯
"""
)
assert actual == expected
def test_help_format_usage_empty(console):
app = App(
name="app",
help="App Help String Line 1.",
help_flags=[],
version_flags=[],
)
with console.capture() as capture:
console.print(format_usage(app, []))
actual = capture.get()
assert actual == "Usage: app\n\n"
def test_help_format_usage_command(app, console):
@app.command
def foo():
pass
with console.capture() as capture:
console.print(format_usage(app, []))
actual = capture.get()
assert actual == "Usage: app COMMAND\n\n"
def test_format_commands_docstring(app, console):
@app.command
def foo():
"""Docstring for foo.
This should not be shown.
"""
pass
panel = HelpPanel(title="Commands", format="command")
panel.entries.extend(format_command_entries((app["foo"],)))
with console.capture() as capture:
console.print(panel)
actual = capture.get()
assert actual == (
"╭─ Commands ─────────────────────────────────────────────────────────╮\n"
"│ foo Docstring for foo. │\n"
"╰────────────────────────────────────────────────────────────────────╯\n"
)
def test_format_commands_no_show(app, console):
@app.command
def foo():
"""Docstring for foo."""
pass
@app.command(show=False)
def bar():
"""Should not be shown."""
pass
panel = HelpPanel(title="Commands", format="command")
panel.entries.extend(format_command_entries((app,)))
with console.capture() as capture:
app.help_print([], console=console)
actual = capture.get()
expected = dedent(
"""\
Usage: app COMMAND
App Help String Line 1.
╭─ Commands ─────────────────────────────────────────────────────────╮
│ foo Docstring for foo. │
│ --help,-h Display this message and exit. │
│ --version Display application version. │
╰────────────────────────────────────────────────────────────────────╯
"""
)
assert actual == expected
def test_format_commands_explicit_help(app, console):
@app.command(help="Docstring for foo.")
def foo():
"""Should not be shown."""
pass
panel = HelpPanel(title="Commands", format="command")
panel.entries.extend(format_command_entries((app["foo"],)))
with console.capture() as capture:
console.print(panel)
actual = capture.get()
assert actual == (
"╭─ Commands ─────────────────────────────────────────────────────────╮\n"
"│ foo Docstring for foo. │\n"
"╰────────────────────────────────────────────────────────────────────╯\n"
)
def test_format_commands_explicit_name(app, console):
@app.command(name="bar")
def foo():
"""Docstring for bar.
This should not be shown.
"""
pass
panel = HelpPanel(title="Commands", format="command")
panel.entries.extend(format_command_entries((app["bar"],)))
with console.capture() as capture:
console.print(panel)
actual = capture.get()
assert actual == (
"╭─ Commands ─────────────────────────────────────────────────────────╮\n"
"│ bar Docstring for bar. │\n"
"╰────────────────────────────────────────────────────────────────────╯\n"
)
def test_help_empty(console):
app = App(name="foo", version_flags=[], help_flags=[])
with console.capture() as capture:
app.help_print(console=console)
actual = capture.get()
assert actual == "Usage: foo\n\n"
@pytest.fixture
def capture_format_group_parameters(console, default_function_groups):
def inner(cmd):
command = ResolvedCommand(cmd, *default_function_groups)
with console.capture() as capture:
group, iparams = command.groups_iparams[0]
cparams = [command.iparam_to_cparam[x] for x in iparams]
console.print(create_parameter_help_panel(group, iparams, cparams))
return capture.get()
return inner
def test_help_format_group_parameters(capture_format_group_parameters):
def cmd( | foo: Annotated[str, Parameter(help="Docstring for foo.")], | 2 | 2023-11-03 02:24:25+00:00 | 16k |
RoboFlamingo/RoboFlamingo | robot_flamingo/models/factory.py | [
{
"identifier": "BCFlamingo",
"path": "robot_flamingo/models/flamingo_bc.py",
"snippet": "class BCFlamingo(nn.Module):\n def __init__(\n self,\n vision_encoder: nn.Module,\n lang_encoder: nn.Module,\n eoc_token_id: int,\n media_token_id: int,\n vis_dim: int,\... | from logging import debug
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
from typing import Optional
from robot_flamingo.models.flamingo_bc import BCFlamingo
from robot_flamingo.models.flamingo_mpt import MPTFlamingo
from open_flamingo.src.flamingo_lm import FlamingoLMMixin
from open_flamingo.src.utils import extend_instance
from open_flamingo.src.factory import _infer_decoder_layers_attr_name
import open_clip | 13,277 | def create_model_and_transforms(
clip_vision_encoder_path: str,
clip_vision_encoder_pretrained: str,
lang_encoder_path: str,
tokenizer_path: str,
cross_attn_every_n_layers: int = 1,
use_local_files: bool = False,
decoder_layers_attr_name: str = None,
# this is the window size sampled from the episode
window_size: int = 32,
freeze_embed: bool = False,
train_params = -1,
use_gripper=False,
use_state=False,
last_action=False,
fusion_mode='',
pad_length=-1,
debug=False,
sep_resampler=False,
sep_lm_head=False,
unfreeze_vit=False,
return_feature=False,
multi_step_action=1,
llm_name='llama_9b',
pooling='max',
residual=False,
tcp_rel=False,
replan=-1,
decoder_type='lstm',
hidden_size=None,
freeze_sampler=False,
fwd_pred=False,
fwd_pred_hand=False,
no_image_patch=False,
global_latent=1,
refresh=-1,
**flamingo_kwargs,
):
"""
Initialize a Flamingo model from a pretrained vision encoder and language encoder.
Appends special tokens to the tokenizer and freezes backbones.
Args:
clip_vision_encoder_path (str): path to pretrained clip model (e.g. "ViT-B-32")
clip_vision_encoder_pretrained (str): name of pretraining dataset for clip model (e.g. "laion2b_s32b_b79k")
lang_encoder_path (str): path to pretrained language encoder
tokenizer_path (str): path to pretrained tokenizer
cross_attn_every_n_layers (int, optional): determines how often to add a cross-attention layer. Defaults to 1.
use_local_files (bool, optional): whether to use local files. Defaults to False.
decoder_layers_attr_name (str, optional): name of the decoder layers attribute. Defaults to None.
Returns:
Flamingo: Flamingo model from pretrained vision and language encoders
Image processor: Pipeline to preprocess input images
Tokenizer: A tokenizer for the language model
"""
vision_encoder, _, image_processor = open_clip.create_model_and_transforms(
clip_vision_encoder_path, pretrained=clip_vision_encoder_pretrained
)
# set the vision encoder to output the visual features
vision_encoder.visual.output_tokens = True
text_tokenizer = AutoTokenizer.from_pretrained(
tokenizer_path, local_files_only=use_local_files
)
# add Flamingo special tokens to the tokenizer
text_tokenizer.add_special_tokens(
{"additional_special_tokens": ["<|endofchunk|>", "<image>"]}
)
if text_tokenizer.pad_token is None:
# Issue: GPT models don't have a pad token, which we use to
# modify labels for the loss.
text_tokenizer.add_special_tokens({"pad_token": "<PAD>"})
if debug:
# Load the local checkpoint into a model instance.
lang_encoder = AutoModelForCausalLM.from_pretrained(lang_encoder_path, ignore_keys=["config"], trust_remote_code=True)
# Set the `init_weights` parameter to `False` to prevent the model from loading the pretrained weights.
lang_encoder.init_weights(False)
else:
print(lang_encoder_path)
lang_encoder = AutoModelForCausalLM.from_pretrained(
lang_encoder_path, local_files_only=use_local_files, trust_remote_code=True
)
# print(lang_encoder_path)
# if llm_name == 'llama':
# lang_encoder = AutoModelForCausalLM.from_pretrained(
# lang_encoder_path, local_files_only=use_local_files
# )
# else:
# # name = 'mosaicml/mpt-7b'
# config = {
# "model_type": "auto",
# "add_lm_head": True,
# }
# lang_encoder = AutoModelForCausalLM.from_pretrained(
# lang_encoder_path, local_files_only=use_local_files
# )
# hacks for MPT-1B, which doesn't have a get_input_embeddings method
if "mpt-1b-redpajama-200b" in lang_encoder_path:
class EmbeddingFnMixin:
def get_input_embeddings(self):
return self.transformer.wte
def set_input_embeddings(self, new_embeddings):
self.transformer.wte = new_embeddings
extend_instance(lang_encoder, EmbeddingFnMixin)
extend_instance(lang_encoder, FlamingoLMMixin)
if decoder_layers_attr_name is None:
decoder_layers_attr_name = _infer_decoder_layers_attr_name(lang_encoder)
lang_encoder.set_decoder_layers_attr_name(decoder_layers_attr_name)
# print(lang_encoder.base_model_prefix)
# print(getattr(lang_encoder, lang_encoder.base_model_prefix, lang_encoder))
# print(lang_encoder)
lang_encoder.resize_token_embeddings(len(text_tokenizer))
if 'llama' in llm_name:
Model_fn = BCFlamingo
elif 'mpt' in llm_name:
|
mpt_dict = {
"mpt_3b": {
"lang_encoder_path": "path_to/mpt-1b-redpajama-200b",
"tokenizer_path": "path_to/mpt-1b-redpajama-200b",
"cross_attn_every_n_layers": 1,
"openflamingo_checkpoint": "path_to/OpenFlamingo-3B-vitl-mpt1b/checkpoint.pt"
},
"mpt_dolly_3b": {
"lang_encoder_path": "path_to/mpt-1b-redpajama-200b-dolly",
"tokenizer_path": "path_to/mpt-1b-redpajama-200b-dolly",
"cross_attn_every_n_layers": 1,
"openflamingo_checkpoint": "path_to/OpenFlamingo-3B-vitl-mpt1b-langinstruct/checkpoint.pt"
},
"mpt_4b": {
"lang_encoder_path": "path_to/RedPajama-INCITE-Instruct-3B-v1",
"tokenizer_path": "path_to/RedPajama-INCITE-Instruct-3B-v1",
"cross_attn_every_n_layers": 2,
"openflamingo_checkpoint": "path_to/OpenFlamingo-4B-vitl-rpj3b-langinstruct/checkpoint.pt"
},
"mpt_base_4b": {
"lang_encoder_path": "path_to/RedPajama-INCITE-Base-3B-v1",
"tokenizer_path": "path_to/RedPajama-INCITE-Base-3B-v1",
"cross_attn_every_n_layers": 2,
"openflamingo_checkpoint": "path_to/OpenFlamingo-4B-vitl-rpj3b/checkpoint.pt"
},
"mpt_9b": {
"lang_encoder_path": "path_to/mpt-7b",
"tokenizer_path": "path_to/mpt-7b",
"cross_attn_every_n_layers": 4,
"openflamingo_checkpoint": "path_to/OpenFlamingo-9B-vitl-mpt7b/checkpoint.pt"
},
"llama_9b": {
"lang_encoder_path": "path_to/llama-7b-hf-jxu124",
"tokenizer_path": "path_to/llama-7b-hf-jxu124",
"cross_attn_every_n_layers": 4,
"openflamingo_checkpoint": "path_to/OpenFlamingo-9B/checkpoint.pt"
}
}
def get_transforms(
clip_vision_encoder_path: str = "ViT-L-14",
clip_vision_encoder_pretrained: str = "openai",
tokenizer_path: str = "path_to/llama-7b-hf-jxu124",
use_local_files: bool = False,
):
vision_encoder, _, image_processor = open_clip.create_model_and_transforms(
clip_vision_encoder_path, pretrained=clip_vision_encoder_pretrained
)
text_tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
# add Flamingo special tokens to the tokenizer
text_tokenizer.add_special_tokens(
{"additional_special_tokens": ["<|endofchunk|>", "<image>"]}
)
if text_tokenizer.pad_token is None:
# Issue: GPT models don't have a pad token, which we use to
# modify labels for the loss.
text_tokenizer.add_special_tokens({"pad_token": "<PAD>"})
return image_processor, text_tokenizer
def create_model_and_transforms(
clip_vision_encoder_path: str,
clip_vision_encoder_pretrained: str,
lang_encoder_path: str,
tokenizer_path: str,
cross_attn_every_n_layers: int = 1,
use_local_files: bool = False,
decoder_layers_attr_name: str = None,
# this is the window size sampled from the episode
window_size: int = 32,
freeze_embed: bool = False,
train_params = -1,
use_gripper=False,
use_state=False,
last_action=False,
fusion_mode='',
pad_length=-1,
debug=False,
sep_resampler=False,
sep_lm_head=False,
unfreeze_vit=False,
return_feature=False,
multi_step_action=1,
llm_name='llama_9b',
pooling='max',
residual=False,
tcp_rel=False,
replan=-1,
decoder_type='lstm',
hidden_size=None,
freeze_sampler=False,
fwd_pred=False,
fwd_pred_hand=False,
no_image_patch=False,
global_latent=1,
refresh=-1,
**flamingo_kwargs,
):
"""
Initialize a Flamingo model from a pretrained vision encoder and language encoder.
Appends special tokens to the tokenizer and freezes backbones.
Args:
clip_vision_encoder_path (str): path to pretrained clip model (e.g. "ViT-B-32")
clip_vision_encoder_pretrained (str): name of pretraining dataset for clip model (e.g. "laion2b_s32b_b79k")
lang_encoder_path (str): path to pretrained language encoder
tokenizer_path (str): path to pretrained tokenizer
cross_attn_every_n_layers (int, optional): determines how often to add a cross-attention layer. Defaults to 1.
use_local_files (bool, optional): whether to use local files. Defaults to False.
decoder_layers_attr_name (str, optional): name of the decoder layers attribute. Defaults to None.
Returns:
Flamingo: Flamingo model from pretrained vision and language encoders
Image processor: Pipeline to preprocess input images
Tokenizer: A tokenizer for the language model
"""
vision_encoder, _, image_processor = open_clip.create_model_and_transforms(
clip_vision_encoder_path, pretrained=clip_vision_encoder_pretrained
)
# set the vision encoder to output the visual features
vision_encoder.visual.output_tokens = True
text_tokenizer = AutoTokenizer.from_pretrained(
tokenizer_path, local_files_only=use_local_files
)
# add Flamingo special tokens to the tokenizer
text_tokenizer.add_special_tokens(
{"additional_special_tokens": ["<|endofchunk|>", "<image>"]}
)
if text_tokenizer.pad_token is None:
# Issue: GPT models don't have a pad token, which we use to
# modify labels for the loss.
text_tokenizer.add_special_tokens({"pad_token": "<PAD>"})
if debug:
# Load the local checkpoint into a model instance.
lang_encoder = AutoModelForCausalLM.from_pretrained(lang_encoder_path, ignore_keys=["config"], trust_remote_code=True)
# Set the `init_weights` parameter to `False` to prevent the model from loading the pretrained weights.
lang_encoder.init_weights(False)
else:
print(lang_encoder_path)
lang_encoder = AutoModelForCausalLM.from_pretrained(
lang_encoder_path, local_files_only=use_local_files, trust_remote_code=True
)
# print(lang_encoder_path)
# if llm_name == 'llama':
# lang_encoder = AutoModelForCausalLM.from_pretrained(
# lang_encoder_path, local_files_only=use_local_files
# )
# else:
# # name = 'mosaicml/mpt-7b'
# config = {
# "model_type": "auto",
# "add_lm_head": True,
# }
# lang_encoder = AutoModelForCausalLM.from_pretrained(
# lang_encoder_path, local_files_only=use_local_files
# )
# hacks for MPT-1B, which doesn't have a get_input_embeddings method
if "mpt-1b-redpajama-200b" in lang_encoder_path:
class EmbeddingFnMixin:
def get_input_embeddings(self):
return self.transformer.wte
def set_input_embeddings(self, new_embeddings):
self.transformer.wte = new_embeddings
extend_instance(lang_encoder, EmbeddingFnMixin)
extend_instance(lang_encoder, FlamingoLMMixin)
if decoder_layers_attr_name is None:
decoder_layers_attr_name = _infer_decoder_layers_attr_name(lang_encoder)
lang_encoder.set_decoder_layers_attr_name(decoder_layers_attr_name)
# print(lang_encoder.base_model_prefix)
# print(getattr(lang_encoder, lang_encoder.base_model_prefix, lang_encoder))
# print(lang_encoder)
lang_encoder.resize_token_embeddings(len(text_tokenizer))
if 'llama' in llm_name:
Model_fn = BCFlamingo
elif 'mpt' in llm_name: | Model_fn = MPTFlamingo | 1 | 2023-11-02 01:36:23+00:00 | 16k |
bigai-nlco/langsuite | langsuite/envs/teach/libs/teach/dataset/dataset.py | [
{
"identifier": "Definitions",
"path": "langsuite/envs/teach/libs/teach/dataset/definitions.py",
"snippet": "class Definitions:\n def __init__(self, definitions=None, simulator=\"THOR\", version=\"2.0\"):\n self.simulator = simulator\n self.version = version\n if definitions is N... | import json
import os
from collections import OrderedDict
from langsuite.envs.teach.libs.teach.dataset.definitions import Definitions
from langsuite.envs.teach.libs.teach.dataset.task import Task
from langsuite.envs.teach.libs.teach.dataset.task_THOR import Task_THOR | 14,080 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0
from __future__ import annotations
class Dataset:
def __init__(
self, task_type=None, definitions=None, comments="", version=None, tasks=None
):
self.version = "2.0" if version is None else version
self.task_type = task_type
self.comments = comments
self.definitions = (
Definitions(definitions=definitions, version=version)
if definitions is None
else definitions
)
self.tasks = tasks if tasks is not None else []
def add_task(self, task):
self.tasks.append(task)
def to_dict(self):
_dict = OrderedDict()
_dict["version"] = self.version
_dict["task_type"] = self.task_type
_dict["comments"] = self.comments
_dict["definitions"] = self.definitions.to_dict()
_dict["tasks"] = [x.to_dict() for x in self.tasks]
return _dict
@classmethod
def from_dict(
cls, dataset_dict, process_init_state=True, version="2.0"
) -> "Dataset":
definitions = Definitions(dataset_dict["definitions"])
if version == "2.0":
tasks = [
Task_THOR.from_dict(task_dict, definitions, process_init_state)
for task_dict in dataset_dict.get("tasks")
]
else:
tasks = [
| # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0
from __future__ import annotations
class Dataset:
def __init__(
self, task_type=None, definitions=None, comments="", version=None, tasks=None
):
self.version = "2.0" if version is None else version
self.task_type = task_type
self.comments = comments
self.definitions = (
Definitions(definitions=definitions, version=version)
if definitions is None
else definitions
)
self.tasks = tasks if tasks is not None else []
def add_task(self, task):
self.tasks.append(task)
def to_dict(self):
_dict = OrderedDict()
_dict["version"] = self.version
_dict["task_type"] = self.task_type
_dict["comments"] = self.comments
_dict["definitions"] = self.definitions.to_dict()
_dict["tasks"] = [x.to_dict() for x in self.tasks]
return _dict
@classmethod
def from_dict(
cls, dataset_dict, process_init_state=True, version="2.0"
) -> "Dataset":
definitions = Definitions(dataset_dict["definitions"])
if version == "2.0":
tasks = [
Task_THOR.from_dict(task_dict, definitions, process_init_state)
for task_dict in dataset_dict.get("tasks")
]
else:
tasks = [ | Task.from_dict(task_dict, definitions, process_init_state) | 1 | 2023-11-01 01:47:00+00:00 | 16k |
radekd91/inferno | inferno/datasets/VideoDatasetBase.py | [
{
"identifier": "KeypointNormalization",
"path": "inferno/transforms/keypoints.py",
"snippet": "class KeypointNormalization(KeypointTransform):\n\n def __init__(self, scale_x=1., scale_y=1.):\n super().__init__(scale_x, scale_y)\n\n def forward(self, points):\n # normalization the wa... | import torch
import numpy as np
import imgaug
import pandas as pd
import torch.nn.functional as F
import subprocess
import traceback
import cv2
import timeit
import cv2
import librosa
import mediapipe as mp
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
from inferno.transforms.keypoints import KeypointNormalization, KeypointScale
from inferno.utils.MediaPipeFaceOccluder import MediaPipeFaceOccluder, sizes_to_bb_batch
from pathlib import Path
from scipy.io import wavfile
from python_speech_features import logfbank
from inferno.datasets.FaceDataModuleBase import FaceDataModuleBase
from inferno.datasets.IO import (load_and_process_segmentation, process_segmentation,
load_segmentation, load_segmentation_list, load_segmentation_list_v2,
load_reconstruction_list, load_emotion_list,
load_reconstruction_list_v2, load_emotion_list_v2,
)
from inferno.datasets.ImageDatasetHelpers import bbox2point, bbpoint_warp
from inferno.utils.FaceDetector import load_landmark
from skvideo.io import vread, vreader, FFmpegReader
from inferno.layers.losses.MediaPipeLandmarkLosses import MEDIAPIPE_LANDMARK_NUMBER
from decord import VideoReader, cpu
from inferno.models.mica.MicaInputProcessing import MicaInputProcessor
from munch import Munch
from inferno.models.temporal.Preprocessors import FlamePreprocessor
from tqdm import auto
from inferno.utils.MediaPipeLandmarkDetector import np2mediapipe
from inferno.utils.DecaUtils import tensor_vis_landmarks | 13,194 | else: # landmarks are saved per frame
landmark_validity = np.ones((len(landmarks), 1), dtype=np.float32)
for i in range(start_frame, sequence_length + start_frame):
landmark_path = landmarks_dir / f"{i:05d}_000.pkl"
landmark_type, landmark = load_landmark(landmark_path)
landmarks += [landmark]
if len(landmark) == 0: # dropped detection
landmark = [0, 0]
landmark_validity[li] = 0.
elif len(landmark) > 1: # multiple faces detected
landmarks[li] = landmarks[li][0] # just take the first one for now
else:
landmark[li] = landmarks[li][0]
landmarks = np.stack(landmarks, axis=0)
# if landmark_type == "mediapipe" and self.align_images:
# # # [WARNING] mediapipe landmarks coordinates are saved in the scale [0.0-1.0] (for absolute they need to be multiplied by img size)
# # # landmarks -= 0.5
# # landmarks -= 1.
# landmarks *= 2
# # # landmarks *= 2
# landmarks -= 1
# pad landmarks with zeros if necessary to match the desired video length
if landmarks.shape[0] < sequence_length:
landmarks = np.concatenate([landmarks, np.zeros(
(sequence_length - landmarks.shape[0], *landmarks.shape[1:]),
dtype=landmarks.dtype)], axis=0)
landmark_validity = np.concatenate([landmark_validity, np.zeros((sequence_length - landmark_validity.shape[0], 1),
dtype=landmark_validity.dtype)], axis=0)
landmark_dict[landmark_type] = landmarks.astype(np.float32)
landmark_validity_dict[landmark_type] = landmark_validity
sample["landmarks"] = landmark_dict
sample["landmarks_validity"] = landmark_validity_dict
return sample
def _path_to_segmentations(self, index):
return (Path(self.output_dir) / f"segmentations_{self.segmentation_source}" / self.segmentation_type / self.video_list[self.video_indices[index]]).with_suffix("")
def _read_segmentations(self, index, start_frame=None, end_frame=None):
segmentations_dir = self._path_to_segmentations(index)
if (segmentations_dir / "segmentations.hdf5").exists(): # if random access hdf5 exists (newest), let's use it
segmentations, seg_types, seg_names = load_segmentation_list_v2(segmentations_dir / "segmentations.hdf5", start_frame, end_frame)
elif (segmentations_dir / "segmentations.pkl").exists(): # segmentations are saved in a single pickle (no random access)
segmentations, seg_types, seg_names = load_segmentation_list(segmentations_dir / "segmentations.pkl")
if start_frame is not None and end_frame is not None:
segmentations = segmentations[start_frame: end_frame]
seg_types = seg_types[start_frame: end_frame]
seg_names = seg_names[start_frame: end_frame]
if isinstance(segmentations, list):
segmentations = np.stack(segmentations, axis=0)
if segmentations.ndim == 4: # T, C=1, W, H
segmentations = segmentations[:,0,...]
if isinstance(seg_types[0], bytes):
seg_types = [seg_type.decode("utf-8") for seg_type in seg_types]
if isinstance(seg_names[0], bytes):
seg_names = [seg_name.decode("utf-8") for seg_name in seg_names]
return segmentations, seg_types, seg_names
def _retrieve_segmentations(self, index, start_frame, end_frame):
if not self.preload_videos:
# segmentations_dir = self._path_to_segmentations(index)
# if (segmentations_dir / "segmentations.hdf5").exists(): # random access hdf5 exists, let's use it
# segmentations, seg_types, seg_names = load_segmentation_list_v2(segmentations_dir / "segmentations.hdf5", start_frame, end_frame)
# elif (segmentations_dir / "segmentations.pkl").exists(): # segmentations are saved in a single pickle
# seg_images, seg_types, seg_names = load_segmentation_list(segmentations_dir / "segmentations.pkl")
# segmentations = seg_images[start_frame: end_frame]
# if isinstance(seg_images, list):
# segmentations = np.stack(seg_images, axis=0)
# if seg_images.ndim == 4: # T, C=1, W, H
# segmentations = segmentations[:,0,...]
segmentations, seg_types, seg_names = self._read_segmentations(index, start_frame, end_frame)
return segmentations, seg_types, seg_names
else:
video_path = str(self._get_video_path(index))
segmentations, seg_types, seg_names = self.seg_cache[video_path]
segmentations = segmentations[start_frame: end_frame]
seg_types = seg_types[start_frame: end_frame]
seg_names = seg_names[start_frame: end_frame]
return segmentations, seg_types, seg_names
def _load_reconstructions(self, index, rec_type, appearance=False, start_frame=None, end_frame=None):
reconstructions_dir = self._path_to_reconstructions(index, rec_type)
if (reconstructions_dir / "shape_pose_cam.hdf5").exists(): # random access hdf5 exists, let's use it
shape_pose_cam = load_reconstruction_list_v2(reconstructions_dir / "shape_pose_cam.hdf5",
start_frame=start_frame, end_frame=end_frame)
if appearance:
appearance = load_reconstruction_list_v2(reconstructions_dir / "appearance.hdf5",
start_frame=start_frame, end_frame=end_frame)
else:
appearance = None
elif (reconstructions_dir / "shape_pose_cam.pkl").exists(): # reconstructions are saved in a single pickle
shape_pose_cam = load_reconstruction_list(reconstructions_dir / "shape_pose_cam.pkl",
start_frame=start_frame, end_frame=end_frame)
if appearance:
appearance = load_reconstruction_list(reconstructions_dir / "appearance.pkl",
start_frame=start_frame, end_frame=end_frame)
else:
appearance = None
## should no longer be necessary as the start/end frame is now handled in the load_reconstruction_list function
# if start_frame is not None and end_frame is not None:
# shape_pose_cam = {key: shape_pose_cam[key][:, start_frame: end_frame] for key in shape_pose_cam.keys()}
# if appearance is not None:
# appearance = {key: appearance[key][:, start_frame: end_frame] for key in appearance.keys()}
else:
raise RuntimeError(f"Reconstruction file not found in {reconstructions_dir}")
# for key in shape_pose_cam.keys():
# shape_pose_cam[key] = np.copy(shape_pose_cam[key])
# for key in appearance.keys():
# appearance[key] = np.copy(appearance[key])
return shape_pose_cam, appearance
def _load_emotions(self, index, features=False, start_frame=None, end_frame=None):
emotions_dir = self._path_to_emotions(index)
if (emotions_dir / "emotions.hdf5").exists(): # random access hdf5 exists, let's use it
| """
Author: Radek Danecek
Copyright (c) 2023, Radek Danecek
All rights reserved.
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# Using this computer program means that you agree to the terms
# in the LICENSE file included with this software distribution.
# Any use not explicitly granted by the LICENSE is prohibited.
#
# Copyright©2022 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# For comments or questions, please email us at emote@tue.mpg.de
# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de
"""
class AbstractVideoDataset(torch.utils.data.Dataset):
def __init__(self) -> None:
super().__init__()
def _augment_sequence_sample(self, index, sample):
raise NotImplementedError()
def visualize_sample(self, sample):
raise NotImplementedError()
class VideoDatasetBase(AbstractVideoDataset):
def __init__(self,
root_path,
output_dir,
video_list,
video_metas,
video_indices,
# audio_paths,
audio_metas,
sequence_length,
audio_noise_prob=0.0,
stack_order_audio=4,
audio_normalization="layer_norm",
landmark_types="mediapipe",
segmentation_type = "bisenet",
landmark_source = "original",
segmentation_source = "original",
occlusion_length=0,
occlusion_probability_mouth = 0.0,
occlusion_probability_left_eye = 0.0,
occlusion_probability_right_eye = 0.0,
occlusion_probability_face = 0.0,
image_size=None, ## the image size that the dataset will output
transforms : imgaug.augmenters.Augmenter = None,
hack_length=False,
use_original_video=True,
include_processed_audio = True,
include_raw_audio = True,
temporal_split_start=None, # if temporally splitting the video (train, val, test), this is the start of the split
temporal_split_end=None, # if temporally splitting the video (train, val, test), this is the end of the split
preload_videos=False, # cache all videos in memory (recommended for smaller datasets)
inflate_by_video_size=False,
include_filename=False, # if True includes the filename of the video in the sample
align_images = True,
use_audio=True, #if True, includes audio in the sample
reconstruction_type = None,
return_global_pose = False,
return_appearance = False,
average_shape_decode = False,
emotion_type = None,
return_emotion_feature=False,
read_video=True,
read_audio=True,
original_image_size=None, ## the processed videos may be different in size and if they are, the landmarks will be, too. This is to remember
return_mica_images=False,
) -> None:
super().__init__()
self.root_path = root_path
self.output_dir = output_dir
self.video_list = video_list
self.video_indices = video_indices
self.video_metas = video_metas
self.sequence_length = sequence_length or 1
# self.audio_paths = audio_paths
self.audio_metas = audio_metas
self.audio_noise_prob = audio_noise_prob
self.image_size = image_size
self.original_image_size = original_image_size or image_size
self.scale = 1.25
# if the video is 25 fps and the audio is 16 kHz, stack_order_audio corresponds to 4
# (i.e. 4 consecutive filterbanks will be concatenated to sync with the visual frame)
self.stack_order_audio = stack_order_audio
self.audio_normalization = audio_normalization
self.landmark_types = landmark_types
if isinstance(self.landmark_types, str):
self.landmark_types = [self.landmark_types]
self.landmark_source = landmark_source
if isinstance(self.landmark_source, str):
self.landmark_source = [self.landmark_source] * len(self.landmark_types)
assert len(self.landmark_types) == len(self.landmark_source), "landmark_types and landmark_source must have the same length"
self.segmentation_type = segmentation_type
self.segmentation_source = segmentation_source
self.landmark_normalizer = KeypointNormalization() # postprocesses final landmarks to be in [-1, 1]
self.occluder = MediaPipeFaceOccluder()
self.occlusion_probability_mouth = occlusion_probability_mouth
self.occlusion_probability_left_eye = occlusion_probability_left_eye
self.occlusion_probability_right_eye = occlusion_probability_right_eye
self.occlusion_probability_face = occlusion_probability_face
self.occlusion_length = occlusion_length
if isinstance(self.occlusion_length, int):
self.occlusion_length = [self.occlusion_length, self.occlusion_length+1]
# self.occlusion_length = [20, 30]
self.occlusion_length = sorted(self.occlusion_length)
self.include_processed_audio = include_processed_audio
self.include_raw_audio = include_raw_audio
# self.align_images = True
# self.align_images = False
self.align_images = align_images
self.use_audio = use_audio
self.use_original_video = use_original_video
self.transforms = transforms or imgaug.augmenters.Resize((image_size, image_size))
self.hack_length = hack_length
if self.hack_length == "auto":
if self._true_len() < 64: # hacks the length for supersmall test datasets
self.hack_length = (64 // self._true_len())
if 64 % self._true_len() != 0:
self.hack_length += 1
self.hack_length = float(self.hack_length)
# useful hack to repeat the elements in the dataset for really small datasets
else:
self.hack_length = False
assert self.occlusion_length[0] >= 0
# assert self.occlusion_length[1] <= self.sequence_length + 1
self.temporal_split_start = temporal_split_start
self.temporal_split_end = temporal_split_end
self.preload_videos = preload_videos
self.inflate_by_video_size = inflate_by_video_size
# self.read_video = True
self.read_video = read_video
self.read_audio = read_audio
self.reconstruction_type = reconstruction_type
if self.reconstruction_type is not None:
if isinstance(self.reconstruction_type, str):
self.reconstruction_type = [self.reconstruction_type]
assert isinstance(self.reconstruction_type, list), "reconstruction_type must be a list or None"
self.return_global_pose = return_global_pose
self.return_appearance = return_appearance
self.average_shape_decode = average_shape_decode
self.emotion_type = emotion_type
self.return_emotion_feature = return_emotion_feature
self.video_cache = {}
self.audio_cache = {}
self.seg_cache = {}
self.lmk_cache = {}
self.rec_cache = {}
self.emo_cache = {}
if self.preload_videos:
self._preload_videos()
self.video_sample_indices = None
if self.inflate_by_video_size:
self._inflate_by_video_size()
self.include_filename = include_filename
# if True, face alignment will not crash if invalid. By default this should be False to avoid silent data errors
self._allow_alignment_fail = False
self.return_mica_image = return_mica_images
if not self.read_video:
assert not bool(self.return_mica_image), "return_mica_image is only supported when read_video is True"
if bool(self.return_mica_image):
if self.return_mica_image is True:
self.return_mica_image = "fan"
self.mica_preprocessor = MicaInputProcessor(self.return_mica_image)
@property
def invalid_cutoff(self):
max_cutoff = 0
for rec_type in self.reconstruction_type:
if rec_type == "spectre":
max_cutoff = max(max_cutoff, 2 )
elif rec_type in ["emoca", "deca"] or "emoca" in rec_type.lower() or "emica" in rec_type.lower() or "edeca" in rec_type.lower():
max_cutoff = max(max_cutoff, 0 )
else:
raise ValueError(f"Invalid reconstruction type: '{rec_type}'")
return max_cutoff
def _load_flame(self):
if self.reconstruction_type is not None:
flame_cfg = Munch()
flame_cfg.type = "flame"
flame_cfg.flame = Munch({
"flame_model_path": "/ps/scratch/rdanecek/data/FLAME/geometry/generic_model.pkl" ,
"n_shape": 100 ,
# n_exp: 100
"n_exp": 50,
"flame_lmk_embedding_path": "/ps/scratch/rdanecek/data/FLAME/geometry/landmark_embedding.npy"
})
flame_cfg.use_texture = False
self.flame = FlamePreprocessor(flame_cfg)
# prep = prep.to("cuda")
def _preload_videos(self):
# indices = np.unique(self.video_indices)
for i in auto.tqdm( range(len(self.video_indices)), desc="Preloading videos" ):
video_path = str(self._get_video_path(i))
if self.read_video:
if video_path not in self.video_cache:
self.video_cache[video_path] = vread(video_path)
self.seg_cache[video_path] = self._read_segmentations(i)
if self.read_audio:
if video_path not in self.audio_cache:
self.audio_cache[i] = self._read_audio(i)
for lmk_type, lmk_source in zip(self.landmark_types, self.landmark_source):
if i not in self.lmk_cache:
self.lmk_cache[i] = {}
if lmk_type not in self.lmk_cache[i]:
self.lmk_cache[i][lmk_type] = {}
# if lmk_source not in self.lmk_cache[i][lmk_type]:
self.lmk_cache[i][lmk_type][lmk_source] = self._read_landmarks(i, lmk_type, lmk_source)
if self.reconstruction_type is not None:
for rec_type in self.reconstruction_type:
shape_pose_cam, appearance = self._load_reconstructions(i, rec_type, self.return_appearance)
if i not in self.rec_cache:
self.rec_cache[i] = {}
video_dict = self.rec_cache[i]
if rec_type not in video_dict:
video_dict[rec_type] = {}
self.rec_cache[i][rec_type]["shape_pose_cam"] = shape_pose_cam
self.rec_cache[i][rec_type]["appearance"] = appearance
if self.emotion_type is not None:
emotions, features = self._load_emotions(i, features=self.return_emotion_feature)
if i not in self.emo_cache:
self.emo_cache[i] = {}
self.emo_cache[i]["emotions"] = emotions
self.emo_cache[i]["features"] = features
print("Video cache loaded")
def _inflate_by_video_size(self):
assert isinstance( self.sequence_length, int), "'sequence_length' must be an integer when inflating by video size"
inflated_video_indices = []
video_sample_indices = []
for i in range(len(self.video_indices)):
# for i in self.video_indices:
idx = self.video_indices[i]
num_frames = self._get_num_frames(i)
if self.temporal_split_start is not None and self.temporal_split_end is not None:
num_frames = int((self.temporal_split_end - self.temporal_split_start) * num_frames)
num_samples_in_video = num_frames // self.sequence_length
if num_frames % self.sequence_length != 0:
num_samples_in_video += 1
num_samples_in_video = max(1, num_samples_in_video)
inflated_video_indices += [idx] * num_samples_in_video
video_sample_indices += list(range(num_samples_in_video))
self.video_indices = np.array(inflated_video_indices, dtype=np.int32)
self.video_sample_indices = np.array(video_sample_indices, dtype=np.int32)
def __getitem__(self, index):
# max_attempts = 10
max_attempts = 50
for i in range(max_attempts):
try:
return self._getitem(index)
except AssertionError as e:
if not hasattr(self, "num_total_failed_attempts"):
self.num_total_failed_attempts = 0
old_index = index
index = np.random.randint(0, self.__len__())
tb = traceback.format_exc()
if self.num_total_failed_attempts % 50 == 0:
print(f"[ERROR] AssertionError in {self.__class__.__name__} dataset while retrieving sample {old_index}, retrying with new index {index}")
print(f"In total, there has been {self.num_total_failed_attempts} failed attempts. This number should be very small. If it's not, check the data.")
print("See the exception message for more details.")
print(tb)
self.num_total_failed_attempts += 1
print("[ERROR] Failed to retrieve sample after {} attempts".format(max_attempts))
raise RuntimeError("Failed to retrieve sample after {} attempts".format(max_attempts))
def _getitem(self, index):
time = False
if time:
start_time = timeit.default_timer()
if self.hack_length:
index = index % self._true_len()
# 1) VIDEO
# load the video
sample, start_frame, num_read_frames, video_fps, num_frames, num_available_frames = self._get_video(index)
if time:
video_read_time = timeit.default_timer() - start_time
# 2) AUDIO
if self.read_audio:
sample = self._get_audio(index, start_frame, num_read_frames, video_fps, num_frames, sample)
if time:
audio_read_time = timeit.default_timer() - start_time - video_read_time
# 3) LANDMARKS
sample = self._get_landmarks(index, start_frame, num_read_frames, video_fps, num_frames, sample)
if time:
lmk_read_time = timeit.default_timer() - start_time - video_read_time - audio_read_time
# 4) SEGMENTATIONS
if self.read_video:
sample = self._get_segmentations(index, start_frame, num_read_frames, video_fps, num_frames, sample)
if time:
seg_read_time = timeit.default_timer() - start_time - video_read_time - audio_read_time - lmk_read_time
# 5) FACE ALIGNMENT IF ANY
if self.read_video:
sample = self._align_faces(index, sample)
if time:
face_align_time = timeit.default_timer() - start_time - video_read_time - audio_read_time - lmk_read_time - seg_read_time
# 6) GEOMETRY
if self.reconstruction_type is not None:
sample = self._get_reconstructions(index, start_frame, num_read_frames, video_fps, num_frames, sample)
if time:
geom_read_time = timeit.default_timer() - start_time - video_read_time - audio_read_time - lmk_read_time - seg_read_time - face_align_time
# 7) EMOTION
if self.emotion_type is not None:
sample = self._get_emotions(index, start_frame, num_read_frames, video_fps, num_frames, sample)
if time:
emo_read_time = timeit.default_timer() - start_time - video_read_time - audio_read_time - lmk_read_time - seg_read_time - face_align_time - geom_read_time
# 8) AUGMENTATION
if self.read_video:
sample = self._augment_sequence_sample(index, sample)
if time:
aug_time = timeit.default_timer() - start_time - video_read_time - audio_read_time - lmk_read_time - seg_read_time - face_align_time - geom_read_time - emo_read_time
# TO TORCH
sample = to_torch(sample)
# AUDIO NORMALIZATION (if any), this is a remnant from av-hubert and is not being used anywhere, will be removed in the future
if self.read_audio:
if self.include_processed_audio:
if self.audio_normalization is not None:
if self.audio_normalization == "layer_norm":
sample["audio"] = F.layer_norm(sample["audio"], sample["audio"].shape[1:])
else:
raise ValueError(f"Unsupported audio normalization {self.audio_normalization}")
# audio_process_time = timeit.default_timer() - start_time - video_read_time - audio_read_time - lmk_read_time - seg_read_time - face_align_time - geom_read_time - emo_read_time - aug_time
if self.read_video:
# T,H,W,C to T,C,H,W
sample["video"] = sample["video"].permute(0, 3, 1, 2)
if "video_masked" in sample.keys():
sample["video_masked"] = sample["video_masked"].permute(0, 3, 1, 2)
# sample["segmenation"] = sample["segmenation"].permute(0, 2, 1)
# sample["segmentation_masked"] = sample["segmentation_masked"].permute(0, 2, 1)
if self.return_mica_image:
fan_landmarks = None
landmarks_validity = None
if "landmarks" in sample.keys():
if isinstance(sample["landmarks"], dict):
if "fan3d" in sample["landmarks"].keys():
fan_landmarks = sample["landmarks"]["fan3d"]
landmarks_validity = sample["landmarks_validity"]["fan3d"]
elif "fan" in sample["landmarks"].keys():
fan_landmarks = sample["landmarks"]["fan"]
landmarks_validity = sample["landmarks_validity"]["fan"]
elif isinstance(sample["landmarks"], (np.ndarray, torch.Tensor)):
if sample["landmarks"].shape[1] == 68:
fan_landmarks = sample["landmarks"]
landmarks_validity = sample["landmarks_validity"]
sample["mica_video"] = self.mica_preprocessor(sample["video"], fan_landmarks, landmarks_validity=landmarks_validity)
sample["mica_video_masked"] = self.mica_preprocessor(sample["video_masked"], fan_landmarks, landmarks_validity=landmarks_validity)
# # normalize landmarks
# if self.landmark_normalizer is not None:
# if isinstance(self.landmark_normalizer, KeypointScale):
# raise NotImplementedError("Landmark normalization is deprecated")
# self.landmark_normalizer.set_scale(
# img.shape[0] / input_img_shape[0],
# img.shape[1] / input_img_shape[1])
# elif isinstance(self.landmark_normalizer, KeypointNormalization):
# self.landmark_normalizer.set_scale(sample["video"].shape[2], sample["video"].shape[3])
# else:
# raise ValueError(f"Unsupported landmark normalizer type: {type(self.landmark_normalizer)}")
# for key in sample["landmarks"].keys():
# sample["landmarks"][key] = self.landmark_normalizer(sample["landmarks"][key])
if time:
print(f"Video read time: {video_read_time:.2f} s")
print(f"Audio read time: {audio_read_time:.2f} s")
print(f"Landmark read time: {lmk_read_time:.2f} s")
print(f"Segmentation read time: {seg_read_time:.2f} s")
print(f"Face alignment time: {face_align_time:.2f} s")
print(f"Geometry read time: {geom_read_time:.2f} s")
print(f"Emotion read time: {emo_read_time:.2f} s")
print(f"Augmentation time: {aug_time:.2f} s")
# print(f"Audio process time: {audio_process_time:.2f} s")
print(f"Total read time: {timeit.default_timer() - start_time:.2f} s")
return sample
def _get_video_path(self, index):
if self.use_original_video:
video_path = self.root_path / self.video_list[self.video_indices[index]]
else:
video_path = Path(self.output_dir) / "videos_aligned" / self.video_list[self.video_indices[index]]
return video_path
def _get_audio_path(self, index):
audio_path = (Path(self.output_dir) / "audio" / self.video_list[self.video_indices[index]]).with_suffix(".wav")
return audio_path
def _get_num_frames(self, index):
video_meta = self.video_metas[self.video_indices[index]]
# print("Video path: ", video_path)
# num video frames
num_frames = video_meta["num_frames"]
video_path = self._get_video_path(index)
if num_frames == 0:
# use ffprobe to get the number of frames
num_frames = int(subprocess.check_output(["ffprobe", "-v", "error", "-select_streams", "v:0", "-count_packets", "-show_entries", "stream=nb_read_packets", "-of", "csv=p=0", str(video_path)]))
if num_frames == 0:
_vr = FFmpegReader(str(video_path))
num_frames = _vr.getShape()[0]
del _vr
return num_frames
def _get_sample_length(self, index):
if isinstance(self.sequence_length, int): # if sequence length set, use it
return self.sequence_length
elif isinstance(self.sequence_length, str): # otherwise use the one from the metadata
if self.sequence_length == "all":
if self.temporal_split_start is not None and self.temporal_split_end is not None:
num_frames = self._get_num_frames(index)
temporal_split_start_frame = int(self.temporal_split_start * num_frames)
temporal_split_end_frame = int(self.temporal_split_end * num_frames)
return temporal_split_end_frame - temporal_split_start_frame
else:
num_frames = self._get_num_frames(index)
else:
raise ValueError(f"Unsupported sequence length value: '{self.sequence_length}'")
return num_frames
raise
def _get_video(self, index):
video_path = self._get_video_path(index)
video_meta = self.video_metas[self.video_indices[index]]
# print("Video path: ", video_path)
# num video frames
num_frames = self._get_num_frames(index)
assert num_frames > 0, "Number of frames is 0 for video {}".format(video_path)
video_fps = video_meta["fps"]
n1, n2 = video_fps.split("/")
n1 = int(n1)
n2 = int(n2)
assert n1 % n2 == 0
video_fps = n1 // n2
# assert num_frames >= self.sequence_length, f"Video {video_path} has only {num_frames} frames, but sequence length is {self.sequence_length}"
# TODO: handle the case when sequence length is longer than the video length
sequence_length = self._get_sample_length(index)
# pick the starting video frame
if self.temporal_split_start is not None and self.temporal_split_end is not None:
temporal_split_start = int(self.temporal_split_start * num_frames)
temporal_split_end = int(self.temporal_split_end * num_frames)
num_available_frames = temporal_split_end - temporal_split_start
# start_frame = np.random.randint(temporal_split_start, temporal_split_end - sequence_length)
else:
temporal_split_start = 0
temporal_split_end = num_frames
num_available_frames = num_frames
if num_available_frames <= sequence_length:
start_frame = temporal_split_start
else:
if self.video_sample_indices is None: # one video is one sample
start_frame = np.random.randint(temporal_split_start, temporal_split_end - sequence_length)
else: # one video is multiple samples (as many as the sequence length allows without repetition)
start_frame = temporal_split_start + (self.video_sample_indices[index] * sequence_length)
# start_frame = np.random.randint(0, num_frames - sequence_length)
sample = {}
if self.include_filename:
sample["filename"] = str(video_path)
sample["fps"] = video_fps # include the fps in the sample
# TODO: picking the starting frame should probably be done a bit more robustly
# (e.g. by ensuring the sequence has at least some valid landmarks) ...
# maybe the video should be skipped altogether if it can't provide that
# load the frames
# frames = []
# for i in range(start_frame, start_frame + sequence_length):
# frame_path = video_path / f"frame_{i:04d}.jpg"
# frame = imread(str(frame_path))
# frames.append(frame)
assert video_path.is_file(), f"Video {video_path} does not exist"
num_read_frames = self._get_sample_length(index)
num_read_frames_ = self._get_sample_length(index)
if self.read_video:
num_read_frames = 0
try:
if not self.preload_videos:
# import timeit
# start_time = timeit.default_timer()
# frames = vread(video_path.as_posix())
# end_time = timeit.default_timer()
# print(f"Video read time: {end_time - start_time:.2f} s")
# from decord import VideoReader
# from decord import cpu, gpu
# start_time = timeit.default_timer()
vr = VideoReader(video_path.as_posix(), ctx=cpu(0), width=self.image_size, height=self.image_size)
if len(vr) < sequence_length:
sequence_length_ = len(vr)
else:
sequence_length_ = sequence_length
frames = vr.get_batch(range(start_frame,(start_frame + sequence_length_)))
frames = frames.asnumpy()
if sequence_length_ < sequence_length:
# pad with zeros if video shorter than sequence length
frames = np.concatenate([frames, np.zeros((sequence_length - frames.shape[0], frames.shape[1], frames.shape[2], frames.shape[3]), dtype=frames.dtype)])
# end_time = timeit.default_timer()
# print(f"Video read time: {end_time - start_time:.2f} s")
else:
frames = self.video_cache[video_path.as_posix()]
assert len(frames) == num_frames, f"Video {video_path} has {len(frames)} frames, but meta says it has {num_frames}"
frames = frames[start_frame:(start_frame + sequence_length)]
num_read_frames = frames.shape[0]
# # plot frames
# import matplotlib.pyplot as plt
# frame_idx = 0
# plt.figure()
# plt.imshow(frames[frame_idx])
# plt.show()
if frames.shape[0] < sequence_length:
# pad with zeros if video shorter than sequence length
frames = np.concatenate([frames, np.zeros((sequence_length - frames.shape[0], frames.shape[1], frames.shape[2]), dtype=frames.dtype)])
except ValueError:
# reader = vreader(video_path.as_posix())
# create an opencv video reader
reader = cv2.VideoCapture(video_path.as_posix())
fi = 0
frames = []
while fi < start_frame:
fi += 1
# _ = next(reader)
_, frame = reader.read()
for i in range(sequence_length):
# frames.append(next(reader))
if reader.isOpened():
_, frame = reader.read()
if frame is None:
# frame = np.zeros((self.image_size, self.image_size, 3), dtype=np.uint8)
frame = np.zeros_like(frames[0])
frames.append(frame)
continue
num_read_frames += 1
# bgr to rgb
frame = frame[:, :, ::-1]
else:
# if we ran out of frames, pad with black
frame = np.zeros_like(frames[0])
frames.append(frame)
reader.release()
frames = np.stack(frames, axis=0)
frames = frames.astype(np.float32) / 255.0
# sample = {
sample["video"] = frames
sample["frame_indices"] = np.arange(start_frame, start_frame + sequence_length, dtype=np.int32)
if num_read_frames_ != num_read_frames:
print(f"[Warning]: read {num_read_frames} frames instead of {num_read_frames_} for video {video_path}")
return sample, start_frame, num_read_frames, video_fps, num_frames, num_available_frames
def _read_audio(self, index):
# audio_path = (Path(self.output_dir) / "audio" / self.video_list[self.video_indices[index]]).with_suffix(".wav")
audio_path = self._get_audio_path(index)
# audio_meta = self.audio_metas[self.video_indices[index]]
# load the audio
# if self.include_raw_audio:
sampling_rate = 16000
wavdata, sampling_rate = librosa.load(audio_path, sr=sampling_rate)
# wavdata, sampling_rate = librosa.load(audio_path, sr=sampling_rate)
if wavdata.ndim > 1:
wavdata = librosa.to_mono(wavdata)
wavdata = (wavdata.astype(np.float64) * 32768.0).astype(np.int16)
return wavdata, sampling_rate
def _get_audio(self, index, start_frame, num_read_frames, video_fps, num_frames, sample):
if self.preload_videos:
wavdata, sampling_rate = self.audio_cache[index]
else:
wavdata, sampling_rate = self._read_audio(index)
sequence_length = self._get_sample_length(index)
# audio augmentation
if np.random.rand() < self.audio_noise_prob:
wavdata = self.add_noise(wavdata)
if self.include_processed_audio:
# sampling_rate, wavdata = wavfile.read(audio_path.as_posix())
# assert samplerate == 16000 and len(wavdata.shape) == 1
audio_feats = logfbank(wavdata, samplerate=sampling_rate).astype(np.float32) # [T (num audio frames), F (num filters)]
# the audio feats frequency (and therefore num frames) is too high, so we stack them together to match num visual frames
audio_feats = stacker(audio_feats, self.stack_order_audio)
# audio_feats = audio_feats[start_frame:(start_frame + sequence_length)]
audio_feats = audio_feats[start_frame:(start_frame + num_read_frames)]
# temporal pad with zeros if necessary to match the desired video length
if audio_feats.shape[0] < sequence_length:
# concatente with zeros
audio_feats = np.concatenate([audio_feats,
np.zeros((sequence_length - audio_feats.shape[0], audio_feats.shape[1]),
dtype=audio_feats.dtype)], axis=0)
# stack the frames and audio feats together
sample["audio"] = audio_feats
if self.include_raw_audio:
assert sampling_rate % video_fps == 0
wav_per_frame = sampling_rate // video_fps
wavdata_ = np.zeros((num_frames, wav_per_frame), dtype=wavdata.dtype)
wavdata_ = wavdata_.reshape(-1)
if wavdata.size > wavdata_.size:
wavdata_[...] = wavdata[:wavdata_.size]
else:
wavdata_[:wavdata.size] = wavdata
wavdata_ = wavdata_.reshape((num_frames, wav_per_frame))
wavdata_ = wavdata_[start_frame:(start_frame + num_read_frames)]
if wavdata_.shape[0] < sequence_length:
# concatente with zeros
wavdata_ = np.concatenate([wavdata_,
np.zeros((sequence_length - wavdata_.shape[0], wavdata_.shape[1]),
dtype=wavdata_.dtype)], axis=0)
wavdata_ = wavdata_.astype(np.float64) / np.int16(np.iinfo(np.int16).max)
# wavdata_ = np.zeros((sequence_length, samplerate // video_fps), dtype=wavdata.dtype)
# wavdata_ = np.zeros((n * frames.shape[0]), dtype=wavdata.dtype)
# wavdata_[:wavdata.shape[0]] = wavdata
# wavdata_ = wavdata_.reshape((frames.shape[0], -1))
sample["raw_audio"] = wavdata_
sample["samplerate"] = sampling_rate
return sample
def _path_to_landmarks(self, index, landmark_type, landmark_source):
return (Path(self.output_dir) / f"landmarks_{landmark_source}" / landmark_type / self.video_list[self.video_indices[index]]).with_suffix("")
def _read_landmarks(self, index, landmark_type, landmark_source):
landmarks_dir = self._path_to_landmarks(index, landmark_type, landmark_source)
landmark_list = FaceDataModuleBase.load_landmark_list(landmarks_dir / f"landmarks_{landmark_source}.pkl")
return landmark_list
def _get_landmarks(self, index, start_frame, num_read_frames, video_fps, num_frames, sample):
sequence_length = self._get_sample_length(index)
landmark_dict = {}
landmark_validity_dict = {}
for lti, landmark_type in enumerate(self.landmark_types):
landmark_source = self.landmark_source[lti]
landmarks_dir = self._path_to_landmarks(index, landmark_type, landmark_source)
landmarks = []
if (landmarks_dir / "landmarks.pkl").exists(): # landmarks are saved per video in a single file
# landmark_list = FaceDataModuleBase.load_landmark_list(landmarks_dir / "landmarks.pkl")
# landmark_list = FaceDataModuleBase.load_landmark_list(landmarks_dir / "landmarks_original.pkl")
if not self.preload_videos:
# landmark_list = FaceDataModuleBase.load_landmark_list(landm?arks_dir / f"landmarks_{landmark_source}.pkl")
landmark_list = self._read_landmarks(index, landmark_type, landmark_source)
# landmark_types = FaceDataModuleBase.load_landmark_list(landmarks_dir / "landmark_types.pkl")
else:
landmark_list = self.lmk_cache[index][landmark_type][landmark_source]
# landmark_types = self.lmk_cache[index]["landmark_types"]
landmarks = landmark_list[start_frame: sequence_length + start_frame]
landmark_validity = np.ones((len(landmarks), 1), dtype=np.float32)
for li in range(len(landmarks)):
if len(landmarks[li]) == 0: # dropped detection
if landmark_type == "mediapipe":
# [WARNING] mediapipe landmarks coordinates are saved in the scale [0.0-1.0] (for absolute they need to be multiplied by img size)
landmarks[li] = np.zeros((MEDIAPIPE_LANDMARK_NUMBER, 3))
elif landmark_type in ["fan", "kpt68"]:
landmarks[li] = np.zeros((68, 2))
else:
raise ValueError(f"Unknown landmark type '{landmark_type}'")
landmark_validity[li] = 0.
elif len(landmarks[li]) > 1: # multiple faces detected
landmarks[li] = landmarks[li][0] # just take the first one for now
else: \
landmarks[li] = landmarks[li][0]
# # pad landmarks with zeros if necessary to match the desired video length
# # if landmarks.shape[0] < sequence_length:
# if len(landmarks) < sequence_length:
# # concatente with zeros
# landmarks += [np.zeros((landmarks.shape[1]))] * (sequence_length - len(landmarks))
# landmarks = np.concatenate([landmarks, np.zeros((sequence_length - landmarks.shape[0], landmarks.shape[1]))], axis=0)
# landmark_validity = np.concatenate([landmark_validity, np.zeros((sequence_length - landmark_validity.shape[0]), dtype=np.bool)], axis=0)
else: # landmarks are saved per frame
landmark_validity = np.ones((len(landmarks), 1), dtype=np.float32)
for i in range(start_frame, sequence_length + start_frame):
landmark_path = landmarks_dir / f"{i:05d}_000.pkl"
landmark_type, landmark = load_landmark(landmark_path)
landmarks += [landmark]
if len(landmark) == 0: # dropped detection
landmark = [0, 0]
landmark_validity[li] = 0.
elif len(landmark) > 1: # multiple faces detected
landmarks[li] = landmarks[li][0] # just take the first one for now
else:
landmark[li] = landmarks[li][0]
landmarks = np.stack(landmarks, axis=0)
# if landmark_type == "mediapipe" and self.align_images:
# # # [WARNING] mediapipe landmarks coordinates are saved in the scale [0.0-1.0] (for absolute they need to be multiplied by img size)
# # # landmarks -= 0.5
# # landmarks -= 1.
# landmarks *= 2
# # # landmarks *= 2
# landmarks -= 1
# pad landmarks with zeros if necessary to match the desired video length
if landmarks.shape[0] < sequence_length:
landmarks = np.concatenate([landmarks, np.zeros(
(sequence_length - landmarks.shape[0], *landmarks.shape[1:]),
dtype=landmarks.dtype)], axis=0)
landmark_validity = np.concatenate([landmark_validity, np.zeros((sequence_length - landmark_validity.shape[0], 1),
dtype=landmark_validity.dtype)], axis=0)
landmark_dict[landmark_type] = landmarks.astype(np.float32)
landmark_validity_dict[landmark_type] = landmark_validity
sample["landmarks"] = landmark_dict
sample["landmarks_validity"] = landmark_validity_dict
return sample
def _path_to_segmentations(self, index):
return (Path(self.output_dir) / f"segmentations_{self.segmentation_source}" / self.segmentation_type / self.video_list[self.video_indices[index]]).with_suffix("")
def _read_segmentations(self, index, start_frame=None, end_frame=None):
segmentations_dir = self._path_to_segmentations(index)
if (segmentations_dir / "segmentations.hdf5").exists(): # if random access hdf5 exists (newest), let's use it
segmentations, seg_types, seg_names = load_segmentation_list_v2(segmentations_dir / "segmentations.hdf5", start_frame, end_frame)
elif (segmentations_dir / "segmentations.pkl").exists(): # segmentations are saved in a single pickle (no random access)
segmentations, seg_types, seg_names = load_segmentation_list(segmentations_dir / "segmentations.pkl")
if start_frame is not None and end_frame is not None:
segmentations = segmentations[start_frame: end_frame]
seg_types = seg_types[start_frame: end_frame]
seg_names = seg_names[start_frame: end_frame]
if isinstance(segmentations, list):
segmentations = np.stack(segmentations, axis=0)
if segmentations.ndim == 4: # T, C=1, W, H
segmentations = segmentations[:,0,...]
if isinstance(seg_types[0], bytes):
seg_types = [seg_type.decode("utf-8") for seg_type in seg_types]
if isinstance(seg_names[0], bytes):
seg_names = [seg_name.decode("utf-8") for seg_name in seg_names]
return segmentations, seg_types, seg_names
def _retrieve_segmentations(self, index, start_frame, end_frame):
if not self.preload_videos:
# segmentations_dir = self._path_to_segmentations(index)
# if (segmentations_dir / "segmentations.hdf5").exists(): # random access hdf5 exists, let's use it
# segmentations, seg_types, seg_names = load_segmentation_list_v2(segmentations_dir / "segmentations.hdf5", start_frame, end_frame)
# elif (segmentations_dir / "segmentations.pkl").exists(): # segmentations are saved in a single pickle
# seg_images, seg_types, seg_names = load_segmentation_list(segmentations_dir / "segmentations.pkl")
# segmentations = seg_images[start_frame: end_frame]
# if isinstance(seg_images, list):
# segmentations = np.stack(seg_images, axis=0)
# if seg_images.ndim == 4: # T, C=1, W, H
# segmentations = segmentations[:,0,...]
segmentations, seg_types, seg_names = self._read_segmentations(index, start_frame, end_frame)
return segmentations, seg_types, seg_names
else:
video_path = str(self._get_video_path(index))
segmentations, seg_types, seg_names = self.seg_cache[video_path]
segmentations = segmentations[start_frame: end_frame]
seg_types = seg_types[start_frame: end_frame]
seg_names = seg_names[start_frame: end_frame]
return segmentations, seg_types, seg_names
def _load_reconstructions(self, index, rec_type, appearance=False, start_frame=None, end_frame=None):
reconstructions_dir = self._path_to_reconstructions(index, rec_type)
if (reconstructions_dir / "shape_pose_cam.hdf5").exists(): # random access hdf5 exists, let's use it
shape_pose_cam = load_reconstruction_list_v2(reconstructions_dir / "shape_pose_cam.hdf5",
start_frame=start_frame, end_frame=end_frame)
if appearance:
appearance = load_reconstruction_list_v2(reconstructions_dir / "appearance.hdf5",
start_frame=start_frame, end_frame=end_frame)
else:
appearance = None
elif (reconstructions_dir / "shape_pose_cam.pkl").exists(): # reconstructions are saved in a single pickle
shape_pose_cam = load_reconstruction_list(reconstructions_dir / "shape_pose_cam.pkl",
start_frame=start_frame, end_frame=end_frame)
if appearance:
appearance = load_reconstruction_list(reconstructions_dir / "appearance.pkl",
start_frame=start_frame, end_frame=end_frame)
else:
appearance = None
## should no longer be necessary as the start/end frame is now handled in the load_reconstruction_list function
# if start_frame is not None and end_frame is not None:
# shape_pose_cam = {key: shape_pose_cam[key][:, start_frame: end_frame] for key in shape_pose_cam.keys()}
# if appearance is not None:
# appearance = {key: appearance[key][:, start_frame: end_frame] for key in appearance.keys()}
else:
raise RuntimeError(f"Reconstruction file not found in {reconstructions_dir}")
# for key in shape_pose_cam.keys():
# shape_pose_cam[key] = np.copy(shape_pose_cam[key])
# for key in appearance.keys():
# appearance[key] = np.copy(appearance[key])
return shape_pose_cam, appearance
def _load_emotions(self, index, features=False, start_frame=None, end_frame=None):
emotions_dir = self._path_to_emotions(index)
if (emotions_dir / "emotions.hdf5").exists(): # random access hdf5 exists, let's use it | emotions = load_emotion_list_v2(emotions_dir / "emotions.hdf5", start_frame, end_frame) | 13 | 2023-11-07 20:13:32+00:00 | 16k |
hxz393/ConfigCenterComparer | ui/action_compare.py | [
{
"identifier": "get_resource_path",
"path": "lib/get_resource_path.py",
"snippet": "def get_resource_path(relative_path: Union[str, os.PathLike]) -> Optional[str]:\n \"\"\"\n 获取资源的绝对路径。这个函数适用于 PyInstaller 打包后的可执行文件。\n\n :type relative_path: Union[str, os.PathLike]\n :param relative_path: 相对... | import logging
from typing import Dict, Optional, List
from PyQt5.QtCore import QObject, pyqtSignal
from PyQt5.QtGui import QIcon
from PyQt5.QtWidgets import QAction
from lib.get_resource_path import get_resource_path
from lib.log_time import log_time
from ui.config_manager import ConfigManager
from ui.dialog_comparison import DialogComparison
from ui.global_signals import global_signals
from ui.lang_manager import LangManager
from ui.message_show import message_show
from ui.table_main import TableMain | 12,167 | """
本文件包含用于处理和比较配置数据的类和函数。
该模块主要包含 `ActionCompare` 类，用于在用户界面中处理数据比较的逻辑。该类提供了对比配置数据、更新界面语言、重组数据等功能，方便用户进行环境配置的对比分析。
:author: assassing
:contact: https://github.com/hxz393
:copyright: Copyright 2023, hxz393. 保留所有权利。
"""
logger = logging.getLogger(__name__)
class ActionCompare(QObject):
"""
提供数据比较功能的类。
该类负责处理用户界面中的数据对比逻辑，包括初始化UI组件、更新语言设置、执行数据对比等功能。它还负责处理各种事件和信号，并更新用户界面状态。
:param lang_manager: 语言管理器实例，用于处理界面语言更新。
:type lang_manager: LangManager
:param config_manager: 配置管理器，用于获取网络测试相关配置。
:type config_manager: ConfigManager
:param table: 主界面表格实例，提供数据获取和显示功能。
:type table: TableMain
"""
status_updated = pyqtSignal(str)
def __init__(self,
lang_manager: LangManager,
config_manager: ConfigManager,
table: TableMain):
super().__init__()
self.lang_manager = lang_manager
self.lang_manager.lang_updated.connect(self.update_lang)
self.config_manager = config_manager
self.table = table
self.initUI()
def initUI(self) -> None:
"""
初始化用户界面组件。
:rtype: None
:return: 无返回值。
"""
self.action_compare = QAction(QIcon(get_resource_path('media/icons8-diff-files-26')), 'Compare')
self.action_compare.setShortcut('F8')
# 为了记录运行时间，使用匿名函数
self.action_compare.triggered.connect(lambda checked=False: self.compare())
self.update_lang()
def update_lang(self) -> None:
"""
更新界面语言设置。
:rtype: None
:return: 无返回值。
"""
self.lang = self.lang_manager.get_lang()
self.action_compare.setText(self.lang['ui.action_compare_1'])
self.action_compare.setStatusTip(self.lang['ui.action_compare_2'])
| """
本文件包含用于处理和比较配置数据的类和函数。
该模块主要包含 `ActionCompare` 类，用于在用户界面中处理数据比较的逻辑。该类提供了对比配置数据、更新界面语言、重组数据等功能，方便用户进行环境配置的对比分析。
:author: assassing
:contact: https://github.com/hxz393
:copyright: Copyright 2023, hxz393. 保留所有权利。
"""
logger = logging.getLogger(__name__)
class ActionCompare(QObject):
"""
提供数据比较功能的类。
该类负责处理用户界面中的数据对比逻辑，包括初始化UI组件、更新语言设置、执行数据对比等功能。它还负责处理各种事件和信号，并更新用户界面状态。
:param lang_manager: 语言管理器实例，用于处理界面语言更新。
:type lang_manager: LangManager
:param config_manager: 配置管理器，用于获取网络测试相关配置。
:type config_manager: ConfigManager
:param table: 主界面表格实例，提供数据获取和显示功能。
:type table: TableMain
"""
status_updated = pyqtSignal(str)
def __init__(self,
lang_manager: LangManager,
config_manager: ConfigManager,
table: TableMain):
super().__init__()
self.lang_manager = lang_manager
self.lang_manager.lang_updated.connect(self.update_lang)
self.config_manager = config_manager
self.table = table
self.initUI()
def initUI(self) -> None:
"""
初始化用户界面组件。
:rtype: None
:return: 无返回值。
"""
self.action_compare = QAction(QIcon(get_resource_path('media/icons8-diff-files-26')), 'Compare')
self.action_compare.setShortcut('F8')
# 为了记录运行时间，使用匿名函数
self.action_compare.triggered.connect(lambda checked=False: self.compare())
self.update_lang()
def update_lang(self) -> None:
"""
更新界面语言设置。
:rtype: None
:return: 无返回值。
"""
self.lang = self.lang_manager.get_lang()
self.action_compare.setText(self.lang['ui.action_compare_1'])
self.action_compare.setStatusTip(self.lang['ui.action_compare_2'])
| @log_time | 1 | 2023-11-07 01:02:38+00:00 | 16k |
pytorch-labs/ao | test/test.py | [
{
"identifier": "DynamicallyPerAxisQuantizedLinear",
"path": "torchao/quantization/dynamic_quant.py",
"snippet": "class DynamicallyPerAxisQuantizedLinear(torch.nn.Linear):\n \"\"\"\n This class is a replacement for `torch.nn.Linear`. It implements a\n quantized matmul using int8 dynamic symmetr... | import copy
import unittest
import torch
import torch.nn as nn
import os
from torch._inductor.utils import run_and_get_code
from torch._dynamo import config
from torch.ao.quantization import MinMaxObserver, QConfigMapping
from torchao.quantization.dynamic_quant import (
DynamicallyPerAxisQuantizedLinear,
)
from torchao.quantization.quant_api import (
apply_dynamic_quant,
apply_weight_only_int8_quant,
change_linear_weights_to_int8_dqtensors,
change_linear_weights_to_int8_woqtensors,
change_linear_weights_to_int4_woqtensors,
_replace_with_custom_fn_if_matches_filter,
)
from torchao.quantization.quant_primitives import (
dequantize_per_channel,
dequantize_per_tensor,
dynamically_quantize_per_channel,
dynamically_quantize_per_tensor,
quant_int8_dynamic_linear,
quant_int8_dynamic_per_token_linear,
quantize_activation_per_token_absmax,
safe_int_mm,
)
from torchao.quantization.smoothquant import (
get_scale,
smooth_fq_linear_to_inference,
SmoothFakeDynamicallyQuantizedLinear,
swap_linear_with_smooth_fq_linear,
)
from torchao.quantization.subclass import (
Int8DynamicallyQuantizedLinearWeight,
Int8WeightOnlyQuantizedLinearWeight,
Int4WeightOnlyQuantizedLinearWeight
)
from torchao.quantization.utils import (
_apply_logging_hook,
compute_error,
compute_error as SQNR,
_fqn_to_op_to_shape_to_count,
LoggingTensorMode,
)
from torch.ao.quantization.quantize_fx import convert_to_reference_fx, prepare_fx
from transformers import ( # type: ignore[import-untyped]
DistilBertModel,
DistilBertTokenizer,
) | 11,462 |
x = torch.randn(32, 32, dtype=dtype, device=device)
y_calib_eager_t = lin_eager_t(x)
y_calib_opt_t = lin_opt_t(x)
y_calib_opt = lin_opt(x)
torch.testing.assert_close(y_calib_eager_t, y_calib_opt_t)
torch.testing.assert_close(y_calib_eager_t, y_calib_opt)
lin_eager_t.to_inference()
lin_opt_t.to_inference()
lin_opt.to_inference()
torch.testing.assert_close(lin_eager_t.W_int_repr, lin_opt_t.W_int_repr)
torch.testing.assert_close(lin_eager_t.W_int_repr, lin_opt.W_int_repr)
lin_opt_t = torch.compile(lin_opt_t, mode="max-autotune")
lin_opt = torch.compile(lin_opt, mode="max-autotune")
y_ref = lin_ref(x)
y_eager = lin_eager_t(x)
y_opt_t = lin_opt_t(x)
y_opt = lin_opt(x)
if not torch.any(torch.isinf(y_ref)) and torch.any(torch.isinf(y_eager)):
# eager mode torch._int_mm is sometimes buggy, when this happens
# we can't really compare the compiled version against it properly
print("eager mode torch._int_mm known bad, test is inconclusive")
return
sqnr_ref_eager = compute_error(y_ref, y_eager)
sqnr_eager_opt_t = compute_error(y_eager, y_opt_t)
sqnr_eager_opt = compute_error(y_eager, y_opt)
# since torch.compile for a torch.half model can
# change numerics significantly, we can only test for a high SQNR here
# and not for closeness
self.assertTrue(sqnr_eager_opt_t >= 45.0)
self.assertTrue(sqnr_eager_opt >= 45.0)
# y_opt_t and y_opt should be equivalent
torch.testing.assert_close(y_opt_t, y_opt)
def test_selective_torch_compile(self):
m = nn.Sequential(
nn.Linear(4, 4),
nn.Sequential(
nn.Linear(4, 4),
nn.Linear(4, 4),
),
nn.Linear(4, 4),
)
x = torch.randn(4, 4)
y_ref = m(x)
_replace_with_custom_fn_if_matches_filter(
m,
lambda mod: torch.compile(mod),
lambda mod, fqn: isinstance(mod, nn.Linear) and fqn != "1.0",
)
self.assertTrue(isinstance(m[0], torch._dynamo.eval_frame.OptimizedModule))
self.assertTrue(isinstance(m[1][0], nn.Linear))
self.assertTrue(isinstance(m[1][1], torch._dynamo.eval_frame.OptimizedModule))
self.assertTrue(isinstance(m[2], torch._dynamo.eval_frame.OptimizedModule))
y = m(x)
torch.testing.assert_close(y, y_ref)
def test_debug_x_absmax(self):
m = nn.Sequential(nn.Linear(3, 4))
x0 = torch.randn(4, 5, 3)
y0 = m(x0)
swap_linear_with_smooth_fq_linear(m)
# no calibration, straight to inference, should not crash
smooth_fq_linear_to_inference(m, debug_skip_calibration=True)
y1 = m(x0)
class PythonQuantPrimitivesUnitTest(unittest.TestCase):
def _test_dynamic_quant_per_tensor_numerics_impl(
self, qmin, qmax, int_dtype, qint_dtype, float_dtype, device, qscheme
):
x = torch.randn(256, dtype=float_dtype, device=device)
y_vals, y_scale, y_zero_point = dynamically_quantize_per_tensor(
x, qmin, qmax, int_dtype, qscheme
)
# reference
# quantize_per_tensor_dynamic doesn't work for half, so we cast there and back
x_for_ref = x.half().float() if float_dtype == torch.float16 else x
# quantize_per_tensor_dynamic doesn't support qscheme, so we just do dynamic
# quant manually with observers + static quant
obs = MinMaxObserver(
dtype=qint_dtype, qscheme=qscheme, quant_min=qmin, quant_max=qmax
).to(device)
obs(x_for_ref)
ref_scale, ref_zero_point = obs.calculate_qparams()
y_ref = torch.quantize_per_tensor(
x_for_ref, ref_scale, ref_zero_point, qint_dtype
)
# y_ref = torch.quantize_per_tensor_dynamic(x_for_ref, qint_dtype, False)
# print(y_ref)
if float_dtype == torch.float:
assert torch.equal(y_vals, y_ref.int_repr())
else:
# numerics are not exactly aligned yet, off-by-one probably due
# to rounding
assert torch.max(torch.abs(y_vals - y_ref.int_repr())).item() <= 1
torch.testing.assert_close(
y_scale, torch.tensor([y_ref.q_scale()], device=device, dtype=float_dtype)
)
if y_zero_point is not None:
assert torch.equal(
y_zero_point, torch.tensor([y_ref.q_zero_point()], device=device)
)
else:
self.assertTrue(y_ref.q_zero_point() == 0)
# dequantize and check again
| # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# mypy: ignore-errors
torch.manual_seed(0)
config.cache_size_limit = 100
class SmoothquantUnitTest(unittest.TestCase):
# first, let's reproduce the graphic from the paper, Figure 4, to ensure
# we are calculating the scales correctly
def test_figure_4(self):
X = torch.FloatTensor([1, -16, 2, 6, -2, 8, -1, -9]).reshape(1, 2, 4)
W = torch.FloatTensor([2, 1, -2, 1, -1, -1, 2, -1, -2, -1, -1, 1]).reshape(4, 3)
X_mul_W = torch.matmul(X, W)
smoothquant_scale = get_scale(
torch.amax(torch.abs(X), dim=(0, 1)),
torch.amax(torch.abs(W), dim=1),
alpha=0.5,
)
# reproduce scaled calculation
X_scaled = X / smoothquant_scale.reshape(1, 1, -1)
W_scaled = torch.matmul(torch.diag(smoothquant_scale), W)
X_scaled_mul_scaled_W = torch.matmul(X_scaled, W_scaled)
assert torch.allclose(X_mul_W, X_scaled_mul_scaled_W), "not close!"
assert X_mul_W.shape == X_scaled_mul_scaled_W.shape
# next, run the above test on a sample of representative inputs
def test_tensors(self):
x_shape = (1, 5, 7)
w_shape = (7, 9)
for i in range(3):
X = torch.randn(x_shape) * 10
W = torch.randn(w_shape)
s = get_scale(
torch.amax(torch.abs(X), dim=(0, 1)),
torch.amax(torch.abs(W), dim=1),
alpha=0.5,
)
Y = torch.matmul(X, W)
Y_ref = torch.matmul(
X / s.reshape(1, 1, -1),
torch.matmul(torch.diag(s), W),
)
assert torch.allclose(Y, Y_ref, atol=1e-3, rtol=1e-3), "not close!"
def _test_smooth_linear_impl(self, x_shape, lin_shape, device):
# so we can use the full range
torch.backends.quantized.engine = "qnnpack"
x = torch.randn(*x_shape, device=device) * 9 + 10
lin_fp32 = nn.Linear(*lin_shape, device=device) # misc: ignore
lin_smooth = SmoothFakeDynamicallyQuantizedLinear.from_float(
copy.deepcopy(lin_fp32), alpha=0.25
)
lin_smooth_skip_scaling = SmoothFakeDynamicallyQuantizedLinear.from_float(
copy.deepcopy(lin_fp32), alpha=0.25
)
lin_fp32_copy = copy.deepcopy(lin_fp32) # assignment: ignore
lin_fp32_copy.qconfig = torch.ao.quantization.QConfig( # assignment: ignore
activation=None,
weight=torch.ao.quantization.default_per_channel_weight_observer,
)
lin_dynamic_q = torch.ao.nn.quantized.dynamic.Linear.from_float(
lin_fp32_copy.cpu()
)
y_ref = lin_fp32(x)
# calibrate the smoothquant versions
y_smooth_nocalib = lin_smooth(x)
_ = lin_smooth_skip_scaling(x)
lin_smooth.to_inference()
lin_smooth_skip_scaling.debug_skip_scaling = True
lin_smooth_skip_scaling.to_inference()
# verify that with scaling turned off, numerics match quantized version
y_smooth_fq_only = lin_smooth_skip_scaling(x)
y_smooth_fq = lin_smooth(x)
y_dynamic_q = lin_dynamic_q(x.cpu()).to(device)
# print('y_ref', y_ref)
# print('y_smooth_nocalib', y_smooth_nocalib)
# print('y_smooth_fq', y_smooth_fq)
# print('y_smooth_fq_only', y_smooth_fq_only)
# print('y_dynamic_q', y_dynamic_q)
sqnr_smooth_fq = compute_error(y_ref, y_smooth_fq)
sqnr_dynamic_q = compute_error(y_ref, y_dynamic_q)
sqnr_fq = compute_error(y_smooth_fq_only, y_dynamic_q)
# print('sqnr_smooth', sqnr_smooth_fq, 'sqnr_dynamic', sqnr_dynamic_q, 'sqnr_fq', sqnr_fq)
assert torch.allclose(
y_ref, y_smooth_nocalib
), "y_ref not close to y_smooth_nocalib"
# after https://github.com/pytorch-labs/ao_benchmarks/pull/32,
# numerics do not match exactly between production c++ code
# and this Python code
# assert torch.allclose(
# y_smooth_fq_only, y_dynamic_q,
# atol=torch.max(y_smooth_fq_only).item()*0.01,
# rtol=0.00001), \
# 'y_smooth_fq_only not close to y_dynamic_q'
self.assertTrue(sqnr_smooth_fq.item() >= 40.0)
self.assertTrue(sqnr_dynamic_q.item() >= 40.0)
self.assertTrue(sqnr_fq.item() >= 40.0)
def test_smooth_linear_cpu(self):
self._test_smooth_linear_impl((1, 5, 3), (3, 4), "cpu")
def test_smooth_linear_cuda(self):
if not torch.cuda.is_available():
print("no cuda, skip")
return
self._test_smooth_linear_impl((1, 32, 32), (32, 16), "cuda")
def test_smooth_linear_edge_cases(self):
# so we can use the full range
torch.backends.quantized.engine = "qnnpack"
lin_fp32 = nn.Linear(3, 4)
lin_smooth = SmoothFakeDynamicallyQuantizedLinear.from_float(
lin_fp32, alpha=0.25
)
# test different ranks
x0 = torch.randn(4, 5, 3)
x1 = torch.randn(1, 8, 5, 3)
x2 = torch.randn(2, 3, 7, 5, 3)
# calibrate
_ = lin_smooth(x0)
_ = lin_smooth(x1)
_ = lin_smooth(x2)
# inference
lin_smooth.to_inference()
_ = lin_smooth(x0)
_ = lin_smooth(x1)
_ = lin_smooth(x2)
def test_swap(self):
m = nn.Sequential(
nn.Sequential(nn.Linear(4, 4), nn.ReLU(), nn.Linear(4, 4)),
nn.Linear(4, 4),
)
m_copy = copy.deepcopy(m)
swap_linear_with_smooth_fq_linear(m_copy, skip_fqn_list=["0.2"])
# verify all linears are swapped
assert isinstance(m_copy[0][0], SmoothFakeDynamicallyQuantizedLinear)
assert isinstance(m_copy[0][1], nn.ReLU)
# this one was skipped
assert isinstance(m_copy[0][2], nn.Linear)
assert isinstance(m_copy[1], SmoothFakeDynamicallyQuantizedLinear)
# verify results do not change without smoothing
x = torch.randn(4, 4)
y_ref = m(x)
y = m_copy(x)
assert torch.allclose(y_ref, y)
def test_weight_t_and_non_t_numerics_match(self):
# verify that numerics match whether weight is stored
# in transposed format (for cuBLAS) vs non-transposed format
# (for torch.compile)
if not torch.cuda.is_available():
print("no cuda, skip")
return
dtype = torch.half
device = "cuda"
lin_ref = nn.Linear(32, 16, dtype=dtype, device=device)
lin_eager_t = copy.deepcopy(lin_ref)
lin_opt_t = copy.deepcopy(lin_eager_t)
lin_opt = copy.deepcopy(lin_eager_t)
lin_eager_t = SmoothFakeDynamicallyQuantizedLinear.from_float(lin_eager_t)
lin_opt_t = SmoothFakeDynamicallyQuantizedLinear.from_float(lin_opt_t)
lin_opt = SmoothFakeDynamicallyQuantizedLinear.from_float(lin_opt)
lin_opt.store_w_int_repr_t = False
x = torch.randn(32, 32, dtype=dtype, device=device)
y_calib_eager_t = lin_eager_t(x)
y_calib_opt_t = lin_opt_t(x)
y_calib_opt = lin_opt(x)
torch.testing.assert_close(y_calib_eager_t, y_calib_opt_t)
torch.testing.assert_close(y_calib_eager_t, y_calib_opt)
lin_eager_t.to_inference()
lin_opt_t.to_inference()
lin_opt.to_inference()
torch.testing.assert_close(lin_eager_t.W_int_repr, lin_opt_t.W_int_repr)
torch.testing.assert_close(lin_eager_t.W_int_repr, lin_opt.W_int_repr)
lin_opt_t = torch.compile(lin_opt_t, mode="max-autotune")
lin_opt = torch.compile(lin_opt, mode="max-autotune")
y_ref = lin_ref(x)
y_eager = lin_eager_t(x)
y_opt_t = lin_opt_t(x)
y_opt = lin_opt(x)
if not torch.any(torch.isinf(y_ref)) and torch.any(torch.isinf(y_eager)):
# eager mode torch._int_mm is sometimes buggy, when this happens
# we can't really compare the compiled version against it properly
print("eager mode torch._int_mm known bad, test is inconclusive")
return
sqnr_ref_eager = compute_error(y_ref, y_eager)
sqnr_eager_opt_t = compute_error(y_eager, y_opt_t)
sqnr_eager_opt = compute_error(y_eager, y_opt)
# since torch.compile for a torch.half model can
# change numerics significantly, we can only test for a high SQNR here
# and not for closeness
self.assertTrue(sqnr_eager_opt_t >= 45.0)
self.assertTrue(sqnr_eager_opt >= 45.0)
# y_opt_t and y_opt should be equivalent
torch.testing.assert_close(y_opt_t, y_opt)
def test_selective_torch_compile(self):
m = nn.Sequential(
nn.Linear(4, 4),
nn.Sequential(
nn.Linear(4, 4),
nn.Linear(4, 4),
),
nn.Linear(4, 4),
)
x = torch.randn(4, 4)
y_ref = m(x)
_replace_with_custom_fn_if_matches_filter(
m,
lambda mod: torch.compile(mod),
lambda mod, fqn: isinstance(mod, nn.Linear) and fqn != "1.0",
)
self.assertTrue(isinstance(m[0], torch._dynamo.eval_frame.OptimizedModule))
self.assertTrue(isinstance(m[1][0], nn.Linear))
self.assertTrue(isinstance(m[1][1], torch._dynamo.eval_frame.OptimizedModule))
self.assertTrue(isinstance(m[2], torch._dynamo.eval_frame.OptimizedModule))
y = m(x)
torch.testing.assert_close(y, y_ref)
def test_debug_x_absmax(self):
m = nn.Sequential(nn.Linear(3, 4))
x0 = torch.randn(4, 5, 3)
y0 = m(x0)
swap_linear_with_smooth_fq_linear(m)
# no calibration, straight to inference, should not crash
smooth_fq_linear_to_inference(m, debug_skip_calibration=True)
y1 = m(x0)
class PythonQuantPrimitivesUnitTest(unittest.TestCase):
def _test_dynamic_quant_per_tensor_numerics_impl(
self, qmin, qmax, int_dtype, qint_dtype, float_dtype, device, qscheme
):
x = torch.randn(256, dtype=float_dtype, device=device)
y_vals, y_scale, y_zero_point = dynamically_quantize_per_tensor(
x, qmin, qmax, int_dtype, qscheme
)
# reference
# quantize_per_tensor_dynamic doesn't work for half, so we cast there and back
x_for_ref = x.half().float() if float_dtype == torch.float16 else x
# quantize_per_tensor_dynamic doesn't support qscheme, so we just do dynamic
# quant manually with observers + static quant
obs = MinMaxObserver(
dtype=qint_dtype, qscheme=qscheme, quant_min=qmin, quant_max=qmax
).to(device)
obs(x_for_ref)
ref_scale, ref_zero_point = obs.calculate_qparams()
y_ref = torch.quantize_per_tensor(
x_for_ref, ref_scale, ref_zero_point, qint_dtype
)
# y_ref = torch.quantize_per_tensor_dynamic(x_for_ref, qint_dtype, False)
# print(y_ref)
if float_dtype == torch.float:
assert torch.equal(y_vals, y_ref.int_repr())
else:
# numerics are not exactly aligned yet, off-by-one probably due
# to rounding
assert torch.max(torch.abs(y_vals - y_ref.int_repr())).item() <= 1
torch.testing.assert_close(
y_scale, torch.tensor([y_ref.q_scale()], device=device, dtype=float_dtype)
)
if y_zero_point is not None:
assert torch.equal(
y_zero_point, torch.tensor([y_ref.q_zero_point()], device=device)
)
else:
self.assertTrue(y_ref.q_zero_point() == 0)
# dequantize and check again | x_dq = dequantize_per_tensor(y_vals, y_scale, y_zero_point, float_dtype) | 8 | 2023-11-03 21:27:36+00:00 | 16k |
intellerce/controlanimate | animatediff/pipelines/controlanimation_pipeline.py | [
{
"identifier": "UNet3DConditionModel",
"path": "animatediff/models/unet.py",
"snippet": "class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Opti... | import inspect
import numpy as np
import torch
import PIL
import math
from typing import Callable, List, Optional, Union, Dict, Any
from dataclasses import dataclass
from tqdm import tqdm
from diffusers.utils import is_accelerate_available
from packaging import version
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UniPCMultistepScheduler
)
from diffusers.utils import BaseOutput
from diffusers.utils import (
deprecate,
logging,
# replace_example_docstring,
# scale_lora_layers,
# unscale_lora_layers,
)
from einops import rearrange
from ..models.unet import UNet3DConditionModel
from ..utils.util import preprocess_image
from diffusers.utils.torch_utils import randn_tensor
from modules.controlresiduals_pipeline import MultiControlNetResidualsPipeline
from diffusers.image_processor import VaeImageProcessor
from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.utils.torch_utils import randn_tensor
from diffusers import SchedulerMixin, ConfigMixin
from typing import Any, Dict, List, Optional, Tuple, Union
from diffusers.configuration_utils import register_to_config
from accelerate import cpu_offload | 12,605 |
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(self, prompt, height, width, callback_steps):
if not isinstance(prompt, str) and not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
def prepare_latents(self, input_frames, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator,
latent_timestep, overlaps, strength,
latents=None,
last_output_frames = None,
use_lcm = False,
use_img2img = False,
):
shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # torch.randn(shape, generator=generator, dtype=dtype) #.to(device) , device=rand_device
last_output_frames_latents = []
if overlaps > 0 or strength < 1 or use_lcm:
frames_latents = []
if input_frames is not None:
for i , frame in enumerate(input_frames):
image = frame
image = self.image_processor.preprocess(image)
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
frame_latents = self.vae.encode(image).latent_dist.sample(generator)
frame_latents = self.vae.config.scaling_factor * frame_latents
frames_latents.append(frame_latents)
if last_output_frames is not None:
for image in last_output_frames:
image = self.image_processor.preprocess(image)
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
frame_latents = self.vae.encode(image).latent_dist.sample(generator)
frame_latents = self.vae.config.scaling_factor * frame_latents
last_output_frames_latents.append(frame_latents)
latents = latents.to(device)
if use_lcm:
frames_latents_tensor = torch.stack(frames_latents,dim=2)
latents = self.scheduler.add_noise(frames_latents_tensor, latents.to(device), latent_timestep)
elif last_output_frames is not None and strength < 1.0:
# for i in range(len(last_output_frames_latents)):
for i in range(video_length):
if i < len(last_output_frames):
latents[:, :, i, :, :] = self.scheduler.add_noise(last_output_frames_latents[i], latents[:, :, i, :, :].to(device), latent_timestep)
else:
if not use_img2img:
latents[:, :, i, :, :] = self.scheduler.add_noise(last_output_frames_latents[-1], latents[:, :, i, :, :].to(device), latent_timestep)
else:
latents[:, :, i, :, :] = self.scheduler.add_noise(frames_latents[i], latents[:, :, i, :, :].to(device), latent_timestep)
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
if strength >= 1 and not use_lcm:
latents = latents * self.scheduler.init_noise_sigma
return latents
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start
@torch.no_grad()
def __call__(
self,
video_length: Optional[int],
input_frames: list = None,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
strength: float = 0.5,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_videos_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "tensor",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
overlaps: int = 0,
| # Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# For LCM:
@dataclass
class AnimationPipelineOutput(BaseOutput):
videos: Union[torch.Tensor, np.ndarray]
class ControlAnimationPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
_optional_components = []
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet3DConditionModel,
scheduler: Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
UniPCMultistepScheduler,
None # LCM
],
):
super().__init__()
scheduler = (
scheduler
if scheduler is not None
else LCMScheduler(
beta_start=0.00085, beta_end=0.0120, beta_schedule="scaled_linear", prediction_type="epsilon"
)
)
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
self.control_image_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
)
self.latents = None
self.frames_latents = None
self.prev_frames_latents = None
self.ip_adapter = None
def enable_vae_slicing(self):
self.vae.enable_slicing()
def disable_vae_slicing(self):
self.vae.disable_slicing()
def enable_sequential_cpu_offload(self, gpu_id=0):
if is_accelerate_available():
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
device = torch.device(f"cuda:{gpu_id}")
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
@property
def _execution_device(self):
if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt):
batch_size = len(prompt) if isinstance(prompt, list) else 1
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
text_embeddings = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
text_embeddings = text_embeddings[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
uncond_embeddings = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
uncond_embeddings = uncond_embeddings[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
# if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
# else:
# scale_lora_layers(self.text_encoder, lora_scale)
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
if clip_skip is None:
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
prompt_embeds = prompt_embeds[0]
else:
prompt_embeds = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
)
# Access the `hidden_states` first, that contains a tuple of
# all the hidden states from the encoder layers. Then index into
# the tuple to access the hidden states from the desired layer.
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
# We also need to apply the final LayerNorm here to not mess with the
# representations. The `last_hidden_states` that we typically use for
# obtaining the final prompt representations passes through the LayerNorm
# layer.
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
if self.text_encoder is not None:
prompt_embeds_dtype = self.text_encoder.dtype
elif self.unet is not None:
prompt_embeds_dtype = self.unet.dtype
else:
prompt_embeds_dtype = prompt_embeds.dtype
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
# unscale_lora_layers(self.text_encoder, lora_scale)
return prompt_embeds, negative_prompt_embeds
def get_w_embedding(self, w, embedding_dim=512, dtype=torch.float32):
"""
see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
timesteps: torch.Tensor: generate embedding vectors at these timesteps
embedding_dim: int: dimension of the embeddings to generate
dtype: data type of the generated embeddings
Returns:
embedding vectors with shape `(len(timesteps), embedding_dim)`
"""
assert len(w.shape) == 1
w = w * 1000.0
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
emb = w.to(dtype)[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0, 1))
assert emb.shape == (w.shape[0], embedding_dim)
return emb
def decode_latents(self, latents):
video_length = latents.shape[2]
latents = 1 / self.vae.config.scaling_factor * latents # 0.18215 * latents
latents = rearrange(latents, "b c f h w -> (b f) c h w")
# video = self.vae.decode(latents).sample
video = []
for frame_idx in tqdm(range(latents.shape[0])):
video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample)
video = torch.cat(video)
video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
video = (video / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
video = video.cpu().float().numpy()
return video
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(self, prompt, height, width, callback_steps):
if not isinstance(prompt, str) and not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
def prepare_latents(self, input_frames, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator,
latent_timestep, overlaps, strength,
latents=None,
last_output_frames = None,
use_lcm = False,
use_img2img = False,
):
shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # torch.randn(shape, generator=generator, dtype=dtype) #.to(device) , device=rand_device
last_output_frames_latents = []
if overlaps > 0 or strength < 1 or use_lcm:
frames_latents = []
if input_frames is not None:
for i , frame in enumerate(input_frames):
image = frame
image = self.image_processor.preprocess(image)
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
frame_latents = self.vae.encode(image).latent_dist.sample(generator)
frame_latents = self.vae.config.scaling_factor * frame_latents
frames_latents.append(frame_latents)
if last_output_frames is not None:
for image in last_output_frames:
image = self.image_processor.preprocess(image)
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
frame_latents = self.vae.encode(image).latent_dist.sample(generator)
frame_latents = self.vae.config.scaling_factor * frame_latents
last_output_frames_latents.append(frame_latents)
latents = latents.to(device)
if use_lcm:
frames_latents_tensor = torch.stack(frames_latents,dim=2)
latents = self.scheduler.add_noise(frames_latents_tensor, latents.to(device), latent_timestep)
elif last_output_frames is not None and strength < 1.0:
# for i in range(len(last_output_frames_latents)):
for i in range(video_length):
if i < len(last_output_frames):
latents[:, :, i, :, :] = self.scheduler.add_noise(last_output_frames_latents[i], latents[:, :, i, :, :].to(device), latent_timestep)
else:
if not use_img2img:
latents[:, :, i, :, :] = self.scheduler.add_noise(last_output_frames_latents[-1], latents[:, :, i, :, :].to(device), latent_timestep)
else:
latents[:, :, i, :, :] = self.scheduler.add_noise(frames_latents[i], latents[:, :, i, :, :].to(device), latent_timestep)
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
if strength >= 1 and not use_lcm:
latents = latents * self.scheduler.init_noise_sigma
return latents
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start
@torch.no_grad()
def __call__(
self,
video_length: Optional[int],
input_frames: list = None,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
strength: float = 0.5,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_videos_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "tensor",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
overlaps: int = 0, | multicontrolnetresiduals_pipeline: Optional[MultiControlNetResidualsPipeline] = None, | 2 | 2023-11-04 01:35:44+00:00 | 16k |
Zaczero/openstreetmap-ng | src/controllers/api/0.6/changeset_comment.py | [
{
"identifier": "api_user",
"path": "src/lib/auth.py",
"snippet": "def api_user(*require_scopes: Scope | ExtendedScope) -> User:\n \"\"\"\n Dependency for authenticating the api user.\n \"\"\"\n\n return Security(\n _get_user,\n scopes=tuple(s.value for s in require_scopes),\n ... | from typing import Annotated
from fastapi import APIRouter, Form
from pydantic import PositiveInt
from src.lib.auth import api_user
from src.lib.format.format06 import Format06
from src.limits import CHANGESET_COMMENT_BODY_MAX_LENGTH
from src.models.db.user import User
from src.models.scope import ExtendedScope, Scope
from src.services.changeset_comment_service import ChangesetCommentService | 11,053 |
router = APIRouter()
@router.post('/changeset/{changeset_id}/subscribe')
async def changeset_subscribe(
changeset_id: PositiveInt,
|
router = APIRouter()
@router.post('/changeset/{changeset_id}/subscribe')
async def changeset_subscribe(
changeset_id: PositiveInt, | _: Annotated[User, api_user(Scope.write_api)], | 3 | 2023-11-04 01:12:13+00:00 | 16k |
codefuse-ai/Collinear-Constrained-Attention | train/trainer/atorch_trainer.py | [
{
"identifier": "print_rank_0",
"path": "utils/common_utils.py",
"snippet": "TASK2ID = {}\nID2TASK = {}\n L = args.num_hidden_layers\n V = args.vocab_size\ndef get_rank():\ndef get_local_rank():\ndef is_main_process():\ndef is_local_main_process():\ndef print_rank_0(*message):\ndef get_world_size(... | import datetime
import json
import logging
import math
import os
import random
import re
import shutil
import time
import warnings
import gc
import numpy as np
import atorch
import torch
from functools import partial
from pathlib import Path
from deepspeed.ops.adam import DeepSpeedCPUAdam
from torch.distributed.fsdp import FullStateDictConfig
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp import StateDictType
from torch.optim.lr_scheduler import LambdaLR, CosineAnnealingLR, CosineAnnealingWarmRestarts
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from torch.utils.tensorboard import SummaryWriter
from tqdm.auto import tqdm
from transformers import get_scheduler as get_scheduler_trans
from transformers.modeling_utils import PreTrainedModel, unwrap_model
from transformers.trainer import (
OPTIMIZER_NAME,
SCHEDULER_NAME,
TRAINER_STATE_NAME,
TRAINING_ARGS_NAME
)
from transformers.trainer_pt_utils import reissue_pt_warnings
from transformers.trainer_utils import (
PREFIX_CHECKPOINT_DIR,
)
from transformers.utils import WEIGHTS_NAME
from torch.nn import CrossEntropyLoss
from utils.common_utils import print_rank_0, get_tflops_megatron, get_computation_speed, TASK2ID, ID2TASK, EarlyStopping, logger
from utils.auto_accelerate_utils import FAMO, get_ltor_masks_and_position_ids, SelfPacedStatus
from atorch.auto import auto_accelerate
from atorch.utils.version import torch_version
from model.gpt_neox.modeling_gpt_neox import GPTNeoXLayer, GPTNeoXAttention, GPTNeoXMLP
from model.llama.modeling_llama import LlamaDecoderLayer, LlamaAttention, LlamaMLP
from model.glm.modeling_glm import GLMBlock
from torch.cuda.amp import GradScaler
from apex.optimizers import FusedSGD
from model.peft.modeling_peft import PeftModel | 12,545 | # labels=batch['labels'],
)
# self paced loss
# ema_tmp: L_valid_ema at step t-1
if (self.global_steps + 1) % self.args.selfpaced_interval == 0 and (self.args.weighted_loss_mode.startswith('famo_valid_ema') or self.args.weighted_loss_mode == 'selfpaced'):
ema_tmp = 0.001 * self.valid_task_loss_prev + (1 - 0.001) * self.ema_valid_task_loss_prev
self.ratio_valid_task_loss_prev = (ema_tmp - self.ema_valid_task_loss_prev ) / (self.ema_valid_task_loss_prev + 1e-6)
self.ema_valid_task_loss_prev = ema_tmp
self.ratio_valid_task_loss_prev = torch.where(self.ratio_valid_task_loss_prev > 0, -1e20, self.ratio_valid_task_loss_prev)
self.famo.ratio_valid_task_loss_prev = self.ratio_valid_task_loss_prev
if self.args.weighted_loss_mode == 'selfpaced':
self.selfpaced_status.update(self.global_steps + 1, epoch + 1, 'train', self.ratio_valid_task_loss_prev)
loss, task_loss, task_num, self.famo, self.selfpaced_status = self.loss_func(outputs, batch, self.args.weighted_loss_mode, self.famo, self.selfpaced_status)
# print(f'rank: {self.rank}, loss: {loss}, task loss: {task_loss}')
if self.args.weighted_loss_mode.startswith('famo'):
self.famo.print_loss = self.famo.print_loss / self.args.gradient_accumulation_steps
loss_tensor = torch.zeros(
[1], device=self.famo.print_loss.device, dtype=self.famo.print_loss.dtype)
loss_tensor[0] = self.famo.print_loss.item()
else:
loss = loss / self.args.gradient_accumulation_steps
loss_tensor = torch.zeros(
[1], device=loss.device, dtype=loss.dtype)
loss_tensor[0] = loss.item()
torch.distributed.all_reduce(loss_tensor)
torch.distributed.all_reduce(task_loss, op=torch.distributed.ReduceOp.SUM)
torch.distributed.all_reduce(task_num, op=torch.distributed.ReduceOp.SUM)
reduce_loss = loss_tensor.sum() / torch.distributed.get_world_size()
if has_inf_or_nan(reduce_loss):
print_rank_0(f'There have nan loss.')
self.skipped_steps += 1
skipped = True
else:
self.accumulated_loss += reduce_loss.item()
mean_task_loss = task_loss / torch.distributed.get_world_size()
self.accumulated_task_loss += mean_task_loss.cpu().numpy()
self.accumulated_task_num += task_num.cpu().numpy()
loss.backward()
self.global_steps += 1
self.famo.global_steps += 1
if step % self.args.gradient_accumulation_steps == 0 or step == len(self.train_dataloader) - 1:
if self.args.max_grad_norm is not None and self.args.max_grad_norm > 0:
# 如果是fp16，需要unscale。如果是bf16，self.optimizer里没有unscale这个方法
try:
self.optimizer.unscale_()
except Exception:
pass
if isinstance(self.model, FSDP):
self.model.clip_grad_norm_(self.args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), self.args.max_grad_norm)
self.optimizer.step()
overflow = hasattr(self.optimizer, "step_was_skipped") and self.optimizer.step_was_skipped
if skipped != overflow:
print(f'skipped != overflow!!!!!!!!!!!!!!!!')
if not overflow:
self.lr_scheduler.step()
# if not skipped:
# self.optimizer.step()
# self.lr_scheduler.step()
self.optimizer.zero_grad()
if (self.args.weighted_loss_mode.startswith('famo_valid') or self.args.weighted_loss_mode == 'selfpaced') and not skipped:
# if self.args.weighted_loss_mode.startswith('famo_valid') and not skipped and step % self.args.famo_interval == 0:
# delete caches
# gc.collect()
# torch.cuda.empty_cache()
# self.model.eval()
# if (step // self.args.famo_interval) % self.valid_dataloader_length == 0:
if step % self.valid_dataloader_length == 0:
valid_iterator = iter(self.valid_dataloader)
valid_step = step % self.valid_dataloader_length
if self.famo_resume:
self.famo_resume = False
for i in range(valid_step):
v_batch = next(valid_iterator)
v_batch = next(valid_iterator)
if self.args.weighted_loss_mode == 'selfpaced':
self.valid_task_loss_prev = self.self_paced_evaluate(self.global_steps, epoch + 1, v_batch, self.ratio_valid_task_loss_prev)
else:
with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
valid_outputs = self.model(
input_ids=v_batch['input_ids'].to(self.device),
attention_mask=v_batch['attention_mask'].to(self.device),
position_ids=v_batch['position_ids'].to(self.device)
)
valid_loss, valid_task_loss, valid_task_num, _, _ = self.loss_func(valid_outputs, v_batch, self.args.weighted_loss_mode)
if self.args.weighted_loss_mode.startswith('famo_valid_ema'):
torch.distributed.all_reduce(valid_task_loss, op=torch.distributed.ReduceOp.SUM)
valid_task_loss /= torch.distributed.get_world_size()
self.valid_task_loss_prev = valid_task_loss.clone().detach()
# if self.famo.first_valid_step and self.args.resume_from_checkpoint == 'true':
if self.args.resume_from_checkpoint == 'true':
self.ema_valid_task_loss_prev = self.valid_task_loss_prev
# self.scaler.scale(valid_loss).backward()
valid_loss.backward()
valid_loss_item = valid_loss.item()
if self.args.max_grad_norm is not None and self.args.max_grad_norm > 0:
# 如果是fp16，需要unscale。如果是bf16，self.optimizer里没有unscale这个方法
try:
self.optimizer.unscale_()
except Exception:
pass
if isinstance(self.model, FSDP):
self.model.clip_grad_norm_(self.args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), self.args.max_grad_norm)
# print_rank_0(f'valid_loss {valid_loss.item()}, task loss: {valid_task_loss}, valid step: {valid_step}')
self.famo.update(valid_task_loss)
self.optimizer.zero_grad()
step_time = time.time() - step_start
| #!/usr/bin/env python
# coding=utf-8
# Copyright (c) 2023 Ant Group. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
HYPER_PARAMETER_NAME = 'hyper_parameters.json'
ATORCH_CHECKPOINT_NAME = 'atorch_checkpoint.bin'
EPOCH_CHECKPOINT_NAME = 'epoch'
FAMO_CHECKPOINT_NAME = 'famo_checkpoint'
EMA_CHECKPOINT_NAME = 'ema_checkpoint'
# logger = logging.getLogger(__name__)
def is_local_main_process():
return atorch.local_rank() == 0
def is_global_main_process():
return atorch.rank() == 0
def has_inf_or_nan(x):
try:
# if x is half, the .float() incurs an additional deep copy, but it's necessary if
# Pytorch's .sum() creates a one-element tensor of the same type as x
# (which is true for some recent version of pytorch).
cpu_sum = float(x.float().sum())
# More efficient version that can be used if .sum() returns a Python scalar
# cpu_sum = float(x.sum())
except RuntimeError as instance:
# We want to check if inst is actually an overflow exception.
# RuntimeError could come from a different error.
# If so, we still want the exception to propagate.
if "value cannot be converted" not in instance.args[0]:
raise
return True
else:
if cpu_sum == float('inf') or cpu_sum == -float('inf') or cpu_sum != cpu_sum:
return True
return False
def count_model_params(model):
trainable_params = 0
all_params = 0
for param in model.parameters():
num_params = param.numel()
all_params += num_params
if param.requires_grad:
trainable_params += num_params
return all_params, trainable_params
class AtorchArguments:
def __init__(self, **kwargs):
for key, value in kwargs.items():
setattr(self, key, value)
def get_linear_schedule_with_log_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1):
def lr_lambda(current_step: int):
inverse_log_warm_up = 1.0 / math.log(num_warmup_steps)
if current_step == 0:
return 0.0
if current_step < num_warmup_steps:
return inverse_log_warm_up * math.log(current_step)
return max(
0.0, float(num_training_steps - current_step) /
float(max(1, num_training_steps - num_warmup_steps))
)
return LambdaLR(optimizer, lr_lambda, last_epoch)
def get_scheduler(name, optimizer, num_warmup_steps, num_training_steps):
scheduler_map = {
'log_warmup_linear_decay': get_linear_schedule_with_log_warmup}
try:
lr_scheduler = get_scheduler_trans(
name, optimizer, num_warmup_steps, num_training_steps)
return lr_scheduler
except Exception:
schedule_func = scheduler_map[name]
return schedule_func(optimizer, num_warmup_steps, num_training_steps)
class AtorchTrainer:
def __init__(self,
model,
args,
train_dataset,
valid_dataset,
tokenizer=None,
callbacks=None,
no_save_atorch_checkpoint=None,
save_pytorch_model_bin_checkpoint=True,
train_peft=False,
rank=0,
max_shard_size='10GB',
files_to_save=None,
args_to_save=None,
data_collator=None,
my_loss_func=None,
**kwargs,
):
self.args = args
self.TASK2ID = TASK2ID
self.ID2TASK = ID2TASK
print('in atorch trainer')
print(TASK2ID)
print(ID2TASK)
self.model = model
self.no_save_atorch_checkpoint = no_save_atorch_checkpoint
self.save_pytorch_model_bin_checkpoint = save_pytorch_model_bin_checkpoint
self.train_peft = train_peft
self.rank = rank
self.kwargs = kwargs
self.train_dataset = train_dataset
self.valid_dataset = valid_dataset
self.tokenizer = tokenizer
self.max_shard_size = max_shard_size
self.files_to_save = files_to_save
self.args_to_save = args_to_save
self.best_metric = None
self.best_model_checkpoint = None
self.no_save_base_model = True
self.device = f"cuda:{atorch.local_rank()}"
self.famo = FAMO(n_tasks=len(TASK2ID), device=self.device, mode=self.args.weighted_loss_mode)
self.famo_resume = False
self.selfpaced_status = SelfPacedStatus(args.selfpaced_interval)
self.total_train_batch_size = self.args.per_device_train_batch_size * \
self.args.gradient_accumulation_steps * \
atorch.world_size()
self.data_collator = data_collator
self.my_loss_func = my_loss_func
if self.args.early_stopping_patience > 0:
print(f'early_stopping_patience: {self.args.early_stopping_patience}')
patience = self.args.early_stopping_patience
self.early_stopping = EarlyStopping(patience, verbose=True)
self.train_dataloader_args = {
"shuffle": True,
"batch_size": self.total_train_batch_size,
"pin_memory": True,
"collate_fn": data_collator,
"drop_last": True,
"num_workers": self.args.num_workers,
# "persistent_workers": args.num_workers > 0,
}
self.valid_dataloader = DataLoader(
valid_dataset,
sampler=DistributedSampler(valid_dataset, shuffle=True),
batch_size=args.per_device_valid_batch_size,
pin_memory=True,
collate_fn=data_collator
)
self.valid_dataloader_length = len(self.valid_dataloader)
if self.args.resume_from_checkpoint == 'true':
self.resume_checkpoint_dir = self.get_last_checkpoint(
self.args.output_dir)
self.atorch_args = AtorchArguments(
lr=args.learning_rate,
weight_decay=args.weight_decay,
adam_eps=args.adam_epsilon,
adam_beta1=args.adam_beta1,
adam_beta2=args.adam_beta2)
self.atorch_init()
self.num_update_steps_per_epoch = math.ceil(
len(self.train_dataloader) / self.args.gradient_accumulation_steps)
print(f'number of update steps per epoch: {self.num_update_steps_per_epoch}')
if self.args.max_steps == -1:
self.args.max_steps = int(
self.args.num_train_epochs * self.num_update_steps_per_epoch)
else:
self.args.num_train_epochs = math.ceil(
self.args.max_steps / self.num_update_steps_per_epoch)
# self.args.warmup_steps = self.args.get_warmup_steps(
# self.args.max_steps) # 找不到get_warmup_steps
custom_lr_scheduler_type = self.kwargs.get(
'custom_lr_scheduler_type', None)
self.lr_scheduler = get_scheduler(
name=custom_lr_scheduler_type if custom_lr_scheduler_type else self.args.lr_scheduler_type,
optimizer=self.optimizer,
num_warmup_steps=self.args.num_warmup_steps,
num_training_steps=self.args.max_steps,
)
print_rank_0(f'lr_scheduler{self.lr_scheduler}')
if self.args.resume_from_checkpoint == 'true':
with warnings.catch_warnings(record=True):
self.lr_scheduler.load_state_dict(torch.load(
os.path.join(self.resume_checkpoint_dir, SCHEDULER_NAME)))
self._load_rng_state(self.resume_checkpoint_dir)
torch.distributed.barrier()
now_datetime = datetime.datetime.now()
timestr = datetime.datetime.strftime(now_datetime, '%Y%m%d-%H%M%S')
self.log_dir = os.path.join(self.args.output_dir, 'runs', timestr)
self.summary_writer = None
if torch.distributed.get_rank() == 0:
self.summary_writer = SummaryWriter(log_dir=self.log_dir)
def get_last_checkpoint(self, folder):
_re_checkpoint = re.compile(r"^" + PREFIX_CHECKPOINT_DIR + r"\-(\d+)")
content = sorted(os.listdir(folder))
checkpoints = [
path
for path in content
if _re_checkpoint.search(path) is not None and os.path.isdir(os.path.join(folder, path))
]
if len(checkpoints) == 0:
return
return os.path.join(folder, max(checkpoints, key=lambda x: int(_re_checkpoint.search(x).groups()[0])))
def _load_rng_state(self, resume_checkpoint_dir):
# Load RNG states from `checkpoint`
if resume_checkpoint_dir is None:
return
if self.args.world_size > 1:
rng_file = os.path.join(
resume_checkpoint_dir, f"rng_state_{self.rank}.pth")
if not os.path.isfile(rng_file):
logger.info(
f"Didn't find an RNG file for process {self.rnak}, if you are resuming a training that "
"wasn't launched in a distributed fashion, reproducibility is not guaranteed."
)
return
else:
rng_file = os.path.join(resume_checkpoint_dir, "rng_state.pth")
if not os.path.isfile(rng_file):
logger.info(
"Didn't find an RNG file, if you are resuming a training that was launched in a distributed "
"fashion, reproducibility is not guaranteed."
)
return
checkpoint_rng_state = torch.load(rng_file)
random.setstate(checkpoint_rng_state["python"])
np.random.set_state(checkpoint_rng_state["numpy"])
torch.random.set_rng_state(checkpoint_rng_state["cpu"])
if torch.cuda.is_available():
if self.args.local_rank != -1:
torch.cuda.random.set_rng_state(checkpoint_rng_state["cuda"])
else:
try:
torch.cuda.random.set_rng_state_all(
checkpoint_rng_state["cuda"])
except Exception as e:
logger.info(
f"Didn't manage to set back the RNG states of the GPU because of the following error:\n {e}"
"\nThis won't yield the same results as if the training had not been interrupted."
)
def load_atorch_model_state(self, model_state_dict, **kwargs):
print('resume atorch model state')
if self.is_rank0():
self.model.load_state_dict(model_state_dict)
# 在 rank 0 加载完毕后，再通过sync_module_states分发参数
torch.distributed.barrier()
# self.model = FSDP(self.model, sync_module_states=True, **kwargs)
def load_atorch_optim_state(self, optim_state_dict):
print('resume optimizer state')
optim_state_dict = FSDP.scatter_full_optim_state_dict(
optim_state_dict, self.model) # may be removed after PyTorch 2.2
def move_optim_state_to_cpu(optim_state_dict):
for k in optim_state_dict:
if isinstance(optim_state_dict[k], torch.Tensor):
optim_state_dict[k] = optim_state_dict[k].cpu()
elif isinstance(optim_state_dict[k], dict):
move_optim_state_to_cpu(optim_state_dict[k])
move_optim_state_to_cpu(optim_state_dict)
self.optimizer.load_state_dict(optim_state_dict)
def load_famo_state(self):
print_rank_0(f'loading famo checkpoint')
self.famo_resume = True
famo_dir = os.path.join(self.resume_checkpoint_dir, 'famo_checkpoint/')
if not os.path.exists(famo_dir):
print_rank_0(f'can not find the famo checkpoint dir!')
else:
famo_state_name = FAMO_CHECKPOINT_NAME + f'_rank_{self.rank}.pth'
famo_checkpoint_state = torch.load(os.path.join(famo_dir, famo_state_name))
w_opt_state = famo_checkpoint_state['w_opt_state']
self.famo.prev_train_loss = famo_checkpoint_state['prev_train_loss'].to(self.famo.device)
self.famo.prev_valid_loss = famo_checkpoint_state['prev_valid_loss'].to(self.famo.device)
self.famo.first_train_step = famo_checkpoint_state['first_train_step']
self.famo.first_valid_step = famo_checkpoint_state['first_valid_step']
self.famo.ratio_valid_task_loss_prev = famo_checkpoint_state['ratio_valid_task_loss_prev'].to(self.famo.device)
self.famo.w = famo_checkpoint_state['w'].to(self.famo.device)
self.famo.w_opt.load_state_dict(w_opt_state)
print_rank_0(f'prev_train_loss: {self.famo.prev_train_loss}')
print_rank_0(f'prev_valid_loss: {self.famo.prev_valid_loss}')
print_rank_0(f'first_train_step: {self.famo.first_train_step}')
print_rank_0(f'first_valid_step: {self.famo.first_valid_step}')
print_rank_0(f'ratio_valid_task_loss_prev: {self.famo.ratio_valid_task_loss_prev}')
print_rank_0(f'w: {self.famo.w}')
print_rank_0(f'load famo checkpoint successfully')
def atorch_init(self):
assert torch_version() >= (2, 0, 0), "use pt2.0 for use orig param if fsdp"
if self.args.model_type == 'gpt_neox':
# wrap_class = (GPTNeoXAttention, GPTNeoXMLP)
wrap_class = (GPTNeoXLayer,)
elif self.args.model_type == 'llama':
# wrap_class = (LlamaAttention, LlamaMLP)
wrap_class = (LlamaDecoderLayer,)
elif self.args.model_type == 'glm':
wrap_class = (GLMBlock,)
parallel_mode = []
if self.args.dp:
# p_mode = ([("data", torch.distributed.get_world_size())], None)
parallel_mode.append(("data", self.args.dp))
if self.args.tp:
parallel_mode.append(("tensor_parallel", self.args.tp))
strategy = [
# ("parallel_mode", p_mode),
("parallel_mode", (parallel_mode, None)),
"module_replace",
# ("fsdp", fsdp_config),
# ("amp_native", {"dtype": torch.bfloat16}) if self.args.bf16 else "amp_native",
# ("checkpoint", wrap_class),
]
if self.args.peft_type is None or self.args.peft_type == 'lora':
cpu_offload = False if self.args.total_model_param < 1e9 else True
fsdp_config = {
"atorch_wrap_cls": wrap_class,
"sync_module_states": True,
"use_orig_params": True,
"limit_all_gathers": True,
# "cpu_offload": True,
}
print(fsdp_config)
fsdp_opt = ("fsdp", fsdp_config)
strategy.append(fsdp_opt)
self.args.atorch_opt = "fsdp"
else:
num_all_params, num_trainable_params = count_model_params(self.model)
if num_all_params < 11e9 or self.args.peft_type == "qlora": # For GLM-10B
logger.info(
f"Found using {self.args.peft_type} method. The peft model has {num_all_params} and only "
f"{num_trainable_params} params are trainable({100 * num_trainable_params / num_all_params}%)"
". Set atorch opt to DistributedDataParallel.")
self.args.atorch_opt = "ddp"
if self.args.bf16 or self.args.fp16:
if self.args.bf16:
amp_config = {"dtype": torch.bfloat16, "skip_if_nonfinite": True}
# amp_config = {"dtype": torch.bfloat16}
if self.args.peft_type == "qlora":
# The dtype of grads is bf16 when using qlora
# atorch scaler does not support bf16 grads.
amp_config["skip_if_nonfinite"] = False
elif self.args.fp16:
amp_config = {"dtype": torch.float16}
strategy.append(("amp_native", amp_config))
# strategy.append(("half", "bf16"))
if self.args.checkpoint_activations:
strategy.append(("checkpoint", wrap_class))
print(f"Manually loaded auto acc strategy: {strategy}")
def prepare_input(batch, device): # DEBUG: GLM NoneType
batch = {k: v.to(device=device, non_blocking=True) if v is not None else None
for k, v in batch.items()}
return batch
def optim_param_func(model, args):
no_decay = ["bias", "LayerNorm.weight", "layernorm.weight"]
optimizer_grouped_parameters = [
{
"params": [
p
for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)
],
"weight_decay": args.weight_decay,
},
{
"params": [
p
for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)
],
"weight_decay": 0.0,
},
]
return optimizer_grouped_parameters
# load fsdp checkpoint参数
if self.args.resume_from_checkpoint == 'true':
logger.info(f'Resume training from {self.resume_checkpoint_dir}')
if self.is_rank0():
sd = torch.load(os.path.join(
self.resume_checkpoint_dir, ATORCH_CHECKPOINT_NAME), map_location='cpu')
model_state_dict, optim_state_dict = sd['model_state_dict'], sd['optimizer_state_dict']
else:
model_state_dict, optim_state_dict = None, None
torch.distributed.barrier() # other rank waiting
##########
self.load_atorch_model_state(model_state_dict)
##########
if self.is_rank0():
print(f'GPU mem before fsdp:')
print(torch.cuda.memory_summary(device=self.device, abbreviated=False))
optim_func = torch.optim.AdamW
print(f'optimizer before fsdp: {optim_func}')
ddp_find_unused_parameters = None
if self.args.atorch_opt == "ddp" and not (self.args.peft_type in ["lora", "qlora"] and self.args.checkpoint_activations):
ddp_find_unused_parameters = True
status, result, best_strategy = auto_accelerate(
self.model,
optim_func,
self.train_dataset,
dataloader_args=self.train_dataloader_args,
loss_func=self.my_loss_func,
prepare_input=prepare_input,
optim_args={
"lr": self.atorch_args.lr,
"weight_decay": self.atorch_args.weight_decay,
"eps": self.atorch_args.adam_eps,
"betas": (self.atorch_args.adam_beta1, self.atorch_args.adam_beta2),
},
optim_param_func=partial(
optim_param_func, args=self.atorch_args),
load_strategy=strategy,
ignore_dryrun_on_load_strategy=True,
find_unused_parameters=ddp_find_unused_parameters,
)
assert (
status
), f"auto_accelerate failed. status: {status}, result: {result}, best_strategy: {best_strategy}"
print(f"Best strategy is: {best_strategy}")
self.model = result.model
self.optimizer = result.optim
print(f'optimizer after fsdp: {self.optimizer}')
self.loss_func = result.loss_func
self.train_dataloader = result.dataloader
self.prepare_input = result.prepare_input
if self.args.resume_from_checkpoint == 'true':
self.load_atorch_optim_state(optim_state_dict)
if self.args.weighted_loss_mode.startswith('famo_valid'):
self.load_famo_state()
print(f"atorch use optimizer: {self.optimizer}")
if self.is_rank0():
print(f'GPU mem after fsdp:')
print(torch.cuda.memory_summary(device=self.device, abbreviated=False))
def evaluate(self):
logger.info(f"Start evaluation")
if self.is_rank0():
print(torch.cuda.memory_summary(device=self.device, abbreviated=False))
print(f'valid dataset length is: {len(self.valid_dataset)}')
print(f'valid dataloader length is: {len(self.valid_dataloader)}')
print(f'per device batch size: {self.args.per_device_valid_batch_size}')
progress_bar = tqdm(range(len(self.valid_dataloader)),
disable=not is_local_main_process(),
smoothing=0)
self.model.eval()
losses = []
accumulated_task_loss_np = np.zeros(len(self.ID2TASK))
accumulated_task_num_np = np.zeros(len(self.ID2TASK))
accumulated_step = 0
for step, batch in enumerate(self.valid_dataloader):
# if step >= self.args.valid_iters:
if step >= self.args.valid_iters and (self.args.total_model_param >= 1e9 or self.args.train_mode == 'sst'):
break
with torch.no_grad():
# batch = {k: v.to(self.device) for k, v in batch.items()}
# batch = self.prepare_input(batch, self.device)
# outputs = self.model(**batch)
outputs = self.model(
input_ids=batch['input_ids'].to(self.device),
attention_mask=batch['attention_mask'].to(self.device),
position_ids=batch['position_ids'].to(self.device)
)
# loss = outputs["loss"]
loss, task_loss, task_num, _, _ = self.loss_func(outputs, batch, self.args.weighted_loss_mode)
repeated_loss = loss.repeat(
self.args.per_device_valid_batch_size)
if repeated_loss.ndim == 0:
repeated_loss = repeated_loss.clone()[None]
output_tensors = [repeated_loss.clone()
for _ in range(atorch.world_size())]
torch.distributed.all_gather(output_tensors, repeated_loss)
for tensor in output_tensors:
if torch.isnan(tensor).any() or torch.isinf(tensor).any():
accumulated_step -= 1
continue
losses.append(torch.cat(output_tensors, dim=0).cpu())
task_loss = task_loss.cpu().numpy()
task_num = task_num.cpu().numpy()
accumulated_task_loss_np += task_loss
accumulated_task_num_np += task_num
accumulated_step += 1
progress_bar.update(1)
losses = torch.cat(losses)
losses = losses[: len(self.valid_dataset)]
mean_loss = torch.mean(losses).item()
accumulated_task_loss = torch.tensor(accumulated_task_loss_np).to(self.device)
accumulated_task_num = torch.tensor(accumulated_task_num_np).to(self.device)
torch.distributed.all_reduce(accumulated_task_loss, op=torch.distributed.ReduceOp.SUM)
torch.distributed.all_reduce(accumulated_task_num, op=torch.distributed.ReduceOp.SUM)
accumulated_task_loss /= torch.distributed.get_world_size()
valid_task_loss = accumulated_task_loss / (accumulated_step - 1)
logs = {'valid_loss': mean_loss}
per_task_valid_loss = {self.ID2TASK[i]+'_loss': valid_task_loss[i].item() for i in range(len(self.ID2TASK))}
logs.update(per_task_valid_loss)
if is_global_main_process():
logger.info('log point')
for i in range(len(self.ID2TASK)):
if accumulated_task_num[i] != 0:
logger.info(f"{self.ID2TASK[i]}_loss: {valid_task_loss[i]}, sample nums: {accumulated_task_num[i]}")
self.log(logs, step=self.global_steps, phase='Evaluation')
metrics = {'valid_loss': mean_loss, 'valid_task_loss': valid_task_loss}
logger.info(f"Finish evaluation")
if self.is_rank0():
print(torch.cuda.memory_summary(device=self.device, abbreviated=False))
return metrics
def log(self, logs, step, phase='Train'):
if not self.summary_writer:
return
logger.info(json.dumps(logs))
for key, value in logs.items():
self.summary_writer.add_scalar(f'{phase}/{key}', value, step)
def _sorted_checkpoints(
self,
output_dir=None,
checkpoint_prefix=PREFIX_CHECKPOINT_DIR,
checkpoint_name_pattern='([0-9]+)',
use_mtime=False
):
ordering_and_checkpoint_path = []
glob_checkpoints = [str(x) for x in Path(output_dir).glob(
f"{checkpoint_prefix}-*") if os.path.isdir(x)]
for path in glob_checkpoints:
if use_mtime:
ordering_and_checkpoint_path.append(
(os.path.getmtime(path), path))
else:
regex_match = re.search(
f".*{checkpoint_prefix}-({checkpoint_name_pattern})", path)
if regex_match is not None and regex_match.groups() is not None:
ordering_and_checkpoint_path.append(
(int(regex_match.groups()[0]), path))
checkpoints_sorted = sorted(ordering_and_checkpoint_path)
checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
# Make sure we don't delete the best model.
if self.best_model_checkpoint is not None:
best_model_index = checkpoints_sorted.index(str(Path(self.best_model_checkpoint)))
# for i in range(best_model_index, len(checkpoints_sorted) - 2):
for i in range(best_model_index, len(checkpoints_sorted) - 1):
checkpoints_sorted[i], checkpoints_sorted[i + 1] = checkpoints_sorted[i + 1], checkpoints_sorted[i]
print_rank_0(f'checkpoints sorted list: {checkpoints_sorted}')
return checkpoints_sorted
def _rotate_checkpoints(
self,
use_mtime=False,
output_dir=None,
prefix=PREFIX_CHECKPOINT_DIR,
checkpoint_name_pattern='.*') -> None:
if self.args.save_total_limit is None or self.args.save_total_limit <= 0:
return
# Check if we should delete older checkpoint(s)
checkpoints_sorted = self._sorted_checkpoints(
use_mtime=use_mtime,
output_dir=output_dir,
checkpoint_prefix=prefix,
checkpoint_name_pattern=checkpoint_name_pattern)
if len(checkpoints_sorted) <= self.args.save_total_limit:
return
# If save_total_limit=1 with load_best_model_at_end=True, we could end up deleting the last checkpoint, which
# we don't do to allow resuming.
save_total_limit = self.args.save_total_limit
number_of_checkpoints_to_delete = max(
0, len(checkpoints_sorted) - save_total_limit)
checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
for checkpoint in checkpoints_to_be_deleted:
logger.info(
f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit")
shutil.rmtree(checkpoint, ignore_errors=True)
def _clean_atorch_checkpoints(self, output_dir=None, prefix=PREFIX_CHECKPOINT_DIR):
# Check if we should delete older checkpoint(s)
checkpoints_sorted = self._sorted_checkpoints(
output_dir=output_dir,
checkpoint_prefix=prefix,
checkpoint_name_pattern='([0-9]+)')
# If save_total_limit=1 with load_best_model_at_end=True, we could end up deleting the last checkpoint, which
# we don't do to allow resuming.
for checkpoint in checkpoints_sorted[:-1]:
logger.info(
f"Deleting older atorch checkpoint [{checkpoint}] due to self.args.save_total_limit")
try:
os.remove(os.path.join(checkpoint, ATORCH_CHECKPOINT_NAME))
except Exception:
continue
def _save_peft_model(self, output_dir, state_dict=None):
logger.info(f"Start saving peft model to {output_dir}")
output_dir = output_dir if output_dir is not None else self.args.output_dir
os.makedirs(output_dir, exist_ok=True)
model = unwrap_model(self.model)
if isinstance(model, PeftModel):
if state_dict is None:
state_dict = model.state_dict()
model.save_pretrained(
output_dir, state_dict=state_dict, is_main_process=self.is_rank0())
else:
if state_dict is None:
state_dict = self.model.state_dict()
if self.is_rank0():
torch.save(state_dict, os.path.join(
output_dir, "pytorch_model.bin"))
logger.info(f"Saving peft model done.")
def _save_model(self, output_dir=None, state_dict=None):
# If we are executing this function, we are the process zero, so we don't check for that.
output_dir = output_dir if output_dir is not None else self.args.output_dir
os.makedirs(output_dir, exist_ok=True)
logger.info(f"Saving model checkpoint to {output_dir}")
# Save a trained model and configuration using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
if not isinstance(self.model, PreTrainedModel):
if isinstance(unwrap_model(self.model), PreTrainedModel):
print_rank_0('save in not pretrained model~~~~~~~')
if state_dict is None:
state_dict = self.model.state_dict()
# state_dict = {key: value.bfloat16() if self.args.bf16 else value.half() for key, value in self.model.state_dict().items()}
model = unwrap_model(self.model)
model.save_pretrained(
output_dir,
state_dict=state_dict,
max_shard_size=self.max_shard_size,
is_main_process=self.is_rank0())
# unwrap_model(self.model).save_pretrained(
# output_dir, state_dict=state_dict, max_shard_size=self.max_shard_size)
elif isinstance(unwrap_model(self.model), PeftModel):
if state_dict is None:
state_dict = unwrap_model(self.model).base_model.model.state_dict()
# state_dict = {key: value.bfloat16() if self.args.bf16 else value.half() for key, value in state_dict.items()}
# Filter the peft params ...
param_keys = list(state_dict.keys())
base_model_state_dict = {}
for key in param_keys:
if LORA_KEY in key:
# state_dict.pop(key)
continue
elif PEFT_PARAM_PREFIX in key:
# value = state_dict.pop(key)
value = state_dict[key]
new_key = key.replace(PEFT_PARAM_PREFIX, "")
base_model_state_dict[new_key] = value
else:
base_model_state_dict[key] = value
if self.is_rank0():
torch.save(base_model_state_dict,
os.path.join(output_dir, WEIGHTS_NAME))
else:
logger.info(
"Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
if state_dict is None:
state_dict = self.model.state_dict()
# state_dict = {key: value.bfloat16() if self.args.bf16 else value.half() for key, value in self.model.state_dict().items()}
torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
else:
print(f'save in pretrained model!!!!!!')
if state_dict is None:
state_dict = self.model.state_dict()
# state_dict = {key: value.bfloat16() if self.args.bf16 else value.half() for key, value in self.model.state_dict().items()}
self.model.save_pretrained(
output_dir,
state_dict=state_dict,
max_shard_size=self.max_shard_size)
# if self.tokenizer is not None and self.args.model_type == 'glm': # TODO: 需要适配加载tokenizer，主要是gpt2_multi_task_dataset的encode部分里的tokenizer.tokenize函数
if self.tokenizer is not None:
self.tokenizer.save_pretrained(output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))
def is_rank0(self):
return self.rank == 0
def _save_atorch_checkpoint(self, output_dir):
# StateDictType.FULL_STATE_DICT得到完整的模型状态。
# FullStateDictConfig指定保存到CPU，仅rank0保存
save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
with FSDP.state_dict_type(self.model, StateDictType.FULL_STATE_DICT, save_policy):
model_state_dict = self.model.state_dict()
optim_state_dict = FSDP.full_optim_state_dict(self.model, self.optimizer) # may be removed after PyTorch 2.2
if self.is_rank0():
os.makedirs(output_dir, exist_ok=True)
torch.save(
{
"model_state_dict": model_state_dict,
"optimizer_state_dict": optim_state_dict,
"global_steps": self.global_steps,
},
os.path.join(output_dir, ATORCH_CHECKPOINT_NAME),
)
torch.distributed.barrier() # other rank waiting
def _save_famo_checkpoint(self, output_dir):
famo_dir = os.path.join(output_dir, 'famo_checkpoint/')
os.makedirs(famo_dir, exist_ok=True)
if self.famo.w_opt is not None:
optim_state = self.famo.w_opt.state_dict()
famo_state = {'prev_train_loss': self.famo.prev_train_loss,
'prev_valid_loss': self.famo.prev_valid_loss,
'first_train_step': self.famo.first_train_step,
'first_valid_step': self.famo.first_valid_step,
'ratio_valid_task_loss_prev': self.famo.ratio_valid_task_loss_prev,
'w': self.famo.w,
'w_opt_state': optim_state,
}
famo_state_name = FAMO_CHECKPOINT_NAME + f'_rank_{self.rank}.pth'
torch.save(famo_state, os.path.join(famo_dir, famo_state_name))
# if self.is_rank0():
# torch.save(famo_state, os.path.join(output_dir, FAMO_CHECKPOINT_NAME))
torch.distributed.barrier() # other rank waiting
def save(self, suffix=None, metrics=None):
logger.info('Save start')
if self.is_rank0():
print(torch.cuda.memory_summary(device=self.device, abbreviated=False))
if not self.save_pytorch_model_bin_checkpoint:
return
if suffix is None:
checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.global_steps}"
else:
checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{suffix}"
run_dir = self.args.output_dir
output_dir = os.path.join(run_dir, checkpoint_folder)
# self._save_model(output_dir)
# 获取要存的state_dict, 每个rank都要调用
if isinstance(self.model, FSDP):
save_policy = FullStateDictConfig(offload_to_cpu=atorch.world_size() > 1, rank0_only=atorch.world_size() > 1)
with FSDP.state_dict_type(self.model, StateDictType.FULL_STATE_DICT, save_policy):
model_state_dict = self.model.state_dict()
optim_state_dict = FSDP.full_optim_state_dict(self.model, self.optimizer) # may be removed after PyTorch 2.2
else:
model_state_dict = unwrap_model(self.model).state_dict()
optim_state_dict = self.optimizer.state_dict()
if not self.no_save_atorch_checkpoint:
if self.args.peft_type is None or not self.no_save_base_model:
if self.is_rank0():
os.makedirs(output_dir, exist_ok=True)
torch.save(
{
"model_state_dict": model_state_dict,
"optimizer_state_dict": optim_state_dict,
"global_steps": self.global_steps,
},
os.path.join(output_dir, ATORCH_CHECKPOINT_NAME),
)
torch.distributed.barrier() # other rank waiting
if self.args.peft_type is not None:
print(f'no_save_base_model: {self.no_save_base_model}')
if not self.no_save_base_model:
self._save_model(output_dir=output_dir)
self._save_peft_model(output_dir=output_dir)
else:
self._save_model(output_dir=output_dir)
# if not self.no_save_atorch_checkpoint:
# self._save_atorch_checkpoint(output_dir)
# else:
# torch.save(self.optimizer.state_dict(),
# os.path.join(output_dir, OPTIMIZER_NAME))
with warnings.catch_warnings(record=True) as caught_warnings:
torch.save(self.lr_scheduler.state_dict(),
os.path.join(output_dir, SCHEDULER_NAME))
reissue_pt_warnings(caught_warnings)
# Save RNG state in non-distributed training
rng_states = {
"python": random.getstate(),
"numpy": np.random.get_state(),
"cpu": torch.random.get_rng_state(),
}
if torch.cuda.is_available():
if self.args.local_rank == -1:
# In non distributed, we save the global CUDA RNG state (will take care of DataParallel)
rng_states["cuda"] = torch.cuda.random.get_rng_state_all()
else:
rng_states["cuda"] = torch.cuda.random.get_rng_state()
os.makedirs(output_dir, exist_ok=True)
if torch.distributed.get_world_size() <= 1:
torch.save(rng_states, os.path.join(output_dir, "rng_state.pth"))
else:
# torch.save(rng_states, os.path.join(
# output_dir, f"rng_state_{self.args.process_index}.pth")) # process_index = rank
torch.save(rng_states, os.path.join(
output_dir, f"rng_state_{self.rank}.pth"))
if self.args_to_save:
json.dump(self.args_to_save, open(os.path.join(output_dir,
HYPER_PARAMETER_NAME), 'w'), ensure_ascii=False, indent=2)
# save state
state = {'global_steps': self.global_steps}
json.dump(state, open(os.path.join(
output_dir, TRAINER_STATE_NAME), 'w'), ensure_ascii=False, indent=2)
# if self.args.weighted_loss_mode == "selfpaced" or self.args.weighted_loss_mode.startswith('famo_valid_ema'):
# ema_dir = os.path.join(output_dir, 'ema_checkpoint/')
# os.makedirs(ema_dir, exist_ok=True)
# ema_state = {'valid_task_loss_prev': self.valid_task_loss_prev,
# 'ema_valid_task_loss_prev': self.ema_valid_task_loss_prev,
# 'ratio_valid_task_loss_prev': self.ratio_valid_task_loss_prev}
# ema_state_name = EMA_CHECKPOINT_NAME + f'_rank_{self.rank}.pth'
# torch.save(ema_state, os.path.join(ema_dir, ema_state_name))
# torch.distributed.barrier() # other rank waiting
# print_rank_0(f'ema state to save: {ema_state}')
# if self.files_to_save:
# for name in self.files_to_save:
# if not os.path.exists(name):
# continue
# try:
# if os.path.isfile(name):
# shutil.copy(name, output_dir)
# elif os.path.isdir(name):
# shutil.copytree(name, os.path.join(
# output_dir, os.path.basename(name)))
# except Exception:
# continue
# Determine the new best metric / best model checkpoint
if metrics is not None and self.args.metric_for_best_model is not None:
metric_to_check = self.args.metric_for_best_model
if not metric_to_check.startswith("valid_"):
metric_to_check = f"valid_{metric_to_check}"
metric_value = metrics[metric_to_check]
operator = np.greater if self.args.greater_is_better == 'true' else np.less
if (
self.best_metric is None
or self.best_model_checkpoint is None
or operator(metric_value, self.best_metric)
):
self.best_metric = metric_value
self.best_model_checkpoint = output_dir
print_rank_0(f'current best model checkpoint is: {self.best_model_checkpoint}, valid_loss: {self.best_metric}')
if self.args.weighted_loss_mode.startswith('famo_valid'):
self._save_famo_checkpoint(output_dir)
if self.is_rank0():
if self.args.extra_save_by_epoch:
print('extra_save_by_epoch')
# 如果是每个epoch extra save的，那么每个epoch的checkpoint不会删除，不受save_total_limit的影响，
# 而对按step存的，则会只保留save_total_limit个
self._rotate_checkpoints(
output_dir=run_dir, prefix=PREFIX_CHECKPOINT_DIR, checkpoint_name_pattern='([0-9]+)$')
else:
self._rotate_checkpoints(
output_dir=run_dir, prefix=PREFIX_CHECKPOINT_DIR)
# 只保留最新一个checkpoint的atorch checkpoint
self._clean_atorch_checkpoints(
output_dir=run_dir, prefix=PREFIX_CHECKPOINT_DIR)
print(torch.cuda.memory_summary(device=self.device, abbreviated=False))
torch.distributed.barrier()
logger.info('Save finished')
def self_paced_evaluate(self, global_steps, current_epoch, v_batch, ratio_valid_task_loss_prev):
self.model.eval()
# v_batch = next(valid_iterator)
with torch.no_grad():
valid_outputs = self.model(
input_ids=v_batch['input_ids'],
attention_mask=v_batch['attention_mask'],
position_ids=v_batch['position_ids']
)
self.selfpaced_status.update(global_steps, current_epoch, 'valid', ratio_valid_task_loss_prev)
_, valid_task_loss, valid_task_num, _, self.selfpaced_status = self.loss_func(valid_outputs, v_batch, self.args.weighted_loss_mode, self.famo, self.selfpaced_status)
torch.distributed.all_reduce(valid_task_loss, op=torch.distributed.ReduceOp.SUM)
valid_task_loss /= torch.distributed.get_world_size()
# print_rank_0(f"self paced valid loss: {valid_task_loss}")
return valid_task_loss
def train(self, **kwargs):
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(self.train_dataset)}")
logger.info(f" Num Epochs = {self.args.num_train_epochs}")
logger.info(
f" Instantaneous batch size per device = {self.args.per_device_train_batch_size}")
logger.info(
f" Total train batch size (w. parallel, distributed & accumulation) = {self.total_train_batch_size}")
logger.info(
f" Gradient Accumulation steps = {self.args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {self.args.max_steps}")
progress_bar = tqdm(range(self.args.max_steps),
disable=not is_local_main_process(),
smoothing=0)
training_time = 0
self.global_steps = 0
start_epoch = 0
steps_trained_in_current_epoch = 0
exit_flag = False
if self.args.resume_from_checkpoint == 'true':
state = json.load(
open(os.path.join(self.resume_checkpoint_dir, TRAINER_STATE_NAME), 'r'))
self.global_steps = state.get('global_steps', 0)
# progress_bar.update(self.global_steps)
progress_bar = tqdm(range(self.args.max_steps),
disable=not is_local_main_process(),
initial=self.global_steps,
smoothing=0)
start_epoch = self.global_steps // self.num_update_steps_per_epoch
steps_trained_in_current_epoch = self.global_steps % self.num_update_steps_per_epoch
steps_trained_in_current_epoch *= self.args.gradient_accumulation_steps
print(f'Start training at step {self.global_steps}')
self.last_step_logged = self.global_steps
self.skipped_steps = 0
self.accumulated_loss = 0
self.accumulated_task_loss = np.zeros(len(self.ID2TASK))
self.accumulated_task_num = np.zeros(len(self.ID2TASK))
self.args.selfpaced_interval = 20 if self.args.total_model_param >= 1e9 else 20
self.args.famo_interval = 10
self.train_task_loss_prev = None
self.valid_task_loss_prev = None # L_valid at step t-1
self.ema_valid_task_loss_prev = None # L_valid_ema at step t-2
self.ratio_valid_task_loss_prev = torch.zeros(len(self.ID2TASK)).to(self.device) # ema ratio at step t-1
if self.args.weighted_loss_mode.startswith('famo_valid_ema'):
if self.args.resume_from_checkpoint == 'true': # TODO
pass
# ema_dir = os.path.join(self.resume_checkpoint_dir, 'ema_checkpoint/')
# if not os.path.exists(ema_dir):
# print_rank_0(f'can not find the famo checkpoint dir!')
# else:
# ema_state_name = EMA_CHECKPOINT_NAME + f'_rank_{self.rank}.pth'
# ema_checkpoint_state = torch.load(os.path.join(ema_dir, ema_state_name))
# self.valid_task_loss_prev = ema_checkpoint_state['valid_task_loss_prev'].to(self.device) # L_valid at step t-1
# self.ema_valid_task_loss_prev = ema_checkpoint_state['ema_valid_task_loss_prev'].to(self.device) # L_valid_ema at step t-2
# self.ratio_valid_task_loss_prev = ema_checkpoint_state['ratio_valid_task_loss_prev'].to(self.device) # ema ratio at step t-1
# print_rank_0(f'trainer state from resume: {ema_checkpoint_state}')
else:
metrics = self.evaluate()
self.ema_valid_task_loss_prev = metrics['valid_task_loss']
self.famo.global_steps = self.global_steps
if self.args.weighted_loss_mode == "selfpaced":
self.ema_valid_task_loss_prev = self.self_paced_evaluate(self.global_steps, start_epoch + 1, next(iter(self.valid_dataloader)), self.ratio_valid_task_loss_prev)
for epoch in range(start_epoch, int(self.args.num_train_epochs)):
self.train_dataloader.set_epoch(epoch)
self.model.train()
start_time = time.time()
valid_iterator = iter(self.valid_dataloader)
for step, batch in enumerate(self.train_dataloader):
if step == 0:
print_rank_0(f"step 1 batch shape: {batch['input_ids'].shape},\n"
f"last 10 tokens: {batch['input_ids'][:, -10:]}")
# f"last 10 loss mask: {batch['loss_mask'][:, -10:]}"
print_rank_0(f"first 1000 tokens")
for pt in range(10):
print_rank_0(f"{batch['input_ids'][:, 10 * pt:10 * pt + 10]}")
# print_rank_0(f"{batch['loss_mask'][:, 10 * pt:10 * pt + 10]}")
# self.global_steps += 1
skipped = False
self.model.train()
step_start = time.time()
if steps_trained_in_current_epoch and step < steps_trained_in_current_epoch:
continue
steps_trained_in_current_epoch = 0 # 恢复到上一次的steps in current epoch后,需要置零,否则后面的每个epoch都会跳过前面的steps
# batch = self.prepare_input(batch, self.device)
outputs = self.model(
input_ids=batch['input_ids'].to(self.device),
attention_mask=batch['attention_mask'].to(self.device),
position_ids=batch['position_ids'].to(self.device),
# labels=batch['labels'],
)
# self paced loss
# ema_tmp: L_valid_ema at step t-1
if (self.global_steps + 1) % self.args.selfpaced_interval == 0 and (self.args.weighted_loss_mode.startswith('famo_valid_ema') or self.args.weighted_loss_mode == 'selfpaced'):
ema_tmp = 0.001 * self.valid_task_loss_prev + (1 - 0.001) * self.ema_valid_task_loss_prev
self.ratio_valid_task_loss_prev = (ema_tmp - self.ema_valid_task_loss_prev ) / (self.ema_valid_task_loss_prev + 1e-6)
self.ema_valid_task_loss_prev = ema_tmp
self.ratio_valid_task_loss_prev = torch.where(self.ratio_valid_task_loss_prev > 0, -1e20, self.ratio_valid_task_loss_prev)
self.famo.ratio_valid_task_loss_prev = self.ratio_valid_task_loss_prev
if self.args.weighted_loss_mode == 'selfpaced':
self.selfpaced_status.update(self.global_steps + 1, epoch + 1, 'train', self.ratio_valid_task_loss_prev)
loss, task_loss, task_num, self.famo, self.selfpaced_status = self.loss_func(outputs, batch, self.args.weighted_loss_mode, self.famo, self.selfpaced_status)
# print(f'rank: {self.rank}, loss: {loss}, task loss: {task_loss}')
if self.args.weighted_loss_mode.startswith('famo'):
self.famo.print_loss = self.famo.print_loss / self.args.gradient_accumulation_steps
loss_tensor = torch.zeros(
[1], device=self.famo.print_loss.device, dtype=self.famo.print_loss.dtype)
loss_tensor[0] = self.famo.print_loss.item()
else:
loss = loss / self.args.gradient_accumulation_steps
loss_tensor = torch.zeros(
[1], device=loss.device, dtype=loss.dtype)
loss_tensor[0] = loss.item()
torch.distributed.all_reduce(loss_tensor)
torch.distributed.all_reduce(task_loss, op=torch.distributed.ReduceOp.SUM)
torch.distributed.all_reduce(task_num, op=torch.distributed.ReduceOp.SUM)
reduce_loss = loss_tensor.sum() / torch.distributed.get_world_size()
if has_inf_or_nan(reduce_loss):
print_rank_0(f'There have nan loss.')
self.skipped_steps += 1
skipped = True
else:
self.accumulated_loss += reduce_loss.item()
mean_task_loss = task_loss / torch.distributed.get_world_size()
self.accumulated_task_loss += mean_task_loss.cpu().numpy()
self.accumulated_task_num += task_num.cpu().numpy()
loss.backward()
self.global_steps += 1
self.famo.global_steps += 1
if step % self.args.gradient_accumulation_steps == 0 or step == len(self.train_dataloader) - 1:
if self.args.max_grad_norm is not None and self.args.max_grad_norm > 0:
# 如果是fp16，需要unscale。如果是bf16，self.optimizer里没有unscale这个方法
try:
self.optimizer.unscale_()
except Exception:
pass
if isinstance(self.model, FSDP):
self.model.clip_grad_norm_(self.args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), self.args.max_grad_norm)
self.optimizer.step()
overflow = hasattr(self.optimizer, "step_was_skipped") and self.optimizer.step_was_skipped
if skipped != overflow:
print(f'skipped != overflow!!!!!!!!!!!!!!!!')
if not overflow:
self.lr_scheduler.step()
# if not skipped:
# self.optimizer.step()
# self.lr_scheduler.step()
self.optimizer.zero_grad()
if (self.args.weighted_loss_mode.startswith('famo_valid') or self.args.weighted_loss_mode == 'selfpaced') and not skipped:
# if self.args.weighted_loss_mode.startswith('famo_valid') and not skipped and step % self.args.famo_interval == 0:
# delete caches
# gc.collect()
# torch.cuda.empty_cache()
# self.model.eval()
# if (step // self.args.famo_interval) % self.valid_dataloader_length == 0:
if step % self.valid_dataloader_length == 0:
valid_iterator = iter(self.valid_dataloader)
valid_step = step % self.valid_dataloader_length
if self.famo_resume:
self.famo_resume = False
for i in range(valid_step):
v_batch = next(valid_iterator)
v_batch = next(valid_iterator)
if self.args.weighted_loss_mode == 'selfpaced':
self.valid_task_loss_prev = self.self_paced_evaluate(self.global_steps, epoch + 1, v_batch, self.ratio_valid_task_loss_prev)
else:
with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
valid_outputs = self.model(
input_ids=v_batch['input_ids'].to(self.device),
attention_mask=v_batch['attention_mask'].to(self.device),
position_ids=v_batch['position_ids'].to(self.device)
)
valid_loss, valid_task_loss, valid_task_num, _, _ = self.loss_func(valid_outputs, v_batch, self.args.weighted_loss_mode)
if self.args.weighted_loss_mode.startswith('famo_valid_ema'):
torch.distributed.all_reduce(valid_task_loss, op=torch.distributed.ReduceOp.SUM)
valid_task_loss /= torch.distributed.get_world_size()
self.valid_task_loss_prev = valid_task_loss.clone().detach()
# if self.famo.first_valid_step and self.args.resume_from_checkpoint == 'true':
if self.args.resume_from_checkpoint == 'true':
self.ema_valid_task_loss_prev = self.valid_task_loss_prev
# self.scaler.scale(valid_loss).backward()
valid_loss.backward()
valid_loss_item = valid_loss.item()
if self.args.max_grad_norm is not None and self.args.max_grad_norm > 0:
# 如果是fp16，需要unscale。如果是bf16，self.optimizer里没有unscale这个方法
try:
self.optimizer.unscale_()
except Exception:
pass
if isinstance(self.model, FSDP):
self.model.clip_grad_norm_(self.args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), self.args.max_grad_norm)
# print_rank_0(f'valid_loss {valid_loss.item()}, task loss: {valid_task_loss}, valid step: {valid_step}')
self.famo.update(valid_task_loss)
self.optimizer.zero_grad()
step_time = time.time() - step_start | step_tflops = get_tflops_megatron(self.args.total_model_param, self.args.hidden_size, self.args.num_hidden_layers, | 0 | 2023-11-02 01:37:01+00:00 | 16k |
bytedance/cryostar | projects/star/train_density.py | [
{
"identifier": "StarfileDataSet",
"path": "cryostar/utils/dataio.py",
"snippet": "class StarfileDataSet(Dataset):\n\n def __init__(self, cfg: StarfileDatasetConfig):\n super().__init__()\n self.cfg = cfg\n self.df = starfile.read(Path(cfg.starfile_path))\n\n if \"optics\"... | import os
import os.path as osp
import einops
import lightning.pytorch as pl
import numpy as np
import torch
from lightning.pytorch.strategies import DDPStrategy
from lightning.pytorch.utilities import rank_zero_only
from torch.utils.data import DataLoader
from tqdm import tqdm
from mmengine import mkdir_or_exist
from cryostar.utils.dataio import StarfileDataSet, StarfileDatasetConfig
from cryostar.nerf.volume_utils import ImplicitFourierVolume
from cryostar.utils.transforms import SpatialGridTranslate, FourierGridTranslate
from cryostar.utils.ctf_utils import CTFRelion, CTFCryoDRGN
from cryostar.utils.fft_utils import (fourier_to_primal_2d, primal_to_fourier_2d)
from cryostar.utils.latent_space_utils import sample_along_pca, get_nearest_point, cluster_kmeans
from cryostar.utils.misc import (pl_init_exp, create_circular_mask, log_to_current, pretty_dict)
from cryostar.utils.losses import calc_kl_loss
from cryostar.utils.ml_modules import VAEEncoder, reparameterize
from cryostar.utils.mrc_tools import save_mrc
from miscs import infer_ctf_params_from_config | 10,998 |
log_to_current = rank_zero_only(log_to_current)
TASK_NAME = "density"
class CryoModel(pl.LightningModule):
def __init__(self, cfg, dataset):
super().__init__()
self.cfg = cfg
self.dataset = dataset
self.z_dim = cfg.model.z_dim
self.history_saved_dirs = []
if cfg.extra_input_data_attr.given_z is None and self.z_dim != 0:
if cfg.model.enc_space == "real":
self.encoder = VAEEncoder(self.cfg.data_process.down_side_shape**2,
cfg.model.hidden,
self.z_dim,
num_hidden_layers=4)
elif cfg.model.enc_space == "fourier":
self.encoder = VAEEncoder(2 * self.cfg.data_process.down_side_shape**2,
cfg.model.hidden,
self.z_dim,
num_hidden_layers=4)
else:
raise NotImplementedError
if cfg.model.shift_method == "interp":
self.translate = SpatialGridTranslate(self.cfg.data_process.down_side_shape, )
log_to_current("We will deprecate `model.shift_method=interp` in a future version, use `model.shift_method=fft` instead.")
elif cfg.model.shift_method == "fft":
self.f_translate = FourierGridTranslate(self.cfg.data_process.down_side_shape, )
else:
raise NotImplementedError
ctf_params = infer_ctf_params_from_config(cfg)
if cfg.model.ctf == "v1":
self.ctf = CTFRelion(**ctf_params, num_particles=len(dataset))
log_to_current("We will deprecate `model.ctf=v1` in a future version, use `model.ctf=v2` instead.")
elif cfg.model.ctf == "v2":
self.ctf = CTFCryoDRGN(**ctf_params, num_particles=len(dataset))
else:
raise NotImplementedError
log_to_current(ctf_params)
self.vol = ImplicitFourierVolume(
self.z_dim, self.cfg.data_process.down_side_shape, self.cfg.loss.mask_rad_for_image_loss, {
"net_type": cfg.model.net_type,
"pe_dim": self.cfg.data_process.down_side_shape,
"D": self.cfg.data_process.down_side_shape,
"pe_type": cfg.model.pe_type,
"force_symmetry": False,
"hidden": cfg.model.hidden,
})
mask = create_circular_mask(self.cfg.data_process.down_side_shape, self.cfg.data_process.down_side_shape, None,
self.cfg.data_process.down_side_shape // 2 * self.cfg.loss.mask_rad_for_image_loss,)
self.register_buffer("mask", torch.from_numpy(mask))
if cfg.extra_input_data_attr.given_z is not None:
self.register_buffer("given_z", torch.from_numpy(np.load(cfg.extra_input_data_attr.given_z)))
if getattr(self.cfg.extra_input_data_attr, "ckpt_path", None) is not None:
log_to_current(f"load checkpoint from {self.cfg.extra_input_data_attr.ckpt_path}")
state_dict = torch.load(self.cfg.extra_input_data_attr.ckpt_path, map_location=self.device)
self.vol.load_state_dict(state_dict)
def _get_save_dir(self):
save_dir = os.path.join(self.cfg.work_dir, f"{self.current_epoch:04d}_{self.global_step:07d}")
mkdir_or_exist(save_dir)
return save_dir
def process_image(self, batch):
R = batch["rotmat"]
bsz = len(R)
trans = torch.cat([
batch["shiftY"].float().reshape(bsz, 1, 1) / self.cfg.data_process.down_apix,
batch["shiftX"].float().reshape(bsz, 1, 1) / self.cfg.data_process.down_apix
],
dim=2)
proj_in = batch["proj"].to(self.device)
if self.cfg.model.shift_method == "interp":
proj = self.translate.transform(proj_in.squeeze(1), trans.to(self.device))
elif self.cfg.model.shift_method == "fft":
fproj = primal_to_fourier_2d(proj_in)
fproj = self.f_translate.transform(fproj.squeeze(1), trans.to(self.device))
proj = fourier_to_primal_2d(fproj)
if self.cfg.model.shift_data:
return proj, proj
else:
return proj_in, proj
def training_step(self, batch, batch_idx):
R = batch["rotmat"]
bsz = len(R)
proj_in, proj_out = self.process_image(batch)
f_proj_in = primal_to_fourier_2d(proj_in)
if self.z_dim != 0:
if self.cfg.extra_input_data_attr.given_z is not None:
z = self.given_z[batch["idx"]].reshape(bsz, -1)
kld_loss = 0.0
else:
if self.cfg.model.enc_space == "fourier":
enc_input = einops.rearrange(torch.view_as_real(f_proj_in), "b 1 ny nx c2 -> b (1 ny nx c2)", c2=2)
elif self.cfg.model.enc_space == "real":
enc_input = einops.rearrange(proj_in, "b 1 ny nx -> b (1 ny nx)")
mu, log_var = self.encoder(enc_input)
|
log_to_current = rank_zero_only(log_to_current)
TASK_NAME = "density"
class CryoModel(pl.LightningModule):
def __init__(self, cfg, dataset):
super().__init__()
self.cfg = cfg
self.dataset = dataset
self.z_dim = cfg.model.z_dim
self.history_saved_dirs = []
if cfg.extra_input_data_attr.given_z is None and self.z_dim != 0:
if cfg.model.enc_space == "real":
self.encoder = VAEEncoder(self.cfg.data_process.down_side_shape**2,
cfg.model.hidden,
self.z_dim,
num_hidden_layers=4)
elif cfg.model.enc_space == "fourier":
self.encoder = VAEEncoder(2 * self.cfg.data_process.down_side_shape**2,
cfg.model.hidden,
self.z_dim,
num_hidden_layers=4)
else:
raise NotImplementedError
if cfg.model.shift_method == "interp":
self.translate = SpatialGridTranslate(self.cfg.data_process.down_side_shape, )
log_to_current("We will deprecate `model.shift_method=interp` in a future version, use `model.shift_method=fft` instead.")
elif cfg.model.shift_method == "fft":
self.f_translate = FourierGridTranslate(self.cfg.data_process.down_side_shape, )
else:
raise NotImplementedError
ctf_params = infer_ctf_params_from_config(cfg)
if cfg.model.ctf == "v1":
self.ctf = CTFRelion(**ctf_params, num_particles=len(dataset))
log_to_current("We will deprecate `model.ctf=v1` in a future version, use `model.ctf=v2` instead.")
elif cfg.model.ctf == "v2":
self.ctf = CTFCryoDRGN(**ctf_params, num_particles=len(dataset))
else:
raise NotImplementedError
log_to_current(ctf_params)
self.vol = ImplicitFourierVolume(
self.z_dim, self.cfg.data_process.down_side_shape, self.cfg.loss.mask_rad_for_image_loss, {
"net_type": cfg.model.net_type,
"pe_dim": self.cfg.data_process.down_side_shape,
"D": self.cfg.data_process.down_side_shape,
"pe_type": cfg.model.pe_type,
"force_symmetry": False,
"hidden": cfg.model.hidden,
})
mask = create_circular_mask(self.cfg.data_process.down_side_shape, self.cfg.data_process.down_side_shape, None,
self.cfg.data_process.down_side_shape // 2 * self.cfg.loss.mask_rad_for_image_loss,)
self.register_buffer("mask", torch.from_numpy(mask))
if cfg.extra_input_data_attr.given_z is not None:
self.register_buffer("given_z", torch.from_numpy(np.load(cfg.extra_input_data_attr.given_z)))
if getattr(self.cfg.extra_input_data_attr, "ckpt_path", None) is not None:
log_to_current(f"load checkpoint from {self.cfg.extra_input_data_attr.ckpt_path}")
state_dict = torch.load(self.cfg.extra_input_data_attr.ckpt_path, map_location=self.device)
self.vol.load_state_dict(state_dict)
def _get_save_dir(self):
save_dir = os.path.join(self.cfg.work_dir, f"{self.current_epoch:04d}_{self.global_step:07d}")
mkdir_or_exist(save_dir)
return save_dir
def process_image(self, batch):
R = batch["rotmat"]
bsz = len(R)
trans = torch.cat([
batch["shiftY"].float().reshape(bsz, 1, 1) / self.cfg.data_process.down_apix,
batch["shiftX"].float().reshape(bsz, 1, 1) / self.cfg.data_process.down_apix
],
dim=2)
proj_in = batch["proj"].to(self.device)
if self.cfg.model.shift_method == "interp":
proj = self.translate.transform(proj_in.squeeze(1), trans.to(self.device))
elif self.cfg.model.shift_method == "fft":
fproj = primal_to_fourier_2d(proj_in)
fproj = self.f_translate.transform(fproj.squeeze(1), trans.to(self.device))
proj = fourier_to_primal_2d(fproj)
if self.cfg.model.shift_data:
return proj, proj
else:
return proj_in, proj
def training_step(self, batch, batch_idx):
R = batch["rotmat"]
bsz = len(R)
proj_in, proj_out = self.process_image(batch)
f_proj_in = primal_to_fourier_2d(proj_in)
if self.z_dim != 0:
if self.cfg.extra_input_data_attr.given_z is not None:
z = self.given_z[batch["idx"]].reshape(bsz, -1)
kld_loss = 0.0
else:
if self.cfg.model.enc_space == "fourier":
enc_input = einops.rearrange(torch.view_as_real(f_proj_in), "b 1 ny nx c2 -> b (1 ny nx c2)", c2=2)
elif self.cfg.model.enc_space == "real":
enc_input = einops.rearrange(proj_in, "b 1 ny nx -> b (1 ny nx)")
mu, log_var = self.encoder(enc_input) | z = reparameterize(mu, log_var) | 15 | 2023-11-06 07:15:26+00:00 | 16k |
UMass-Foundation-Model/CoVLM | transformers/src/transformers/models/sew/configuration_sew.py | [
{
"identifier": "PretrainedConfig",
"path": "transformers/src/transformers/configuration_utils.py",
"snippet": "class PretrainedConfig(PushToHubMixin):\n r\"\"\"\n Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as\n methods for lo... | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging | 12,428 | # coding=utf-8
# Copyright 2021 ASAPP Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" SEW model configuration"""
logger = logging.get_logger(__name__)
SEW_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See all SEW models at https://huggingface.co/models?filter=sew
}
| # coding=utf-8
# Copyright 2021 ASAPP Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" SEW model configuration"""
logger = logging.get_logger(__name__)
SEW_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See all SEW models at https://huggingface.co/models?filter=sew
}
| class SEWConfig(PretrainedConfig): | 0 | 2023-11-07 04:23:57+00:00 | 16k |
HKU-BAL/ClairS-TO | src/realign_reads.py | [
{
"identifier": "subprocess_popen",
"path": "shared/utils.py",
"snippet": "BASIC_BASES = set(\"ACGTU\")\nWARNING = '\\033[93m'\nERROR = '\\033[91m'\nENDC = '\\033[0m'\ndef log_error(log):\ndef log_warning(log):\ndef is_file_exists(file_name, suffix=\"\"):\ndef is_folder_exists(folder_name, suffix=\"\"):... | import sys
import os
import shlex
import ctypes
import re
import subprocess
import shared.param as param
from subprocess import PIPE
from argparse import ArgumentParser, SUPPRESS
from collections import defaultdict
from shared.utils import subprocess_popen, reference_sequence_from, IUPAC_base_to_ACGT_base_dict as BASE2ACGT, log_error
from shared.interval_tree import bed_tree_from
from shared.intervaltree.intervaltree import IntervalTree | 14,016 | start = reference_position
end = reference_position + advance
for ins_idx in range(start, end):
pileup[ins_idx]["X"] += 1
# deletion consumes reference
reference_position += advance
# reset advance
advance = 0
yield chunk_start, chunk_end
yield None, None
def reads_realignment(args):
POS = args.pos
args.ctg_start = POS - args.realign_flanking_window
args.ctg_end = POS + args.realign_flanking_window
bed_file_path = args.bed_fn
extend_bed = args.extend_bed
fasta_file_path = args.ref_fn
ctg_name = args.ctg_name
ctg_start = args.ctg_start
ctg_end = args.ctg_end
samtools_execute_command = args.samtools
bam_file_path = args.bam_fn
min_mq = args.min_mq
min_coverage = args.min_coverage
is_bed_file_given = bed_file_path is not None
is_ctg_name_given = ctg_name is not None
read_fn = args.read_fn
global test_pos
test_pos = None
is_ctg_range_given = is_ctg_name_given and ctg_start is not None and ctg_end is not None
ref_regions = []
reads_regions = []
reference_start, reference_end = None, None
if is_ctg_range_given:
extend_start = ctg_start - max_window_size
extend_end = ctg_end + max_window_size
reads_regions.append(region_from(ctg_name=ctg_name, ctg_start=extend_start, ctg_end=extend_end))
reference_start, reference_end = ctg_start - expandReferenceRegion, ctg_end + expandReferenceRegion
reference_start = 1 if reference_start < 1 else reference_start
ref_regions.append(region_from(ctg_name=ctg_name, ctg_start=reference_start, ctg_end=reference_end))
elif is_ctg_name_given:
reads_regions.append(region_from(ctg_name=ctg_name))
ref_regions.append(region_from(ctg_name=ctg_name))
reference_start = 1
reference_sequence = reference_sequence_from(
samtools_execute_command=samtools_execute_command,
fasta_file_path=fasta_file_path,
regions=ref_regions
)
if reference_sequence is None or len(reference_sequence) == 0:
sys.exit("[ERROR] Failed to load reference sequence from file ({}).".format(fasta_file_path))
tree = bed_tree_from(bed_file_path=bed_file_path)
if is_bed_file_given and ctg_name not in tree:
sys.exit("[ERROR] ctg_name({}) not exists in bed file({}).".format(ctg_name, bed_file_path))
bed_option = ' -L {}'.format(extend_bed) if extend_bed else ""
bed_option = ' -L {}'.format(bed_file_path) if is_bed_file_given else bed_option
mq_option = ' -q {}'.format(min_mq) if min_mq > 0 else ""
samtools_view_command = "{} view -h {} {}".format(samtools_execute_command, bam_file_path,
" ".join(reads_regions)) + mq_option + bed_option
samtools_view_process = subprocess_popen(
shlex.split(samtools_view_command)
)
if read_fn and read_fn == 'PIPE':
save_file_fp = TensorStdout(sys.stdout)
elif read_fn:
save_file_fp = subprocess_popen(shlex.split("{} view -bh - -o {}".format(samtools_execute_command, read_fn + (
'.{}_{}'.format(ctg_start, ctg_end) if is_ctg_range_given and not test_pos else ""))), stdin=PIPE,
stdout=PIPE)
reference_start_0_based = 0 if reference_start is None else (reference_start - 1)
header = []
add_header = False
aligned_reads = defaultdict()
pileup = defaultdict(lambda: {"X": 0})
samtools_view_generator = samtools_view_generator_from(samtools_view_process=samtools_view_process,
aligned_reads=aligned_reads,
pileup=pileup,
ctg_name=ctg_name,
reference_sequence=reference_sequence,
reference_start_0_based=reference_start_0_based,
header=header,
center_pos=POS)
pre_aligned_reads = defaultdict()
while True:
chunk_start, chunk_end = next(samtools_view_generator)
if chunk_start is None:
break
if not add_header:
save_file_fp.stdin.write(''.join(header))
add_header = True
variant_allele_list = [[position, pileup[position]["X"]] for position in list(pileup.keys())]
candidate_position_list = [(position, support_allele_count) for position, support_allele_count in
variant_allele_list if
support_allele_count >= min_coverage and position >= chunk_start - region_expansion_in_bp - 1 and position <= chunk_end + region_expansion_in_bp - 1]
candidate_position_list.sort(key=(lambda x: x[0]))
candidate_position_list = [item for item in candidate_position_list if
item[0] >= POS - args.max_distance and item[0] < POS + args.max_distance]
if not len(aligned_reads) or not len(candidate_position_list):
continue
if len(pre_aligned_reads): # update the read in previous chunk
for read_name, read in pre_aligned_reads.items():
aligned_reads[read_name] = read
region_dict = {}
split_region_size = max_window_size
| # BSD 3-Clause License
#
# Copyright 2023 The University of Hong Kong, Department of Computer Science
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
realign_chunk_size = 5000
min_dbg_mapping_quality = min_dbg_base_quality = 20
region_expansion_in_bp = expand_align_ref_region = 20
min_windows_distance = expand_align_ref_region * 4
max_window_size = max_region_reads_num = 1000
expandReferenceRegion = 100000
realigner_mod = os.path.join(*(os.path.split(__file__)[:-1] + ('realign/realigner',)))
dbg_mod = os.path.join(*(os.path.split(__file__)[:-1] + ('realign/debruijn_graph',)))
if not os.path.exists(realigner_mod) or not os.path.exists(dbg_mod):
# try to find modules in clair3
python_path = subprocess.run('which python', stdout=subprocess.PIPE, shell=True).stdout.decode().rstrip()
conda_prefix = os.path.dirname(os.path.dirname(python_path))
clair3_realign_path = os.path.join(conda_prefix, 'bin', 'preprocess', 'realign')
clair3_realigner_mod = os.path.join(clair3_realign_path, 'realigner')
clair3_dbg_mod = os.path.join(clair3_realign_path, 'debruijn_graph')
if os.path.exists(clair3_realigner_mod) and os.path.exists(clair3_dbg_mod):
realigner_mod = clair3_realigner_mod
dbg_mod = clair3_dbg_mod
else:
print(log_error("[ERROR] `realigner` or `debruijn_graph` submodule not found in conda environment, pls install clair3-illumina package!"))
sys.exit(1)
realigner = ctypes.cdll.LoadLibrary(realigner_mod)
dbg = ctypes.cdll.LoadLibrary(dbg_mod)
class StructPointer(ctypes.Structure):
_fields_ = [("position", ctypes.c_int * max_region_reads_num),
("cigar_string", ctypes.c_char_p * max_region_reads_num),
]
class DBGPointer(ctypes.Structure):
_fields_ = [("consensus_size", ctypes.c_int),
("consensus", ctypes.c_char_p * 200),
]
# Read class for storing read information
cigar_indel_re = r"(\d+)(D)"
cigarRe = r"(\d+)([MIDNSHP=X])"
graph_min_mapping_quality = 14
def get_len(seq, cigar):
if 'D' not in cigar:
return len(seq)
indel_length = 0
for m in re.finditer(cigar_indel_re, cigar):
indel_length += int(m.group(1))
return len(seq) + indel_length
def print_ed(s1, s2):
match_str = ""
for x, y in zip(s1, s2):
if x == y:
match_str += "|"
else:
match_str += " "
print(s1)
print(match_str)
print(s2)
class Read(object):
def __init__(self, read_start, seq, cigar, mapping_quality, base_quality, strand, raw_base_quality=None,
unalign=False, read_name=None, read_id=None, flag=None, RNEXT=0, PNEXT=0, TLEN=0, phasing=None):
self.read_start = read_start
self.cigar = cigar
self.mapping_quality = mapping_quality
self.seq = seq
self.base_quality = base_quality
self.read_id = read_id
self.read_end = self.read_start + get_len(seq, cigar)
self.strand = strand
self.graph_mq = True if self.mapping_quality >= graph_min_mapping_quality else False
self.raw_base_quality = raw_base_quality
self.read_name = read_name
self.region = {}
self.region_cigar = None
self.region_start = None
self.flag = str(flag)
self.RNEXT = RNEXT
self.PNEXT = PNEXT
self.TLEN = PNEXT
self.test_pos = None
self.best_cigar = cigar
self.best_pos = read_start
self.best_align_score = None
self.phasing = phasing
def set_realign_flag(self):
self.unalign = True
def count_align_score(self, cigar):
score = 0
for m in re.finditer(cigarRe, cigar):
l, op, = int(m.group(1)), m.group(2)
if op in 'MX=S':
continue
elif op in 'ID':
score += l
return score
def set_realignment_info(self, region_start, realignment_cigar, realignment_start):
realignment_cigar = realignment_cigar.replace('X', 'M')
if realignment_cigar == self.cigar and realignment_start == self.read_start:
return
if self.best_align_score and realignment_cigar == self.best_cigar and realignment_start == self.best_pos:
return
realignment_align_score = self.count_align_score(realignment_cigar)
if not self.best_align_score or realignment_align_score >= self.best_align_score:
self.best_cigar = realignment_cigar
self.best_pos = realignment_start
self.best_align_score = realignment_align_score
def decode_region(self, region_str):
if region_str == '-' or '-' not in region_str:
return
region_str = region_str.rstrip().split('_')
for region in region_str:
region, cigar, pos = region.split('-')
region, pos = int(region), int(pos)
self.region[region] = [cigar, pos]
def byte(x):
return bytes(x, encoding="utf8")
def find_max_overlap_index(query_region, search_regions):
def overlap_length(region1, region2):
return max(0, (min(region1[1], region2[1]) - max(region1[0], region2[0])))
overlap_lengths = [overlap_length(query_region, search_region) for search_region in search_regions]
argmax = max(range(len(search_regions)), key=lambda idx: overlap_lengths[idx])
return None if overlap_lengths[argmax] == 0 else argmax
def get_reference_seq(sequence, start, end, reference_start_0_based):
if end < start:
end, start = start, end
return sequence[start - reference_start_0_based: end - reference_start_0_based]
def phredscore2raw_score(qual):
return ord(qual) - 33
def evc_base_from(base):
return base if base == "N" else BASE2ACGT[base]
def region_from(ctg_name, ctg_start=None, ctg_end=None):
"""
1-based region string [start, end]
"""
if ctg_name is None:
return ""
if (ctg_start is None) != (ctg_end is None):
return ""
if ctg_start is None and ctg_end is None:
return "{}".format(ctg_name)
return "{}:{}-{}".format(ctg_name, ctg_start, ctg_end)
class TensorStdout(object):
def __init__(self, handle):
self.stdin = handle
def __del__(self):
self.stdin.close()
def get_halpotype_tag(samtools_view_columns):
found_hp_tag = False
tag = [c for c in samtools_view_columns if 'HP:i:' in c]
if not len(tag) or len(tag[0]) < 6 or not tag[0][5].isdigit():
return None
return tag[0][5]
def is_too_many_soft_clipped_bases_for_a_read_from(CIGAR):
soft_clipped_bases = 0
total_alignment_positions = 0
advance = 0
for c in str(CIGAR):
if c.isdigit():
advance = advance * 10 + int(c)
continue
if c == "S":
soft_clipped_bases += advance
total_alignment_positions += advance
advance = 0
# skip a read less than 55% aligned
return 1.0 - float(soft_clipped_bases) / (total_alignment_positions + 1) < 0.55
def samtools_view_generator_from(samtools_view_process, aligned_reads, pileup, ctg_name, reference_sequence,
reference_start_0_based, header, center_pos=None):
CHUNK_SIZE = realign_chunk_size
chunk_start, chunk_end = None, None
for row_id, row in enumerate(samtools_view_process.stdout):
if row[0] == '@':
header.append(row)
continue
columns = row.strip().split()
RNAME = columns[2]
if RNAME != ctg_name:
continue
read_name = columns[0]
FLAG = int(columns[1])
POS = int(columns[3]) - 1 # switch from 1-base to 0-base to match sequence index
MAPQ = int(columns[4])
CIGAR = columns[5]
SEQ = columns[9].upper() # uppercase for SEQ (regexp is \*|[A-Za-z=.]+)
RNEXT = columns[6]
PNEXT = columns[7]
TLEN = columns[8]
reference_position = POS
query_position = 0
raw_base_quality = columns[10]
QUAL = [phredscore2raw_score(item) for item in raw_base_quality]
STRAND = (16 == (FLAG & 16))
HP_TAG = get_halpotype_tag(columns[11:])
read_name += "_" + str(int(STRAND)) # distinguish two strand
if chunk_start is None:
chunk_start = POS
chunk_end = chunk_start + CHUNK_SIZE
if POS >= chunk_end + region_expansion_in_bp:
yield chunk_start, chunk_end
chunk_start += CHUNK_SIZE
chunk_end += CHUNK_SIZE
read = Read(read_start=POS,
seq=SEQ,
cigar=CIGAR,
mapping_quality=MAPQ,
base_quality=QUAL,
strand=STRAND,
raw_base_quality=raw_base_quality,
read_name=read_name,
flag=FLAG,
PNEXT=PNEXT,
RNEXT=RNEXT,
TLEN=TLEN,
phasing=HP_TAG)
if CIGAR == "*" or is_too_many_soft_clipped_bases_for_a_read_from(CIGAR):
continue
aligned_reads[read_name] = read
if MAPQ < min_dbg_mapping_quality:
continue
advance = 0
for c in str(CIGAR):
if c.isdigit():
advance = advance * 10 + int(c)
continue
if c == '=':
reference_position += advance
query_position += advance
elif c == "M" or c == 'X':
for _ in range(advance):
if QUAL[query_position] >= min_dbg_base_quality:
reference_base = reference_sequence[reference_position - reference_start_0_based] # 0 base
query_base = SEQ[query_position]
if reference_base in 'ACGT' and query_base != reference_base:
pileup[reference_position]['X'] += 1
reference_position += 1
query_position += 1
elif c == "I" or c == 'S':
pre_base = reference_sequence[reference_position - reference_start_0_based - 1]
ins_base_quality = QUAL[query_position: query_position + advance]
out_of_region = reference_position < chunk_start - region_expansion_in_bp or reference_position > chunk_end + region_expansion_in_bp
if not out_of_region and pre_base in 'ACGT' and (
sum([True for bq in ins_base_quality if bq < min_dbg_base_quality]) == 0):
# skip the bad seq
start = reference_position - advance
end = reference_position + advance
for ins_idx in range(start, end):
pileup[ins_idx]["X"] += 1
# insertion consumes query
query_position += advance
elif c == "D":
out_of_region = reference_position < chunk_start - region_expansion_in_bp or reference_position > chunk_end + region_expansion_in_bp
pre_base = reference_sequence[reference_position - reference_start_0_based - 1] # 0-base
if not out_of_region and pre_base in 'ACGT':
start = reference_position
end = reference_position + advance
for ins_idx in range(start, end):
pileup[ins_idx]["X"] += 1
# deletion consumes reference
reference_position += advance
# reset advance
advance = 0
yield chunk_start, chunk_end
yield None, None
def reads_realignment(args):
POS = args.pos
args.ctg_start = POS - args.realign_flanking_window
args.ctg_end = POS + args.realign_flanking_window
bed_file_path = args.bed_fn
extend_bed = args.extend_bed
fasta_file_path = args.ref_fn
ctg_name = args.ctg_name
ctg_start = args.ctg_start
ctg_end = args.ctg_end
samtools_execute_command = args.samtools
bam_file_path = args.bam_fn
min_mq = args.min_mq
min_coverage = args.min_coverage
is_bed_file_given = bed_file_path is not None
is_ctg_name_given = ctg_name is not None
read_fn = args.read_fn
global test_pos
test_pos = None
is_ctg_range_given = is_ctg_name_given and ctg_start is not None and ctg_end is not None
ref_regions = []
reads_regions = []
reference_start, reference_end = None, None
if is_ctg_range_given:
extend_start = ctg_start - max_window_size
extend_end = ctg_end + max_window_size
reads_regions.append(region_from(ctg_name=ctg_name, ctg_start=extend_start, ctg_end=extend_end))
reference_start, reference_end = ctg_start - expandReferenceRegion, ctg_end + expandReferenceRegion
reference_start = 1 if reference_start < 1 else reference_start
ref_regions.append(region_from(ctg_name=ctg_name, ctg_start=reference_start, ctg_end=reference_end))
elif is_ctg_name_given:
reads_regions.append(region_from(ctg_name=ctg_name))
ref_regions.append(region_from(ctg_name=ctg_name))
reference_start = 1
reference_sequence = reference_sequence_from(
samtools_execute_command=samtools_execute_command,
fasta_file_path=fasta_file_path,
regions=ref_regions
)
if reference_sequence is None or len(reference_sequence) == 0:
sys.exit("[ERROR] Failed to load reference sequence from file ({}).".format(fasta_file_path))
tree = bed_tree_from(bed_file_path=bed_file_path)
if is_bed_file_given and ctg_name not in tree:
sys.exit("[ERROR] ctg_name({}) not exists in bed file({}).".format(ctg_name, bed_file_path))
bed_option = ' -L {}'.format(extend_bed) if extend_bed else ""
bed_option = ' -L {}'.format(bed_file_path) if is_bed_file_given else bed_option
mq_option = ' -q {}'.format(min_mq) if min_mq > 0 else ""
samtools_view_command = "{} view -h {} {}".format(samtools_execute_command, bam_file_path,
" ".join(reads_regions)) + mq_option + bed_option
samtools_view_process = subprocess_popen(
shlex.split(samtools_view_command)
)
if read_fn and read_fn == 'PIPE':
save_file_fp = TensorStdout(sys.stdout)
elif read_fn:
save_file_fp = subprocess_popen(shlex.split("{} view -bh - -o {}".format(samtools_execute_command, read_fn + (
'.{}_{}'.format(ctg_start, ctg_end) if is_ctg_range_given and not test_pos else ""))), stdin=PIPE,
stdout=PIPE)
reference_start_0_based = 0 if reference_start is None else (reference_start - 1)
header = []
add_header = False
aligned_reads = defaultdict()
pileup = defaultdict(lambda: {"X": 0})
samtools_view_generator = samtools_view_generator_from(samtools_view_process=samtools_view_process,
aligned_reads=aligned_reads,
pileup=pileup,
ctg_name=ctg_name,
reference_sequence=reference_sequence,
reference_start_0_based=reference_start_0_based,
header=header,
center_pos=POS)
pre_aligned_reads = defaultdict()
while True:
chunk_start, chunk_end = next(samtools_view_generator)
if chunk_start is None:
break
if not add_header:
save_file_fp.stdin.write(''.join(header))
add_header = True
variant_allele_list = [[position, pileup[position]["X"]] for position in list(pileup.keys())]
candidate_position_list = [(position, support_allele_count) for position, support_allele_count in
variant_allele_list if
support_allele_count >= min_coverage and position >= chunk_start - region_expansion_in_bp - 1 and position <= chunk_end + region_expansion_in_bp - 1]
candidate_position_list.sort(key=(lambda x: x[0]))
candidate_position_list = [item for item in candidate_position_list if
item[0] >= POS - args.max_distance and item[0] < POS + args.max_distance]
if not len(aligned_reads) or not len(candidate_position_list):
continue
if len(pre_aligned_reads): # update the read in previous chunk
for read_name, read in pre_aligned_reads.items():
aligned_reads[read_name] = read
region_dict = {}
split_region_size = max_window_size | region_tree = IntervalTree() | 2 | 2023-11-07 04:39:16+00:00 | 16k |
the-siesta-group/edfio | tests/test_edfplus_header.py | [
{
"identifier": "AnonymizedDateError",
"path": "edfio/edf.py",
"snippet": "class AnonymizedDateError(ValueError):\n \"\"\"Raised when trying to access an anonymized startdate or birthdate.\"\"\""
},
{
"identifier": "Edf",
"path": "edfio/edf.py",
"snippet": "class Edf:\n \"\"\"Pytho... | import datetime
import numpy as np
import pytest
from edfio import AnonymizedDateError, Edf, EdfSignal, Patient, Recording | 13,683 |
@pytest.fixture()
def patient():
return Patient._from_str("MCH-0234567 F 02-MAY-1951 Haagse_Harry")
@pytest.fixture()
def recording():
return Recording._from_str(
"Startdate 02-MAR-2002 EMG561 BK/JOP Sony. MNC R Median Nerve."
)
@pytest.fixture()
def edf(patient, recording):
|
@pytest.fixture()
def patient():
return Patient._from_str("MCH-0234567 F 02-MAY-1951 Haagse_Harry")
@pytest.fixture()
def recording():
return Recording._from_str(
"Startdate 02-MAR-2002 EMG561 BK/JOP Sony. MNC R Median Nerve."
)
@pytest.fixture()
def edf(patient, recording): | return Edf([EdfSignal(np.arange(10), 1)], patient=patient, recording=recording) | 1 | 2023-11-09 09:53:27+00:00 | 16k |
sb-ai-lab/HypEx | hypex/matcher.py | [
{
"identifier": "FaissMatcher",
"path": "hypex/algorithms/faiss_matcher.py",
"snippet": "class FaissMatcher:\n \"\"\"A class used to match instances using Faiss library.\"\"\"\n\n def __init__(\n self,\n df: pd.DataFrame,\n outcomes: str,\n treatment: st... | import logging
import pickle
import numpy as np
import pandas as pd
from typing import Union
from tqdm.auto import tqdm
from .algorithms.faiss_matcher import FaissMatcher
from .algorithms.no_replacement_matching import MatcherNoReplacement
from .selectors.feature_selector import FeatureSelector
from .selectors.spearman_filter import SpearmanFilter
from .selectors.outliers_filter import OutliersFilter
from .selectors.base_filtration import const_filtration, nan_filtration
from .utils.validators import random_feature
from .utils.validators import random_treatment
from .utils.validators import subset_refuter
from .utils.validators import test_significance | 12,387 | info_col:
Columns with id, date or metadata, not taking part in calculations. Defaults to None
weights:
weights for numeric columns in order to increase matching quality by weighted feature.
By default, is None (all features have the same weight equal to 1). Example: {'feature_1': 10}
base_filtration:
To use or not base filtration of features in order to remove all constant or almost all constant, bool.
Default is False.
generate_report:
Flag to create report. Defaults to True
report_feat_select_dir:
Folder for report files. Defaults to "report_feature_selector"
timeout:
Limit work time of code LAMA. Defaults to 600
n_threads:
Maximum number of threads. Defaults to 1
n_folds:
Number of folds for cross-validation. Defaults to 4
verbose:
Flag to show process stages. Defaults to 2
use_algos:
List of names of LAMA algorithms for feature selection. Defaults to ["lgb"]
same_target_threshold:
Threshold for correlation coefficient filter (Spearman). Default to 0.7
interquartile_coeff:
Percent for drop outliers. Default to 1.5
drop_outliers_by_percentile:
Flag to drop outliers by custom percentiles. Defaults to True
min_percentile:
Minimum percentile to drop outliers. Defaults to 0.02
max_percentile:
Maximum percentile to drop outliers. Defaults to 0.98
n_neighbors:
Number of neighbors to match (in fact you may see more then n matches as every match may have more then
one neighbor with the same distance). Default value is 1.
silent:
Write logs in debug mode
pbar:
Display progress bar while get index
"""
if use_algos is None:
use_algos = USE_ALGOS
self.input_data = input_data
if outcome is None:
outcome = list()
self.outcomes = outcome if type(outcome) == list else [outcome]
self.treatment = treatment
self.group_col = group_col
self.info_col = info_col
self.outcome_type = outcome_type
self.weights = weights
self.generate_report = generate_report
self.report_feat_select_dir = report_feat_select_dir
self.timeout = timeout
self.n_threads = n_threads
self.n_folds = n_folds
self.verbose = verbose
self.use_algos = use_algos
self.same_target_threshold = same_target_threshold
self.interquartile_coeff = interquartile_coeff
self.mode_percentile = drop_outliers_by_percentile
self.min_percentile = min_percentile
self.max_percentile = max_percentile
self.base_filtration = base_filtration
self.features_importance = None
self.matcher = None
self.val_dict = None
self.pval_dict = None
self.new_treatment = None
self.validate = None
self.dropped_features = []
self.n_neighbors = n_neighbors
self.silent = silent
self.pbar = pbar
self._preprocessing_data()
def _convert_categorical_to_dummy(self):
"""Converts categorical variables to dummy variables.
Returns:
Data with categorical variables converted to dummy variables.
"""
info_col = self.info_col if self.info_col is not None else []
group_col = [self.group_col] if self.group_col is not None else []
columns_to_drop = info_col + group_col
if columns_to_drop is not None:
data = self.input_data.drop(columns=columns_to_drop)
else:
data = self.input_data
dummy_data = pd.get_dummies(data, drop_first=True, dtype=np.uint8)
return dummy_data
def _preprocessing_data(self):
"""Converts categorical features into dummy variables."""
info_col = self.info_col if self.info_col is not None else []
group_col = [self.group_col] if self.group_col is not None else []
columns_to_drop = info_col + group_col + self.outcomes + [self.treatment]
if self.base_filtration:
filtered_features = nan_filtration(self.input_data.drop(columns=columns_to_drop))
self.dropped_features = [f for f in self.input_data.columns if f not in filtered_features + columns_to_drop]
self.input_data = self.input_data[filtered_features + columns_to_drop]
nan_counts = self.input_data.isna().sum().sum()
if nan_counts != 0:
self._log(f"Number of NaN values filled with zeros: {nan_counts}", silent=False)
self.input_data = self.input_data.fillna(0)
if self.group_col is not None:
group_col = self.input_data[[self.group_col]]
if self.info_col is not None:
info_col = self.input_data[self.info_col]
self.input_data = self._convert_categorical_to_dummy()
if self.group_col is not None:
self.input_data = pd.concat([self.input_data, group_col], axis=1)
if self.info_col is not None:
self.input_data = pd.concat([self.input_data, info_col], axis=1)
if self.base_filtration:
| """Base Matcher class."""
REPORT_FEAT_SELECT_DIR = "report_feature_selector"
REPORT_PROP_MATCHER_DIR = "report_matcher"
NAME_REPORT = "lama_interactive_report.html"
N_THREADS = 1
N_FOLDS = 4
RANDOM_STATE = 123
TEST_SIZE = 0.2
TIMEOUT = 600
VERBOSE = 2
USE_ALGOS = ["lgb"]
PROP_SCORES_COLUMN = "prop_scores"
GENERATE_REPORT = True
SAME_TARGET_THRESHOLD = 0.7
OUT_INTER_COEFF = 1.5
OUT_MODE_PERCENT = True
OUT_MIN_PERCENT = 0.02
OUT_MAX_PERCENT = 0.98
logger = logging.getLogger("hypex")
console_out = logging.StreamHandler()
logging.basicConfig(
handlers=(console_out,),
format="[%(asctime)s | %(name)s | %(levelname)s]: %(message)s",
datefmt="%d.%m.%Y %H:%M:%S",
level=logging.INFO,
)
class Matcher:
"""Class for compile full pipeline of Matching in Causal Inference task.
Matcher steps:
- Read, analyze data
- Feature selection via LightAutoML
- Converting a dataset with features to another space via Cholesky decomposition
In the new space, the distance L2 becomes equivalent to the Mahalanobis distance.
This allows us to use faiss to search for nearest objects, which can search only by L2 metric,
but without violating the methodology of matching,
for which it is important to count by the Mahalanobis distance
- Finding the nearest neighbors for each unit (with duplicates) using faiss.
For each of the control group, neighbors from the target group are matched and vice versa.
- Calculation bias
- Creating matched df (Wide df with pairs)
- Calculation metrics: ATE, ATT, ATC, p-value, and сonfidence intervals
- Calculation quality: PS-test, KS test, SMD test
- Returns metrics as dataframe, quality results as dict of df's and df_matched
- After receiving the result, the result should be validated using :func:`~hypex.matcher.Matcher.validate_result`
Example:
Common usecase - base pipeline for matching
>>> # Base info
>>> treatment = "treatment" # Column name with info about 'treatment' 0 or 1
>>> target = "target" # Column name with target
>>>
>>> # Optional
>>> info_col = ["user_id", 'address'] # Columns that will not participate in the match and are informative.
>>> group_col = "CatCol" # Column name for strict comparison (for a categorical feature)
>>>
>>> # Matching
>>> model = Matcher(data, outcome=target, treatment=treatment, info_col=info_col, group_col=group_col)
>>> features = model.lama_feature_select() # Feature selection via lama
>>> results, quality, df_matched = model.estimate(features=some_features) # Performs matching
>>>
>>> model.validate_result()
"""
def __init__(
self,
input_data: pd.DataFrame,
treatment: str,
outcome: Union[str, list] = None,
outcome_type: str = "numeric",
group_col: str = None,
info_col: list = None,
weights: dict = None,
base_filtration: bool = False,
generate_report: bool = GENERATE_REPORT,
report_feat_select_dir: str = REPORT_FEAT_SELECT_DIR,
timeout: int = TIMEOUT,
n_threads: int = N_THREADS,
n_folds: int = N_FOLDS,
verbose: bool = VERBOSE,
use_algos: list = None,
same_target_threshold: float = SAME_TARGET_THRESHOLD,
interquartile_coeff: float = OUT_INTER_COEFF,
drop_outliers_by_percentile: bool = OUT_MODE_PERCENT,
min_percentile: float = OUT_MIN_PERCENT,
max_percentile: float = OUT_MAX_PERCENT,
n_neighbors: int = 1,
silent: bool = True,
pbar: bool = True,
):
"""Initialize the Matcher object.
Args:
input_data:
Input dataframe
outcome:
Target column
treatment:
Column determine control and test groups
outcome_type:
Values type of target column. Defaults to "numeric"
group_col:
Column for grouping. Defaults to None.
info_col:
Columns with id, date or metadata, not taking part in calculations. Defaults to None
weights:
weights for numeric columns in order to increase matching quality by weighted feature.
By default, is None (all features have the same weight equal to 1). Example: {'feature_1': 10}
base_filtration:
To use or not base filtration of features in order to remove all constant or almost all constant, bool.
Default is False.
generate_report:
Flag to create report. Defaults to True
report_feat_select_dir:
Folder for report files. Defaults to "report_feature_selector"
timeout:
Limit work time of code LAMA. Defaults to 600
n_threads:
Maximum number of threads. Defaults to 1
n_folds:
Number of folds for cross-validation. Defaults to 4
verbose:
Flag to show process stages. Defaults to 2
use_algos:
List of names of LAMA algorithms for feature selection. Defaults to ["lgb"]
same_target_threshold:
Threshold for correlation coefficient filter (Spearman). Default to 0.7
interquartile_coeff:
Percent for drop outliers. Default to 1.5
drop_outliers_by_percentile:
Flag to drop outliers by custom percentiles. Defaults to True
min_percentile:
Minimum percentile to drop outliers. Defaults to 0.02
max_percentile:
Maximum percentile to drop outliers. Defaults to 0.98
n_neighbors:
Number of neighbors to match (in fact you may see more then n matches as every match may have more then
one neighbor with the same distance). Default value is 1.
silent:
Write logs in debug mode
pbar:
Display progress bar while get index
"""
if use_algos is None:
use_algos = USE_ALGOS
self.input_data = input_data
if outcome is None:
outcome = list()
self.outcomes = outcome if type(outcome) == list else [outcome]
self.treatment = treatment
self.group_col = group_col
self.info_col = info_col
self.outcome_type = outcome_type
self.weights = weights
self.generate_report = generate_report
self.report_feat_select_dir = report_feat_select_dir
self.timeout = timeout
self.n_threads = n_threads
self.n_folds = n_folds
self.verbose = verbose
self.use_algos = use_algos
self.same_target_threshold = same_target_threshold
self.interquartile_coeff = interquartile_coeff
self.mode_percentile = drop_outliers_by_percentile
self.min_percentile = min_percentile
self.max_percentile = max_percentile
self.base_filtration = base_filtration
self.features_importance = None
self.matcher = None
self.val_dict = None
self.pval_dict = None
self.new_treatment = None
self.validate = None
self.dropped_features = []
self.n_neighbors = n_neighbors
self.silent = silent
self.pbar = pbar
self._preprocessing_data()
def _convert_categorical_to_dummy(self):
"""Converts categorical variables to dummy variables.
Returns:
Data with categorical variables converted to dummy variables.
"""
info_col = self.info_col if self.info_col is not None else []
group_col = [self.group_col] if self.group_col is not None else []
columns_to_drop = info_col + group_col
if columns_to_drop is not None:
data = self.input_data.drop(columns=columns_to_drop)
else:
data = self.input_data
dummy_data = pd.get_dummies(data, drop_first=True, dtype=np.uint8)
return dummy_data
def _preprocessing_data(self):
"""Converts categorical features into dummy variables."""
info_col = self.info_col if self.info_col is not None else []
group_col = [self.group_col] if self.group_col is not None else []
columns_to_drop = info_col + group_col + self.outcomes + [self.treatment]
if self.base_filtration:
filtered_features = nan_filtration(self.input_data.drop(columns=columns_to_drop))
self.dropped_features = [f for f in self.input_data.columns if f not in filtered_features + columns_to_drop]
self.input_data = self.input_data[filtered_features + columns_to_drop]
nan_counts = self.input_data.isna().sum().sum()
if nan_counts != 0:
self._log(f"Number of NaN values filled with zeros: {nan_counts}", silent=False)
self.input_data = self.input_data.fillna(0)
if self.group_col is not None:
group_col = self.input_data[[self.group_col]]
if self.info_col is not None:
info_col = self.input_data[self.info_col]
self.input_data = self._convert_categorical_to_dummy()
if self.group_col is not None:
self.input_data = pd.concat([self.input_data, group_col], axis=1)
if self.info_col is not None:
self.input_data = pd.concat([self.input_data, info_col], axis=1)
if self.base_filtration: | filtered_features = const_filtration(self.input_data.drop(columns=columns_to_drop)) | 5 | 2023-11-01 08:58:57+00:00 | 16k |
tianhaowuhz/human-assisting-dex-grasp | Runners/TrainGFPPO.py | [
{
"identifier": "GFPPO",
"path": "Algorithms/ppo/gf_ppo_update.py",
"snippet": "class GFPPO:\n def __init__(self,\n vec_env,\n cfg_train,\n device='cpu',\n sampler='sequential',\n log_dir='run',\n is_testi... | import isaacgym
import condexenvs
import torch
import os
import sys
from Algorithms.ppo import GFPPO
from utils.config import load_cfg, get_args, set_np_formatting | 11,134 | sys.path.append(os.path.dirname(os.path.dirname(__file__)))
if __name__ == '__main__':
set_np_formatting()
args = get_args()
cfg_train, logdir = load_cfg(args)
'''
change for different method
'''
cfg_train['setting']['grad_scale'] = 1.0
reward_normalize = False
cfg_train['policy']['pointnet_version'] = 'pt'
if args.exp_name == 'ours':
reward_type = "ori_similarity+height+sr"
reward_normalize = False
sub_obs_type = "joint+fingertipjoint+wrist+objpcl+gf"
cfg_train['setting']['action_type'] = "joint"
cfg_train['setting']['sub_action_type'] = "add+jointscale"
cfg_train['policy']['pretrain_pointnet'] = True
'''
policy
'''
cfg_train['policy']['hand_pcl'] = False
'''
training setting
'''
cfg_train['learn']['nsteps'] = 50
cfg_train['learn']['noptepochs'] = 2
cfg_train['learn']['nminibatches'] = int(args.num_envs*cfg_train['learn']['nsteps']/64)
if cfg_train['policy']['pointnet_version'] == 'pt':
cfg_train['learn']['optim_stepsize'] = 0.0003
else:
cfg_train['learn']['optim_stepsize'] = 0.0003
cfg_train['learn']['desired_kl'] = 0.016
cfg_train['learn']['gamma'] = 0.99
envs = condexenvs.make(
seed=args.seed,
task="ShadowHandCon",
num_envs=args.num_envs,
sim_device=f"cuda:{args.run_device_id}",
rl_device=f"cuda:{args.run_device_id}",
graphics_device_id = -1,
headless=args.headless,
mode = args.mode,
eval_times=args.eval_times,
method = args.method,
constrained = args.constrained,
reward_type = reward_type,
reward_normalize = reward_normalize,
sub_obs_type = sub_obs_type,
dataset_type = args.dataset_type,
)
envs.reset(env_init=True)
learn_cfg = cfg_train["learn"]
is_testing = learn_cfg["test"]
# Override resume and testing flags if they are passed as parameters.
if args.model_dir != "":
chkpt_path = args.model_dir
logdir = logdir + "_seed{}".format(args.seed)
| sys.path.append(os.path.dirname(os.path.dirname(__file__)))
if __name__ == '__main__':
set_np_formatting()
args = get_args()
cfg_train, logdir = load_cfg(args)
'''
change for different method
'''
cfg_train['setting']['grad_scale'] = 1.0
reward_normalize = False
cfg_train['policy']['pointnet_version'] = 'pt'
if args.exp_name == 'ours':
reward_type = "ori_similarity+height+sr"
reward_normalize = False
sub_obs_type = "joint+fingertipjoint+wrist+objpcl+gf"
cfg_train['setting']['action_type'] = "joint"
cfg_train['setting']['sub_action_type'] = "add+jointscale"
cfg_train['policy']['pretrain_pointnet'] = True
'''
policy
'''
cfg_train['policy']['hand_pcl'] = False
'''
training setting
'''
cfg_train['learn']['nsteps'] = 50
cfg_train['learn']['noptepochs'] = 2
cfg_train['learn']['nminibatches'] = int(args.num_envs*cfg_train['learn']['nsteps']/64)
if cfg_train['policy']['pointnet_version'] == 'pt':
cfg_train['learn']['optim_stepsize'] = 0.0003
else:
cfg_train['learn']['optim_stepsize'] = 0.0003
cfg_train['learn']['desired_kl'] = 0.016
cfg_train['learn']['gamma'] = 0.99
envs = condexenvs.make(
seed=args.seed,
task="ShadowHandCon",
num_envs=args.num_envs,
sim_device=f"cuda:{args.run_device_id}",
rl_device=f"cuda:{args.run_device_id}",
graphics_device_id = -1,
headless=args.headless,
mode = args.mode,
eval_times=args.eval_times,
method = args.method,
constrained = args.constrained,
reward_type = reward_type,
reward_normalize = reward_normalize,
sub_obs_type = sub_obs_type,
dataset_type = args.dataset_type,
)
envs.reset(env_init=True)
learn_cfg = cfg_train["learn"]
is_testing = learn_cfg["test"]
# Override resume and testing flags if they are passed as parameters.
if args.model_dir != "":
chkpt_path = args.model_dir
logdir = logdir + "_seed{}".format(args.seed)
| runner = GFPPO(vec_env=envs, | 0 | 2023-11-09 06:08:40+00:00 | 16k |
ApolloAuto/apollo-model-centerpoint | tools/create_bevformer_nus_infos.py | [
{
"identifier": "NuscenesMVDataset",
"path": "paddle3d/datasets/nuscenes/nuscenes_multiview_det.py",
"snippet": "class NuscenesMVDataset(NuscenesDetDataset):\n \"\"\"\n Nuscecens dataset for multi-view camera detection task.\n \"\"\"\n DATASET_NAME = \"Nuscenes\"\n\n def __init__(self,\n ... | import argparse
import os
import pickle
import numpy as np
import tqdm
from pathlib import Path
from nuscenes import NuScenes
from nuscenes.can_bus.can_bus_api import NuScenesCanBus
from nuscenes.utils import splits as nuscenes_split
from nuscenes.utils.data_classes import Box as NuScenesBox
from nuscenes.utils.geometry_utils import transform_matrix
from pyquaternion import Quaternion
from paddle3d.datasets.nuscenes import NuscenesMVDataset
from paddle3d.datasets.nuscenes.nuscenes_det import NuscenesDetDataset
from paddle3d.utils.logger import logger | 10,934 | rotation = last_pose.pop('orientation')
can_bus.extend(pos)
can_bus.extend(rotation)
for key in last_pose.keys():
can_bus.extend(pose[key]) # 16 elements
can_bus.extend([0., 0.])
return np.array(can_bus)
def fill_trainval_infos(nusc,
nusc_can_bus,
train_scenes,
val_scenes,
test=False,
max_sweeps=10):
"""Generate the train/val infos from the raw data.
"""
train_nusc_infos = []
val_nusc_infos = []
frame_idx = 0
msg = "Begin to generate a info of nuScenes dataset."
for sample_idx in logger.range(len(nusc.sample), msg=msg):
sample = nusc.sample[sample_idx]
lidar_token = sample['data']['LIDAR_TOP']
sd_rec = nusc.get('sample_data', sample['data']['LIDAR_TOP'])
cs_record = nusc.get('calibrated_sensor',
sd_rec['calibrated_sensor_token'])
pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token'])
lidar_path, boxes, _ = nusc.get_sample_data(lidar_token)
assert os.path.exists(lidar_path) # absolute path
can_bus = _get_can_bus_info(nusc, nusc_can_bus, sample)
info = {
'lidar_token': lidar_token,
'lidar_path': nusc.get('sample_data',
lidar_token)['filename'], # relative path
'token': sample['token'],
'prev': sample['prev'],
'next': sample['next'],
'can_bus': can_bus,
'frame_idx': frame_idx, # temporal related info
'sweeps': [],
'cams': dict(),
'scene_token': sample['scene_token'],
'lidar2ego_translation': cs_record['translation'],
'lidar2ego_rotation': cs_record['rotation'],
'ego2global_translation': pose_record['translation'],
'ego2global_rotation': pose_record['rotation'],
'timestamp': sample['timestamp'],
}
if sample['next'] == '':
frame_idx = 0
else:
frame_idx += 1
l2e_r = info['lidar2ego_rotation']
l2e_t = info['lidar2ego_translation']
e2g_r = info['ego2global_rotation']
e2g_t = info['ego2global_translation']
l2e_r_mat = Quaternion(l2e_r).rotation_matrix
e2g_r_mat = Quaternion(e2g_r).rotation_matrix
# obtain 6 image's information per frame
camera_types = [
'CAM_FRONT',
'CAM_FRONT_RIGHT',
'CAM_FRONT_LEFT',
'CAM_BACK',
'CAM_BACK_LEFT',
'CAM_BACK_RIGHT',
]
for cam in camera_types:
cam_token = sample['data'][cam]
cam_path, _, cam_intrinsic = nusc.get_sample_data(cam_token)
cam_info = obtain_sensor2top(nusc, cam_token, l2e_t, l2e_r_mat,
e2g_t, e2g_r_mat, cam)
cam_info.update(cam_intrinsic=cam_intrinsic)
info['cams'].update({cam: cam_info})
# obtain sweeps for a single key-frame
sd_rec = nusc.get('sample_data', sample['data']['LIDAR_TOP'])
sweeps = []
while len(sweeps) < max_sweeps:
if not sd_rec['prev'] == '':
sweep = obtain_sensor2top(nusc, sd_rec['prev'], l2e_t,
l2e_r_mat, e2g_t, e2g_r_mat, 'lidar')
sweeps.append(sweep)
sd_rec = nusc.get('sample_data', sd_rec['prev'])
else:
break
info['sweeps'] = sweeps
# obtain annotation
if not test:
annotations = [
nusc.get('sample_annotation', token) for token in sample['anns']
]
locs = np.array([b.center for b in boxes]).reshape(-1, 3)
dims = np.array([b.wlh for b in boxes]).reshape(-1, 3)
rots = np.array([b.orientation.yaw_pitch_roll[0]
for b in boxes]).reshape(-1, 1)
velocity = np.array(
[nusc.box_velocity(token)[:2] for token in sample['anns']])
valid_flag = np.array(
[(anno['num_lidar_pts'] + anno['num_radar_pts']) > 0
for anno in annotations],
dtype=bool).reshape(-1)
# convert velo from global to lidar
for i in range(len(boxes)):
velo = np.array([*velocity[i], 0.0])
velo = velo @ np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(
l2e_r_mat).T
velocity[i] = velo[:2]
names = [b.name for b in boxes]
for i in range(len(names)):
# NuscenesDetDataset.LABEL_MAP
| # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------
# Modify from https://github.com/fundamentalvision/BEVFormer/blob/master/tools/create_data.py
# Copyright (c) OpenMMLab. All rights reserved.
# ------------------------------------------------------------------------
# ------------------------------------------------------------------------
# Modified from mmdetection3d (https://github.com/open-mmlab/mmdetection3d)
# Copyright (c) OpenMMLab. All rights reserved.
# ------------------------------------------------------------------------
SENSORS = [
'CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_BACK_RIGHT', 'CAM_BACK',
'CAM_BACK_LEFT', 'CAM_FRONT_LEFT'
]
def parse_args():
parser = argparse.ArgumentParser(
description='Create infos for kitti dataset.')
parser.add_argument(
'--dataset_root',
default='data/nuscenes',
help='Path of the dataset.',
type=str)
parser.add_argument(
'--can_bus_root',
type=str,
default='data/nuscenes',
help='specify the root path of nuScenes canbus')
parser.add_argument(
'--save_dir',
default='data/nuscenes',
help='Path to save the generated database.',
type=str)
parser.add_argument(
'--mode', default='train', help='mode to generate dataset.', type=str)
parser.add_argument(
'--num_sweep',
default=10,
help='nummber of sweep frames between two key frame.',
type=int)
return parser.parse_args()
def is_filepath(x):
return isinstance(x, str) or isinstance(x, Path)
def get_available_scenes(nusc):
"""Get available scenes from the input nuscenes class.
"""
available_scenes = []
logger.info('total scene num: {}'.format(len(nusc.scene)))
for scene in nusc.scene:
scene_token = scene['token']
scene_rec = nusc.get('scene', scene_token)
sample_rec = nusc.get('sample', scene_rec['first_sample_token'])
sd_rec = nusc.get('sample_data', sample_rec['data']['LIDAR_TOP'])
has_more_frames = True
scene_not_exist = False
while has_more_frames:
lidar_path, boxes, _ = nusc.get_sample_data(sd_rec['token'])
lidar_path = str(lidar_path)
if os.getcwd() in lidar_path:
# path from lyftdataset is absolute path
lidar_path = lidar_path.split(f'{os.getcwd()}/')[-1]
# relative path
if not is_filepath(lidar_path):
scene_not_exist = True
break
else:
break
if scene_not_exist:
continue
available_scenes.append(scene)
logger.info('exist scene num: {}'.format(len(available_scenes)))
return available_scenes
def obtain_sensor2top(nusc,
sensor_token,
l2e_t,
l2e_r_mat,
e2g_t,
e2g_r_mat,
sensor_type='lidar'):
"""Obtain the info with RT matric from general sensor to Top LiDAR.
"""
sd_rec = nusc.get('sample_data', sensor_token)
cs_record = nusc.get('calibrated_sensor', sd_rec['calibrated_sensor_token'])
pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token'])
data_path = str(nusc.get_sample_data_path(sd_rec['token'])) # absolute path
if os.getcwd() in data_path: # path from lyftdataset is absolute path
data_path = data_path.split(f'{os.getcwd()}/')[-1] # relative path
sweep = {
'data_path': nusc.get('sample_data',
sd_rec['token'])['filename'], # relative path
'type': sensor_type,
'sample_data_token': sd_rec['token'],
'sensor2ego_translation': cs_record['translation'],
'sensor2ego_rotation': cs_record['rotation'],
'ego2global_translation': pose_record['translation'],
'ego2global_rotation': pose_record['rotation'],
'timestamp': sd_rec['timestamp']
}
l2e_r_s = sweep['sensor2ego_rotation']
l2e_t_s = sweep['sensor2ego_translation']
e2g_r_s = sweep['ego2global_rotation']
e2g_t_s = sweep['ego2global_translation']
# obtain the RT from sensor to Top LiDAR
# sweep->ego->global->ego'->lidar
l2e_r_s_mat = Quaternion(l2e_r_s).rotation_matrix
e2g_r_s_mat = Quaternion(e2g_r_s).rotation_matrix
R = (l2e_r_s_mat.T @ e2g_r_s_mat.T) @ (
np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T)
T = (l2e_t_s @ e2g_r_s_mat.T + e2g_t_s) @ (
np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T)
T -= e2g_t @ (np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T
) + l2e_t @ np.linalg.inv(l2e_r_mat).T
sweep['sensor2lidar_rotation'] = R.T # points @ R.T + T
sweep['sensor2lidar_translation'] = T
return sweep
def _get_can_bus_info(nusc, nusc_can_bus, sample):
scene_name = nusc.get('scene', sample['scene_token'])['name']
sample_timestamp = sample['timestamp']
try:
pose_list = nusc_can_bus.get_messages(scene_name, 'pose')
except:
return np.zeros(18) # server scenes do not have can bus information.
can_bus = []
# during each scene, the first timestamp of can_bus may be large than the first sample's timestamp
last_pose = pose_list[0]
for i, pose in enumerate(pose_list):
if pose['utime'] > sample_timestamp:
break
last_pose = pose
_ = last_pose.pop('utime') # useless
pos = last_pose.pop('pos')
rotation = last_pose.pop('orientation')
can_bus.extend(pos)
can_bus.extend(rotation)
for key in last_pose.keys():
can_bus.extend(pose[key]) # 16 elements
can_bus.extend([0., 0.])
return np.array(can_bus)
def fill_trainval_infos(nusc,
nusc_can_bus,
train_scenes,
val_scenes,
test=False,
max_sweeps=10):
"""Generate the train/val infos from the raw data.
"""
train_nusc_infos = []
val_nusc_infos = []
frame_idx = 0
msg = "Begin to generate a info of nuScenes dataset."
for sample_idx in logger.range(len(nusc.sample), msg=msg):
sample = nusc.sample[sample_idx]
lidar_token = sample['data']['LIDAR_TOP']
sd_rec = nusc.get('sample_data', sample['data']['LIDAR_TOP'])
cs_record = nusc.get('calibrated_sensor',
sd_rec['calibrated_sensor_token'])
pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token'])
lidar_path, boxes, _ = nusc.get_sample_data(lidar_token)
assert os.path.exists(lidar_path) # absolute path
can_bus = _get_can_bus_info(nusc, nusc_can_bus, sample)
info = {
'lidar_token': lidar_token,
'lidar_path': nusc.get('sample_data',
lidar_token)['filename'], # relative path
'token': sample['token'],
'prev': sample['prev'],
'next': sample['next'],
'can_bus': can_bus,
'frame_idx': frame_idx, # temporal related info
'sweeps': [],
'cams': dict(),
'scene_token': sample['scene_token'],
'lidar2ego_translation': cs_record['translation'],
'lidar2ego_rotation': cs_record['rotation'],
'ego2global_translation': pose_record['translation'],
'ego2global_rotation': pose_record['rotation'],
'timestamp': sample['timestamp'],
}
if sample['next'] == '':
frame_idx = 0
else:
frame_idx += 1
l2e_r = info['lidar2ego_rotation']
l2e_t = info['lidar2ego_translation']
e2g_r = info['ego2global_rotation']
e2g_t = info['ego2global_translation']
l2e_r_mat = Quaternion(l2e_r).rotation_matrix
e2g_r_mat = Quaternion(e2g_r).rotation_matrix
# obtain 6 image's information per frame
camera_types = [
'CAM_FRONT',
'CAM_FRONT_RIGHT',
'CAM_FRONT_LEFT',
'CAM_BACK',
'CAM_BACK_LEFT',
'CAM_BACK_RIGHT',
]
for cam in camera_types:
cam_token = sample['data'][cam]
cam_path, _, cam_intrinsic = nusc.get_sample_data(cam_token)
cam_info = obtain_sensor2top(nusc, cam_token, l2e_t, l2e_r_mat,
e2g_t, e2g_r_mat, cam)
cam_info.update(cam_intrinsic=cam_intrinsic)
info['cams'].update({cam: cam_info})
# obtain sweeps for a single key-frame
sd_rec = nusc.get('sample_data', sample['data']['LIDAR_TOP'])
sweeps = []
while len(sweeps) < max_sweeps:
if not sd_rec['prev'] == '':
sweep = obtain_sensor2top(nusc, sd_rec['prev'], l2e_t,
l2e_r_mat, e2g_t, e2g_r_mat, 'lidar')
sweeps.append(sweep)
sd_rec = nusc.get('sample_data', sd_rec['prev'])
else:
break
info['sweeps'] = sweeps
# obtain annotation
if not test:
annotations = [
nusc.get('sample_annotation', token) for token in sample['anns']
]
locs = np.array([b.center for b in boxes]).reshape(-1, 3)
dims = np.array([b.wlh for b in boxes]).reshape(-1, 3)
rots = np.array([b.orientation.yaw_pitch_roll[0]
for b in boxes]).reshape(-1, 1)
velocity = np.array(
[nusc.box_velocity(token)[:2] for token in sample['anns']])
valid_flag = np.array(
[(anno['num_lidar_pts'] + anno['num_radar_pts']) > 0
for anno in annotations],
dtype=bool).reshape(-1)
# convert velo from global to lidar
for i in range(len(boxes)):
velo = np.array([*velocity[i], 0.0])
velo = velo @ np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(
l2e_r_mat).T
velocity[i] = velo[:2]
names = [b.name for b in boxes]
for i in range(len(names)):
# NuscenesDetDataset.LABEL_MAP | if names[i] in NuscenesDetDataset.LABEL_MAP: | 1 | 2023-11-08 07:08:03+00:00 | 16k |
ml4bio/RhoFold | rhofold/model/structure_module.py | [
{
"identifier": "Linear",
"path": "rhofold/model/primitives.py",
"snippet": "class Linear(nn.Linear):\n \"\"\"\n A Linear layer with built-in nonstandard initializations. Called just\n like torch.nn.Linear.\n\n Implements the initializers in 1.11.4, plus some additional ones found\n in th... | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Sequence
from rhofold.model.primitives import Linear, LayerNorm
from rhofold.utils.rigid_utils import Rigid
from rhofold.utils.tensor_utils import (
dict_multimap,
permute_final_dims,
flatten_final_dims,
)
from einops import rearrange
from rhofold.utils.alphabet import RNAAlphabet
from rhofold.utils.converter import RNAConverter | 10,951 | self.layers.append(layer)
self.linear_out = Linear(self.c_hidden, self.no_angles * 2)
self.relu = nn.ReLU()
def forward(
self, s: torch.Tensor, s_initial: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
s:
[*, C_hidden] single embedding
s_initial:
[*, C_hidden] single embedding as of the start of the
StructureModule
Returns:
[*, no_angles, 2] predicted angles
"""
# [*, C_hidden]
s_initial = self.relu(s_initial)
s_initial = self.linear_initial(s_initial)
s = self.relu(s)
s = self.linear_in(s)
s = s + s_initial
for l in self.layers:
s = l(s)
s = self.relu(s)
# [*, no_angles * 2]
s = self.linear_out(s)
# [*, no_angles, 2]
s = s.view(s.shape[:-1] + (-1, 2))
unnormalized_s = s
norm_denom = torch.sqrt(
torch.clamp(
torch.sum(s ** 2, dim=-1, keepdim=True),
min=self.eps,
)
)
s = s / norm_denom
return unnormalized_s, s
class InvariantPointAttention(nn.Module):
"""
Implements Algorithm 22.
"""
def __init__(
self,
c_s: int,
c_z: int,
c_hidden: int,
no_heads: int,
no_qk_points: int,
no_v_points: int,
inf: float = 1e5,
eps: float = 1e-8,
):
"""
Args:
c_s:
Single representation channel dimension
c_z:
Pair representation channel dimension
c_hidden:
Hidden channel dimension
no_heads:
Number of attention heads
no_qk_points:
Number of query/key points to generate
no_v_points:
Number of value points to generate
"""
super(InvariantPointAttention, self).__init__()
self.c_s = c_s
self.c_z = c_z
self.c_hidden = c_hidden
self.no_heads = no_heads
self.no_qk_points = no_qk_points
self.no_v_points = no_v_points
self.inf = inf
self.eps = eps
hc = self.c_hidden * self.no_heads
self.linear_q = Linear(self.c_s, hc)
self.linear_kv = Linear(self.c_s, 2 * hc)
hpq = self.no_heads * self.no_qk_points * 3
self.linear_q_points = Linear(self.c_s, hpq)
hpkv = self.no_heads * (self.no_qk_points + self.no_v_points) * 3
self.linear_kv_points = Linear(self.c_s, hpkv)
# hpv = self.no_heads * self.no_v_points * 3
self.linear_b = Linear(self.c_z, self.no_heads)
self.head_weights = nn.Parameter(torch.zeros((no_heads)))
# ipa_point_weights_init_(self.head_weights)
concat_out_dim = self.no_heads * (
self.c_z + self.c_hidden + self.no_v_points * 4
)
self.linear_out = Linear(concat_out_dim, self.c_s)
self.softmax = nn.Softmax(dim=-1)
self.softplus = nn.Softplus()
def forward(
self,
s: torch.Tensor,
z: Optional[torch.Tensor],
| # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
class RefineNet(nn.Module):
""""""
def __init__(self, dim = 64, is_pos_emb = True, n_layer = 4, enable = True, **kwargs):
"""Constructor function."""
super().__init__()
self.is_pos_emb = is_pos_emb
self.alphabet = RNAAlphabet.from_architecture('RNA')
self.embed_tokens = nn.Embedding(len(self.alphabet), dim)
self.enable = enable
if self.is_pos_emb:
self.embed_positions = PosEmbedding(4096, dim, self.alphabet.padding_idx)
self.refine_layer0 = ResEGNN(corrections=n_layer, dims_in=dim)
self.refine_layer1 = ResEGNN(corrections=n_layer, dims_in=dim)
self.refine_layer2 = ResEGNN(corrections=n_layer, dims_in=dim)
self.refine_layer3 = ResEGNN(corrections=n_layer, dims_in=dim)
def forward(self, tokens, cords):
"""Perform the forward pass.
Args:
Returns:
"""
if not self.enable:
return cords
tokens = tokens[:, 0, :]
tokens = tokens.unsqueeze(-1).repeat(1, 1, 23)
b, l, n = tokens.shape
cords = cords.reshape([b, l, n, 3])
fea = self.embed_tokens(tokens)
b, l, n, _ = fea.shape
if self.is_pos_emb:
fea += self.embed_positions(tokens.reshape(b * l, n)).view(fea.size())
out = self.refine_layer0(fea.reshape([ b * l, n, -1]), cords.reshape([ b * l, n, -1]), is_fea = True)
fea, cords = out[-1]
fea = fea.reshape([b, l, n, -1]).transpose(1,2)
cords = cords.reshape([b, l, n, -1]).transpose(1,2)
out = self.refine_layer1(fea.reshape([ b * n, l, -1]), cords.reshape([ b * n, l, -1]), is_fea = True)
fea, cords = out[-1]
fea = fea.reshape([b, n, l, -1]).transpose(1,2)
cords = cords.reshape([b, n, l, -1]).transpose(1,2)
out = self.refine_layer2(fea.reshape([ b * l, n, -1]), cords.reshape([ b * l, n, -1]), is_fea = True)
fea, cords = out[-1]
fea = fea.reshape([b, l, n, -1]).transpose(1,2)
cords = cords.reshape([b, l, n, -1]).transpose(1,2)
out = self.refine_layer3(fea.reshape([ b * n, l, -1]), cords.reshape([ b * n, l, -1]), is_fea = True)
fea, cords = out[-1]
cords = cords.reshape([b, n, l, -1]).transpose(1,2)
cords = cords.reshape([b, l * n, 3])
return cords
class Swish_(torch.nn.Module):
def forward(self, x):
return x * x.sigmoid()
SiLU = torch.nn.SiLU if hasattr(torch.nn, 'SiLU') else Swish_
class CoorsNorm(torch.nn.Module):
def __init__(self, eps=1e-8):
super().__init__()
self.eps = eps
self.fn = torch.nn.LayerNorm(1)
def forward(self, coors):
norm = coors.norm(dim=-1, keepdim=True)
normed_coors = coors / norm.clamp(min=self.eps)
phase = self.fn(norm)
return phase * normed_coors
# classes
class EGNN(torch.nn.Module):
def __init__(
self,
dim,
m_dim=32,
):
super().__init__()
'''
# Most of the code in this file is based on egnn-pytorch by lucidrains.
'''
edge_input_dim = (dim * 2) + 1
self.edge_mlp = torch.nn.Sequential(
torch.nn.Linear(edge_input_dim, edge_input_dim * 2),
SiLU(),
torch.nn.Linear(edge_input_dim * 2, m_dim),
SiLU()
)
self.coors_norm = CoorsNorm()
self.node_mlp = torch.nn.Sequential(
torch.nn.Linear(dim + m_dim, dim * 2),
SiLU(),
torch.nn.Linear(dim * 2, dim),
)
self.coors_mlp = torch.nn.Sequential(
torch.nn.Linear(m_dim, m_dim * 4),
SiLU(),
torch.nn.Linear(m_dim * 4, 1)
)
def forward(self, feats, coors):
rel_coors = rearrange(coors, 'b i d -> b i () d') - rearrange(coors, 'b j d -> b () j d')
rel_dist = (rel_coors ** 2).sum(dim=-1, keepdim=True)
feats_j = rearrange(feats, 'b j d -> b () j d')
feats_i = rearrange(feats, 'b i d -> b i () d')
feats_i, feats_j = torch.broadcast_tensors(feats_i, feats_j)
edge_input = torch.cat((feats_i, feats_j, rel_dist), dim=-1)
m_ij = self.edge_mlp(edge_input)
coor_weights = self.coors_mlp(m_ij)
coor_weights = rearrange(coor_weights, 'b i j () -> b i j')
rel_coors = self.coors_norm(rel_coors)
scale_factor = 1 / 50.0
coors_out = torch.einsum('b i j, b i j c -> b i c', coor_weights * scale_factor, rel_coors) + coors
m_i = m_ij.sum(dim=-2)
node_mlp_input = torch.cat((feats, m_i), dim=-1)
node_out = self.node_mlp(node_mlp_input) + feats
return node_out, coors_out
class ResEGNN(torch.nn.Module):
def __init__(self, corrections=4, dims_in=41, **kwargs):
super().__init__()
self.layers = torch.nn.ModuleList([EGNN(dim=dims_in, **kwargs) for _ in range(corrections)])
def forward(self, amino, geom, is_fea = False, keep_last_cords = None):
output = []
for layer in self.layers:
geom_init = geom
amino, geom = layer(amino, geom)
if keep_last_cords is not None:
geom[:, -keep_last_cords:] = geom_init[:, -keep_last_cords:]
output.append([amino, geom])
return output if is_fea else geom
class PosEmbedding(nn.Embedding):
"""
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
if padding_idx is not None:
num_embeddings_ = num_embeddings + padding_idx + 1
else:
num_embeddings_ = num_embeddings
super().__init__(num_embeddings_, embedding_dim, padding_idx)
self.max_positions = num_embeddings
def forward(self, input: torch.Tensor):
"""Input is expected to be of size [bsz x seqlen]."""
mask = input.ne(self.padding_idx).int()
positions = (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + self.padding_idx
return F.embedding(
positions,
self.weight,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
)
class AngleResnetBlock(nn.Module):
def __init__(self, c_hidden):
"""
Args:
c_hidden:
Hidden channel dimension
"""
super(AngleResnetBlock, self).__init__()
self.c_hidden = c_hidden
self.linear_1 = Linear(self.c_hidden, self.c_hidden)
self.linear_2 = Linear(self.c_hidden, self.c_hidden)
self.relu = nn.ReLU()
def forward(self, a: torch.Tensor) -> torch.Tensor:
s_initial = a
a = self.relu(a)
a = self.linear_1(a)
a = self.relu(a)
a = self.linear_2(a)
return a + s_initial
class AngleResnet(nn.Module):
"""
"""
def __init__(self, c_in, c_hidden, no_blocks, no_angles, epsilon):
"""
Args:
c_in:
Input channel dimension
c_hidden:
Hidden channel dimension
no_blocks:
Number of resnet blocks
no_angles:
Number of torsion angles to generate
epsilon:
Small constant for normalization
"""
super(AngleResnet, self).__init__()
self.c_in = c_in
self.c_hidden = c_hidden
self.no_blocks = no_blocks
self.no_angles = no_angles
self.eps = epsilon
self.linear_in = Linear(self.c_in, self.c_hidden)
self.linear_initial = Linear(self.c_in, self.c_hidden)
self.layers = nn.ModuleList()
for _ in range(self.no_blocks):
layer = AngleResnetBlock(c_hidden=self.c_hidden)
self.layers.append(layer)
self.linear_out = Linear(self.c_hidden, self.no_angles * 2)
self.relu = nn.ReLU()
def forward(
self, s: torch.Tensor, s_initial: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
s:
[*, C_hidden] single embedding
s_initial:
[*, C_hidden] single embedding as of the start of the
StructureModule
Returns:
[*, no_angles, 2] predicted angles
"""
# [*, C_hidden]
s_initial = self.relu(s_initial)
s_initial = self.linear_initial(s_initial)
s = self.relu(s)
s = self.linear_in(s)
s = s + s_initial
for l in self.layers:
s = l(s)
s = self.relu(s)
# [*, no_angles * 2]
s = self.linear_out(s)
# [*, no_angles, 2]
s = s.view(s.shape[:-1] + (-1, 2))
unnormalized_s = s
norm_denom = torch.sqrt(
torch.clamp(
torch.sum(s ** 2, dim=-1, keepdim=True),
min=self.eps,
)
)
s = s / norm_denom
return unnormalized_s, s
class InvariantPointAttention(nn.Module):
"""
Implements Algorithm 22.
"""
def __init__(
self,
c_s: int,
c_z: int,
c_hidden: int,
no_heads: int,
no_qk_points: int,
no_v_points: int,
inf: float = 1e5,
eps: float = 1e-8,
):
"""
Args:
c_s:
Single representation channel dimension
c_z:
Pair representation channel dimension
c_hidden:
Hidden channel dimension
no_heads:
Number of attention heads
no_qk_points:
Number of query/key points to generate
no_v_points:
Number of value points to generate
"""
super(InvariantPointAttention, self).__init__()
self.c_s = c_s
self.c_z = c_z
self.c_hidden = c_hidden
self.no_heads = no_heads
self.no_qk_points = no_qk_points
self.no_v_points = no_v_points
self.inf = inf
self.eps = eps
hc = self.c_hidden * self.no_heads
self.linear_q = Linear(self.c_s, hc)
self.linear_kv = Linear(self.c_s, 2 * hc)
hpq = self.no_heads * self.no_qk_points * 3
self.linear_q_points = Linear(self.c_s, hpq)
hpkv = self.no_heads * (self.no_qk_points + self.no_v_points) * 3
self.linear_kv_points = Linear(self.c_s, hpkv)
# hpv = self.no_heads * self.no_v_points * 3
self.linear_b = Linear(self.c_z, self.no_heads)
self.head_weights = nn.Parameter(torch.zeros((no_heads)))
# ipa_point_weights_init_(self.head_weights)
concat_out_dim = self.no_heads * (
self.c_z + self.c_hidden + self.no_v_points * 4
)
self.linear_out = Linear(concat_out_dim, self.c_s)
self.softmax = nn.Softmax(dim=-1)
self.softplus = nn.Softplus()
def forward(
self,
s: torch.Tensor,
z: Optional[torch.Tensor], | r: Rigid, | 2 | 2023-11-01 10:29:08+00:00 | 16k |
tylerlight071/Project-Cipher | main.py | [
{
"identifier": "clear_terminal",
"path": "components/common_functions.py",
"snippet": "def clear_terminal():\n os.system('cls' if os.name == 'nt' else 'clear')"
},
{
"identifier": "print_slow",
"path": "components/common_functions.py",
"snippet": "def print_slow(text, delay=0.00): #... | import msvcrt
import os
import pickle
import sys
import time
import colorama
import pygame
from colorama import Fore, Style
from components.common_functions import clear_terminal, print_slow, shop_help, help_user, connect_help, mail_help, \
system_help
from conversations.calls import intro_call, first_call, second_call, third_call, fourth_call, fifth_call, sixth_call, \
markus_seen_call
from conversations.minigame_calls import code_shatter_call
from minigames.code_shatter_minigame import code_shatter_minigame
from minigames.eye_spy_minigame import port_scanning
from systems.level_1.amy.amy_system import AmySystem
from systems.level_1.billy.billy_system import BillySystem
from systems.level_1.cameras.camera_1 import camera_first
from systems.level_1.markus.markus_system import MarkusSystem | 13,723 |
# Set the PYGAME_HIDE_SUPPORT_PROMPT environment variable
os.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = "1"
# Initialize pygame mixer
pygame.mixer.init()
# Load the bg music file and loop it
pygame.mixer.music.load('bg_music.mp3')
pygame.mixer.music.play(-1)
# sets the volume to 20% (change value to adjust)
pygame.mixer.music.set_volume(0.2)
# Define the global variables at the module level
inventory = []
balance = 300
emails = []
has_read_email = False
has_read_file = False
has_intro_call = False
seen_markus = False
evidence = []
amy_system = AmySystem()
|
# Set the PYGAME_HIDE_SUPPORT_PROMPT environment variable
os.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = "1"
# Initialize pygame mixer
pygame.mixer.init()
# Load the bg music file and loop it
pygame.mixer.music.load('bg_music.mp3')
pygame.mixer.music.play(-1)
# sets the volume to 20% (change value to adjust)
pygame.mixer.music.set_volume(0.2)
# Define the global variables at the module level
inventory = []
balance = 300
emails = []
has_read_email = False
has_read_file = False
has_intro_call = False
seen_markus = False
evidence = []
amy_system = AmySystem() | billy_system = BillySystem() | 19 | 2023-11-06 09:52:13+00:00 | 16k |
ziqi-zhang/TAOISM | python/test/test_bn.py | [
{
"identifier": "register_layer",
"path": "python/common_net.py",
"snippet": "def register_layer(layer, name):\n layer.register_forward_hook(hooking_layer(name))\n layer.register_backward_hook(hooking_layer_backward(name))\n layer_names.append(name)"
},
{
"identifier": "register_weight_... | import os
import sys
import numpy as np
import torch
import torch.distributed as dist
from pdb import set_trace as st
from torch import optim, nn
from python.common_net import register_layer, register_weight_layer, get_layer_weight, get_layer_input, \
get_layer_weight_grad, get_layer_output, get_layer_output_grad, get_layer_input_grad
from python.enclave_interfaces import GlobalTensor
from python.layers.batch_norm_2d import SecretBatchNorm2dLayer
from python.layers.input import SecretInputLayer
from python.layers.output import SecretOutputLayer
from python.sgx_net import init_communicate, warming_up_cuda, SecretNeuralNetwork, SgdOptimizer
from python.utils.basic_utils import ExecutionModeOptions
from python.utils.torch_utils import compare_expected_actual, seed_torch | 11,022 |
device_cuda = torch.device("cuda:0")
torch.set_printoptions(precision=10)
def test_BN(sid=0, master_addr=0, master_port=0, is_compare=False):
batch_size = 2
n_img_channel = 256
img_hw = 32
x_shape = [batch_size, n_img_channel, img_hw, img_hw]
|
device_cuda = torch.device("cuda:0")
torch.set_printoptions(precision=10)
def test_BN(sid=0, master_addr=0, master_port=0, is_compare=False):
batch_size = 2
n_img_channel = 256
img_hw = 32
x_shape = [batch_size, n_img_channel, img_hw, img_hw]
| GlobalTensor.init() | 8 | 2023-11-01 10:37:37+00:00 | 16k |
Codra-Ingenierie-Informatique/DataLab | cdl/tests/features/embedded2_unit.py | [
{
"identifier": "CDLMainWindow",
"path": "cdl/core/gui/main.py",
"snippet": "class CDLMainWindow(QW.QMainWindow, AbstractCDLControl, metaclass=CDLMainWindowMeta):\n \"\"\"DataLab main window\n\n Args:\n console: enable internal console\n hide_on_close: True to hide window on close\n ... | from cdl.core.gui.main import CDLMainWindow
from cdl.tests.features import embedded1_unit | 13,362 | # -*- coding: utf-8 -*-
#
# Licensed under the terms of the BSD 3-Clause
# (see cdl/LICENSE for details)
"""
Application embedded test 2
DataLab main window is simply hidden when closing application.
It is shown and raised above other windows when reopening application.
"""
# guitest: show
| # -*- coding: utf-8 -*-
#
# Licensed under the terms of the BSD 3-Clause
# (see cdl/LICENSE for details)
"""
Application embedded test 2
DataLab main window is simply hidden when closing application.
It is shown and raised above other windows when reopening application.
"""
# guitest: show
| class HostWindow(embedded1_unit.AbstractHostWindow): | 1 | 2023-11-09 16:56:03+00:00 | 16k |
lalalamdbf/PLSE_IDRR | src/prompt-tuning/prompt/pipeline_base.py | [
{
"identifier": "InputExample",
"path": "src/prompt-tuning/prompt/data_utils.py",
"snippet": "class InputExample(object):\n \"\"\"A raw input example consisting of segments of text,\n a label for classification task or a target sequence of generation task.\n Other desired information can be pas... | from pickle import FALSE
from torch.utils.data.sampler import RandomSampler
from transformers.configuration_utils import PretrainedConfig
from transformers.generation_utils import GenerationMixin
from torch.utils.data import Dataset
from typing import *
from .data_utils import InputExample, InputFeatures
from torch.utils.data._utils.collate import default_collate
from tqdm.std import tqdm
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.utils.dummy_pt_objects import PreTrainedModel
from .utils import TokenizerWrapper
from .prompt_base import Template, Verbalizer
from collections import defaultdict
from collections import namedtuple
from torch.utils.data import DataLoader
import torch
import torch.nn as nn
import inspect
import numpy as np | 10,931 |
def signature(f):
r"""Get the function f 's input arguments. A useful gadget
when some function slot might be instantiated into multiple functions.
Args:
f (:obj:`function`) : the function to get the input arguments.
Returns:
namedtuple : of args, default, varargs, keywords, respectively.s
"""
sig = inspect.signature(f)
args = [
p.name for p in sig.parameters.values()
if p.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD
]
varargs = [
p.name for p in sig.parameters.values()
if p.kind == inspect.Parameter.VAR_POSITIONAL
]
varargs = varargs[0] if varargs else None
keywords = [
p.name for p in sig.parameters.values()
if p.kind == inspect.Parameter.VAR_KEYWORD
]
keywords = keywords[0] if keywords else None
defaults = [
p.default for p in sig.parameters.values()
if p.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD
and p.default is not p.empty
] or None
argspec = namedtuple('Signature', ['args', 'defaults',
'varargs', 'keywords'])
return argspec(args, defaults, varargs, keywords)
class PromptDataLoader(object):
r"""
PromptDataLoader wraps the original dataset. The input data is firstly wrapped with the
prompt's template, and then is tokenized by a wrapperd-tokenizer.
Args:
dataset (:obj:`Dataset` or :obj:`List`): Either a DatasetObject or a list containing the input examples.
template (:obj:`Template`): A derived class of :obj:`Template`
tokenizer (:obj:`PretrainedTokenizer`): The pretrained tokenizer.
tokenizer_wrapper_class (:cls:`TokenizerWrapper`): The class of tokenizer wrapper.
max_seq_length (:obj:`int`, optional): The max sequence length of the input ids. It's used to truncate sentences.
batch_size (:obj:`int`, optional): The batch_size of data loader
teacher_forcing (:obj:`bool`, optional): Whether to fill the mask with target text. Set to true in training generation model.
decoder_max_length (:obj:`int`, optional): the decoder maximum length of an encoder-decoder model.
predict_eos_token (:obj:`bool`, optional): Whether to predict the <eos> token. Suggest to set to true in generation.
truncate_method (:obj:`bool`, optional): the truncate method to use. select from `head`, `tail`, `balanced`.
kwargs :Other kwargs that might be passed into a tokenizer wrapper.
"""
def __init__(self,
dataset: Union[Dataset, List],
template: Template,
|
def signature(f):
r"""Get the function f 's input arguments. A useful gadget
when some function slot might be instantiated into multiple functions.
Args:
f (:obj:`function`) : the function to get the input arguments.
Returns:
namedtuple : of args, default, varargs, keywords, respectively.s
"""
sig = inspect.signature(f)
args = [
p.name for p in sig.parameters.values()
if p.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD
]
varargs = [
p.name for p in sig.parameters.values()
if p.kind == inspect.Parameter.VAR_POSITIONAL
]
varargs = varargs[0] if varargs else None
keywords = [
p.name for p in sig.parameters.values()
if p.kind == inspect.Parameter.VAR_KEYWORD
]
keywords = keywords[0] if keywords else None
defaults = [
p.default for p in sig.parameters.values()
if p.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD
and p.default is not p.empty
] or None
argspec = namedtuple('Signature', ['args', 'defaults',
'varargs', 'keywords'])
return argspec(args, defaults, varargs, keywords)
class PromptDataLoader(object):
r"""
PromptDataLoader wraps the original dataset. The input data is firstly wrapped with the
prompt's template, and then is tokenized by a wrapperd-tokenizer.
Args:
dataset (:obj:`Dataset` or :obj:`List`): Either a DatasetObject or a list containing the input examples.
template (:obj:`Template`): A derived class of :obj:`Template`
tokenizer (:obj:`PretrainedTokenizer`): The pretrained tokenizer.
tokenizer_wrapper_class (:cls:`TokenizerWrapper`): The class of tokenizer wrapper.
max_seq_length (:obj:`int`, optional): The max sequence length of the input ids. It's used to truncate sentences.
batch_size (:obj:`int`, optional): The batch_size of data loader
teacher_forcing (:obj:`bool`, optional): Whether to fill the mask with target text. Set to true in training generation model.
decoder_max_length (:obj:`int`, optional): the decoder maximum length of an encoder-decoder model.
predict_eos_token (:obj:`bool`, optional): Whether to predict the <eos> token. Suggest to set to true in generation.
truncate_method (:obj:`bool`, optional): the truncate method to use. select from `head`, `tail`, `balanced`.
kwargs :Other kwargs that might be passed into a tokenizer wrapper.
"""
def __init__(self,
dataset: Union[Dataset, List],
template: Template, | tokenizer_wrapper: Optional[TokenizerWrapper] = None, | 2 | 2023-11-01 08:52:36+00:00 | 16k |
choderalab/chiron | Examples/LJ_mcmove.py | [
{
"identifier": "LJPotential",
"path": "chiron/potential.py",
"snippet": "class LJPotential(NeuralNetworkPotential):\n def __init__(\n self,\n topology: Topology,\n sigma: unit.Quantity = 3.350 * unit.angstroms,\n epsilon: unit.Quantity = 1.0 * unit.kilocalories_per_mole,\... | from openmmtools.testsystems import LennardJonesFluid
from chiron.potential import LJPotential
from openmm import unit
from chiron.states import SamplerState, ThermodynamicState
from chiron.neighbors import NeighborListNsqrd, OrthogonalPeriodicSpace
from chiron.neighbors import PairList
from chiron.reporters import SimulationReporter
from chiron.mcmc import MetropolisDisplacementMove
import os | 13,336 |
# Use the LennardJonesFluid example from openmmtools to initialize particle positions and topology
# For this example, the topology provides the masses for the particles
# The default LennardJonesFluid example considers the system to be Argon with 39.9 amu
lj_fluid = LennardJonesFluid(reduced_density=0.1, nparticles=1000)
# initialize the LennardJones potential in chiron
#
sigma = 0.34 * unit.nanometer
epsilon = 0.238 * unit.kilocalories_per_mole
cutoff = 3.0 * sigma
lj_potential = LJPotential(
lj_fluid.topology, sigma=sigma, epsilon=epsilon, cutoff=cutoff
)
# define the sampler state
sampler_state = SamplerState(
x0=lj_fluid.positions, box_vectors=lj_fluid.system.getDefaultPeriodicBoxVectors()
)
# define the thermodynamic state
thermodynamic_state = ThermodynamicState(
potential=lj_potential, temperature=300 * unit.kelvin
)
# define the neighbor list for an orthogonal periodic space
skin = 0.5 * unit.nanometer
|
# Use the LennardJonesFluid example from openmmtools to initialize particle positions and topology
# For this example, the topology provides the masses for the particles
# The default LennardJonesFluid example considers the system to be Argon with 39.9 amu
lj_fluid = LennardJonesFluid(reduced_density=0.1, nparticles=1000)
# initialize the LennardJones potential in chiron
#
sigma = 0.34 * unit.nanometer
epsilon = 0.238 * unit.kilocalories_per_mole
cutoff = 3.0 * sigma
lj_potential = LJPotential(
lj_fluid.topology, sigma=sigma, epsilon=epsilon, cutoff=cutoff
)
# define the sampler state
sampler_state = SamplerState(
x0=lj_fluid.positions, box_vectors=lj_fluid.system.getDefaultPeriodicBoxVectors()
)
# define the thermodynamic state
thermodynamic_state = ThermodynamicState(
potential=lj_potential, temperature=300 * unit.kelvin
)
# define the neighbor list for an orthogonal periodic space
skin = 0.5 * unit.nanometer
| nbr_list = NeighborListNsqrd( | 3 | 2023-11-07 18:17:43+00:00 | 16k |
WolfgangFahl/dcm | dcm/dcm_webserver.py | [
{
"identifier": "Assessment",
"path": "dcm/dcm_assessment.py",
"snippet": "class Assessment:\n \"\"\"\n Assessment for CompetenceTree\n \"\"\"\n\n def __init__(\n self,\n webserver: NiceGuiWebserver,\n dcm: DynamicCompetenceMap,\n learner: Learner,\n debug:... | import os
from typing import Optional
from urllib.parse import urlparse
from fastapi import HTTPException
from fastapi.responses import HTMLResponse
from ngwidgets.file_selector import FileSelector
from ngwidgets.input_webserver import InputWebserver
from ngwidgets.webserver import WebserverConfig
from nicegui import Client, app, ui
from pydantic import BaseModel
from dcm.dcm_assessment import Assessment
from dcm.dcm_chart import DcmChart
from dcm.dcm_core import CompetenceTree, DynamicCompetenceMap, Learner
from dcm.svg import SVG, SVGConfig
from dcm.version import Version | 13,264 | """
Endpoints to get the description of a
competence area
Args:
tree_id (str): ID of the tree
area_id (str): ID of the area
aspect_id (str, optional): ID of the aspect. Defaults to None.
Returns:
HTMLResponse: HTML content of the description.
"""
path=f"{tree_id}/{aspect_id}/{area_id}"
return await self.show_description(path)
@app.get("/description/{tree_id}/{aspect_id}")
async def get_description_for_aspect(
tree_id: str, aspect_id: str = None
) -> HTMLResponse:
"""
Endpoint to get the description of a competence aspect
Args:
tree_id (str): ID of the tree
area_id (str): ID of the area
Returns:
HTMLResponse: HTML content of the description.
"""
path=f"{tree_id}/{aspect_id}"
return await self.show_description(path)
@app.get("/description/{tree_id}")
async def get_description_for_tree(
tree_id: str
) -> HTMLResponse:
"""
Endpoint to get the description of a competence tree
Args:
tree_id (str): ID of the tree
Returns:
HTMLResponse: HTML content of the description.
"""
path=f"{tree_id}"
return await self.show_description(path)
async def show_description(
self, path:str=None
) -> HTMLResponse:
"""
Show the HTML description of a specific
competence element given by the path
Args:
path(str): the path identifying the element
Returns:
HTMLResponse: The response object containing the HTML-formatted description.
Raises:
HTTPException: If the example name provided does not exist in the examples collection.
"""
path_parts=path.split("/")
tree_id=path_parts[0]
if tree_id in self.examples:
example = self.examples[tree_id]
element = example.competence_tree.lookup_by_path(path)
if element:
content = element.as_html()
return HTMLResponse(content=content)
else:
content = (
f"No element found for {path} in {tree_id}"
)
return HTMLResponse(content=content, status_code=404)
else:
msg = f"unknown competence tree {tree_id}"
raise HTTPException(status_code=404, detail=msg)
async def render_svg(self, svg_render_request: SVGRenderRequest) -> HTMLResponse:
"""
render the given request
"""
r = svg_render_request
dcm = DynamicCompetenceMap.from_definition_string(
r.name, r.definition, content_class=CompetenceTree, markup=r.markup
)
dcm_chart = DcmChart(dcm)
svg_markup = dcm_chart.generate_svg_markup(
config=r.config, with_java_script=True
)
response = HTMLResponse(content=svg_markup)
return response
def get_basename_without_extension(self, url) -> str:
# Parse the URL to get the path component
path = urlparse(url).path
# Extract the base name (e.g., "example.html" from "/dir/example.html")
basename = os.path.basename(path)
# Split the base name and extension and return just the base name
return os.path.splitext(basename)[0]
async def render(self, _click_args=None):
"""
Renders the json content as an SVG visualization
Args:
click_args (object): The click event arguments.
"""
try:
input_source = self.input
if input_source:
name = self.get_basename_without_extension(input_source)
ui.notify(f"rendering {name}")
definition = self.do_read_input(input_source)
# Determine the format based on the file extension
markup = "json" if input_source.endswith(".json") else "yaml"
if "learner_id" in definition:
| """
Created on 2023-11-06
@author: wf
"""
class SVGRenderRequest(BaseModel):
"""
A request for rendering an SVG.
Attributes:
name (str): The name of the render request.
definition (str): The string representation of the data to be rendered, in either JSON or YAML format.
markup (str): The format of the definition ('json' or 'yaml').
config (SVGConfig): Optional configuration for SVG rendering. Defaults to None, which uses default settings.
"""
name: str
definition: str
markup: str
config: Optional[SVGConfig] = None
class DynamicCompentenceMapWebServer(InputWebserver):
"""
server to supply Dynamic Competence Map Visualizations
"""
@classmethod
def get_config(cls) -> WebserverConfig:
"""
get the configuration for this Webserver
"""
copy_right = "(c)2023-2024 Wolfgang Fahl"
config = WebserverConfig(
copy_right=copy_right, version=Version(), default_port=8885
)
return config
def __init__(self):
"""Constructs all the necessary attributes for the WebServer object."""
InputWebserver.__init__(
self, config=DynamicCompentenceMapWebServer.get_config()
)
self.examples = DynamicCompetenceMap.get_examples(markup="yaml")
self.dcm = None
self.assessment = None
@app.post("/svg/")
async def render_svg(svg_render_request: SVGRenderRequest) -> HTMLResponse:
"""
render the given request
"""
return await self.render_svg(svg_render_request)
@app.get("/description/{tree_id}/{aspect_id}/{area_id}/{facet_id}")
async def get_description_for_facet(
tree_id: str, aspect_id: str = None, area_id:str=None, facet_id: str = None
) -> HTMLResponse:
"""
Endpoints to get the description of a competence facet
Args:
tree_id (str): ID of the tree
area_id (str): ID of the area
aspect_id (str, optional): ID of the aspect. Defaults to None.
facet_id (str, optional): ID of the facet. Defaults to None.
Returns:
HTMLResponse: HTML content of the description.
"""
path=f"{tree_id}/{aspect_id}/{area_id}/{facet_id}"
return await self.show_description(path)
@app.get("/description/{tree_id}/{aspect_id}/{area_id}")
async def get_description_for_area(
tree_id: str, aspect_id: str = None, area_id:str=None
) -> HTMLResponse:
"""
Endpoints to get the description of a
competence area
Args:
tree_id (str): ID of the tree
area_id (str): ID of the area
aspect_id (str, optional): ID of the aspect. Defaults to None.
Returns:
HTMLResponse: HTML content of the description.
"""
path=f"{tree_id}/{aspect_id}/{area_id}"
return await self.show_description(path)
@app.get("/description/{tree_id}/{aspect_id}")
async def get_description_for_aspect(
tree_id: str, aspect_id: str = None
) -> HTMLResponse:
"""
Endpoint to get the description of a competence aspect
Args:
tree_id (str): ID of the tree
area_id (str): ID of the area
Returns:
HTMLResponse: HTML content of the description.
"""
path=f"{tree_id}/{aspect_id}"
return await self.show_description(path)
@app.get("/description/{tree_id}")
async def get_description_for_tree(
tree_id: str
) -> HTMLResponse:
"""
Endpoint to get the description of a competence tree
Args:
tree_id (str): ID of the tree
Returns:
HTMLResponse: HTML content of the description.
"""
path=f"{tree_id}"
return await self.show_description(path)
async def show_description(
self, path:str=None
) -> HTMLResponse:
"""
Show the HTML description of a specific
competence element given by the path
Args:
path(str): the path identifying the element
Returns:
HTMLResponse: The response object containing the HTML-formatted description.
Raises:
HTTPException: If the example name provided does not exist in the examples collection.
"""
path_parts=path.split("/")
tree_id=path_parts[0]
if tree_id in self.examples:
example = self.examples[tree_id]
element = example.competence_tree.lookup_by_path(path)
if element:
content = element.as_html()
return HTMLResponse(content=content)
else:
content = (
f"No element found for {path} in {tree_id}"
)
return HTMLResponse(content=content, status_code=404)
else:
msg = f"unknown competence tree {tree_id}"
raise HTTPException(status_code=404, detail=msg)
async def render_svg(self, svg_render_request: SVGRenderRequest) -> HTMLResponse:
"""
render the given request
"""
r = svg_render_request
dcm = DynamicCompetenceMap.from_definition_string(
r.name, r.definition, content_class=CompetenceTree, markup=r.markup
)
dcm_chart = DcmChart(dcm)
svg_markup = dcm_chart.generate_svg_markup(
config=r.config, with_java_script=True
)
response = HTMLResponse(content=svg_markup)
return response
def get_basename_without_extension(self, url) -> str:
# Parse the URL to get the path component
path = urlparse(url).path
# Extract the base name (e.g., "example.html" from "/dir/example.html")
basename = os.path.basename(path)
# Split the base name and extension and return just the base name
return os.path.splitext(basename)[0]
async def render(self, _click_args=None):
"""
Renders the json content as an SVG visualization
Args:
click_args (object): The click event arguments.
"""
try:
input_source = self.input
if input_source:
name = self.get_basename_without_extension(input_source)
ui.notify(f"rendering {name}")
definition = self.do_read_input(input_source)
# Determine the format based on the file extension
markup = "json" if input_source.endswith(".json") else "yaml"
if "learner_id" in definition: | content_class = Learner | 4 | 2023-11-06 09:24:24+00:00 | 16k |
shadowpa0327/FLORA | models/build.py | [
{
"identifier": "VisionTransformer",
"path": "models/deit.py",
"snippet": "class VisionTransformer(nn.Module):\n \"\"\" Vision Transformer\n\n A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`\n - https://arxiv.org/abs/2010.11929\n\n Include... | from .deit import VisionTransformer
from .swin_transformer import SwinTransformer
from .lr_swin_transformer import LRSwinTransformer
from .lr_swin_transformer_subnet import LRSwinTransformer as LRSwinTransformerSubnet
from .lr_deit import LRVisionTransformer
from .lr_deit_subnet import LRVisionTransformerSubnet | 10,828 | # --------------------------------------------------------
# TinyViT Model Builder
# Copyright (c) 2022 Microsoft
# --------------------------------------------------------
def build_model(config):
model_type = config.MODEL.TYPE
if model_type == 'swin':
model = SwinTransformer(
img_size=config.DATA.IMG_SIZE,
patch_size=config.MODEL.SWIN.PATCH_SIZE,
in_chans=config.MODEL.SWIN.IN_CHANS,
num_classes=config.MODEL.NUM_CLASSES,
embed_dim=config.MODEL.SWIN.EMBED_DIM,
depths=config.MODEL.SWIN.DEPTHS,
num_heads=config.MODEL.SWIN.NUM_HEADS,
window_size=config.MODEL.SWIN.WINDOW_SIZE,
mlp_ratio=config.MODEL.SWIN.MLP_RATIO,
qkv_bias=config.MODEL.SWIN.QKV_BIAS,
qk_scale=config.MODEL.SWIN.QK_SCALE,
drop_rate=config.MODEL.DROP_RATE,
drop_path_rate=config.MODEL.DROP_PATH_RATE,
ape=config.MODEL.SWIN.APE,
patch_norm=config.MODEL.SWIN.PATCH_NORM,
use_checkpoint=config.TRAIN.USE_CHECKPOINT,
fused_window_process=config.FUSED_WINDOW_PROCESS
)
elif model_type == 'deit':
model = VisionTransformer(
img_size=config.DATA.IMG_SIZE,
patch_size=config.MODEL.DEIT.PATCH_SIZE,
in_chans=config.MODEL.DEIT.IN_CHANS,
num_classes=config.MODEL.NUM_CLASSES,
embed_dim=config.MODEL.DEIT.EMBED_DIM,
depth=config.MODEL.DEIT.DEPTH,
num_heads = config.MODEL.DEIT.NUM_HEADS,
mlp_ratio = config.MODEL.DEIT.MLP_RATIO,
qkv_bias = config.MODEL.DEIT.QKV_BIAS,
drop_rate=config.MODEL.DROP_RATE,
drop_path_rate=config.MODEL.DROP_PATH_RATE,
)
elif model_type == 'lr_swin':
model = LRSwinTransformer(
img_size=config.DATA.IMG_SIZE,
patch_size=config.MODEL.SWIN.PATCH_SIZE,
in_chans=config.MODEL.SWIN.IN_CHANS,
num_classes=config.MODEL.NUM_CLASSES,
embed_dim=config.MODEL.SWIN.EMBED_DIM,
depths=config.MODEL.SWIN.DEPTHS,
num_heads=config.MODEL.SWIN.NUM_HEADS,
window_size=config.MODEL.SWIN.WINDOW_SIZE,
mlp_ratio=config.MODEL.SWIN.MLP_RATIO,
qkv_bias=config.MODEL.SWIN.QKV_BIAS,
qk_scale=config.MODEL.SWIN.QK_SCALE,
drop_rate=config.MODEL.DROP_RATE,
drop_path_rate=config.MODEL.DROP_PATH_RATE,
ape=config.MODEL.SWIN.APE,
patch_norm=config.MODEL.SWIN.PATCH_NORM,
use_checkpoint=config.TRAIN.USE_CHECKPOINT,
fused_window_process=config.FUSED_WINDOW_PROCESS
)
elif model_type == 'lr_swin_subnet':
model = LRSwinTransformerSubnet(
svd_config=config.MODEL.SWIN.SVD_CONFIG,
img_size=config.DATA.IMG_SIZE,
patch_size=config.MODEL.SWIN.PATCH_SIZE,
in_chans=config.MODEL.SWIN.IN_CHANS,
num_classes=config.MODEL.NUM_CLASSES,
embed_dim=config.MODEL.SWIN.EMBED_DIM,
depths=config.MODEL.SWIN.DEPTHS,
num_heads=config.MODEL.SWIN.NUM_HEADS,
window_size=config.MODEL.SWIN.WINDOW_SIZE,
mlp_ratio=config.MODEL.SWIN.MLP_RATIO,
qkv_bias=config.MODEL.SWIN.QKV_BIAS,
qk_scale=config.MODEL.SWIN.QK_SCALE,
drop_rate=config.MODEL.DROP_RATE,
drop_path_rate=config.MODEL.DROP_PATH_RATE,
ape=config.MODEL.SWIN.APE,
patch_norm=config.MODEL.SWIN.PATCH_NORM,
use_checkpoint=config.TRAIN.USE_CHECKPOINT,
fused_window_process=config.FUSED_WINDOW_PROCESS
)
elif model_type == 'lr_deit':
model = LRVisionTransformer(
img_size=config.DATA.IMG_SIZE,
patch_size=config.MODEL.DEIT.PATCH_SIZE,
in_chans=config.MODEL.DEIT.IN_CHANS,
num_classes=config.MODEL.NUM_CLASSES,
embed_dim=config.MODEL.DEIT.EMBED_DIM,
depth=config.MODEL.DEIT.DEPTH,
num_heads = config.MODEL.DEIT.NUM_HEADS,
mlp_ratio = config.MODEL.DEIT.MLP_RATIO,
qkv_bias = config.MODEL.DEIT.QKV_BIAS,
drop_rate=config.MODEL.DROP_RATE,
drop_path_rate=config.MODEL.DROP_PATH_RATE,
fused_lr=config.MODEL.DEIT.FUSE_LR,
)
elif model_type == 'lr_deit_subnet':
| # --------------------------------------------------------
# TinyViT Model Builder
# Copyright (c) 2022 Microsoft
# --------------------------------------------------------
def build_model(config):
model_type = config.MODEL.TYPE
if model_type == 'swin':
model = SwinTransformer(
img_size=config.DATA.IMG_SIZE,
patch_size=config.MODEL.SWIN.PATCH_SIZE,
in_chans=config.MODEL.SWIN.IN_CHANS,
num_classes=config.MODEL.NUM_CLASSES,
embed_dim=config.MODEL.SWIN.EMBED_DIM,
depths=config.MODEL.SWIN.DEPTHS,
num_heads=config.MODEL.SWIN.NUM_HEADS,
window_size=config.MODEL.SWIN.WINDOW_SIZE,
mlp_ratio=config.MODEL.SWIN.MLP_RATIO,
qkv_bias=config.MODEL.SWIN.QKV_BIAS,
qk_scale=config.MODEL.SWIN.QK_SCALE,
drop_rate=config.MODEL.DROP_RATE,
drop_path_rate=config.MODEL.DROP_PATH_RATE,
ape=config.MODEL.SWIN.APE,
patch_norm=config.MODEL.SWIN.PATCH_NORM,
use_checkpoint=config.TRAIN.USE_CHECKPOINT,
fused_window_process=config.FUSED_WINDOW_PROCESS
)
elif model_type == 'deit':
model = VisionTransformer(
img_size=config.DATA.IMG_SIZE,
patch_size=config.MODEL.DEIT.PATCH_SIZE,
in_chans=config.MODEL.DEIT.IN_CHANS,
num_classes=config.MODEL.NUM_CLASSES,
embed_dim=config.MODEL.DEIT.EMBED_DIM,
depth=config.MODEL.DEIT.DEPTH,
num_heads = config.MODEL.DEIT.NUM_HEADS,
mlp_ratio = config.MODEL.DEIT.MLP_RATIO,
qkv_bias = config.MODEL.DEIT.QKV_BIAS,
drop_rate=config.MODEL.DROP_RATE,
drop_path_rate=config.MODEL.DROP_PATH_RATE,
)
elif model_type == 'lr_swin':
model = LRSwinTransformer(
img_size=config.DATA.IMG_SIZE,
patch_size=config.MODEL.SWIN.PATCH_SIZE,
in_chans=config.MODEL.SWIN.IN_CHANS,
num_classes=config.MODEL.NUM_CLASSES,
embed_dim=config.MODEL.SWIN.EMBED_DIM,
depths=config.MODEL.SWIN.DEPTHS,
num_heads=config.MODEL.SWIN.NUM_HEADS,
window_size=config.MODEL.SWIN.WINDOW_SIZE,
mlp_ratio=config.MODEL.SWIN.MLP_RATIO,
qkv_bias=config.MODEL.SWIN.QKV_BIAS,
qk_scale=config.MODEL.SWIN.QK_SCALE,
drop_rate=config.MODEL.DROP_RATE,
drop_path_rate=config.MODEL.DROP_PATH_RATE,
ape=config.MODEL.SWIN.APE,
patch_norm=config.MODEL.SWIN.PATCH_NORM,
use_checkpoint=config.TRAIN.USE_CHECKPOINT,
fused_window_process=config.FUSED_WINDOW_PROCESS
)
elif model_type == 'lr_swin_subnet':
model = LRSwinTransformerSubnet(
svd_config=config.MODEL.SWIN.SVD_CONFIG,
img_size=config.DATA.IMG_SIZE,
patch_size=config.MODEL.SWIN.PATCH_SIZE,
in_chans=config.MODEL.SWIN.IN_CHANS,
num_classes=config.MODEL.NUM_CLASSES,
embed_dim=config.MODEL.SWIN.EMBED_DIM,
depths=config.MODEL.SWIN.DEPTHS,
num_heads=config.MODEL.SWIN.NUM_HEADS,
window_size=config.MODEL.SWIN.WINDOW_SIZE,
mlp_ratio=config.MODEL.SWIN.MLP_RATIO,
qkv_bias=config.MODEL.SWIN.QKV_BIAS,
qk_scale=config.MODEL.SWIN.QK_SCALE,
drop_rate=config.MODEL.DROP_RATE,
drop_path_rate=config.MODEL.DROP_PATH_RATE,
ape=config.MODEL.SWIN.APE,
patch_norm=config.MODEL.SWIN.PATCH_NORM,
use_checkpoint=config.TRAIN.USE_CHECKPOINT,
fused_window_process=config.FUSED_WINDOW_PROCESS
)
elif model_type == 'lr_deit':
model = LRVisionTransformer(
img_size=config.DATA.IMG_SIZE,
patch_size=config.MODEL.DEIT.PATCH_SIZE,
in_chans=config.MODEL.DEIT.IN_CHANS,
num_classes=config.MODEL.NUM_CLASSES,
embed_dim=config.MODEL.DEIT.EMBED_DIM,
depth=config.MODEL.DEIT.DEPTH,
num_heads = config.MODEL.DEIT.NUM_HEADS,
mlp_ratio = config.MODEL.DEIT.MLP_RATIO,
qkv_bias = config.MODEL.DEIT.QKV_BIAS,
drop_rate=config.MODEL.DROP_RATE,
drop_path_rate=config.MODEL.DROP_PATH_RATE,
fused_lr=config.MODEL.DEIT.FUSE_LR,
)
elif model_type == 'lr_deit_subnet': | model = LRVisionTransformerSubnet( | 5 | 2023-11-03 09:54:45+00:00 | 16k |
Harvard-Ophthalmology-AI-Lab/FairSeg | SAMed/segment_anything/automatic_mask_generator.py | [
{
"identifier": "Sam",
"path": "SAMed/segment_anything/modeling/sam.py",
"snippet": "class Sam(nn.Module):\n mask_threshold: float = 0.0\n image_format: str = \"RGB\"\n\n def __init__(\n self,\n image_encoder: ImageEncoderViT,\n prompt_encoder: PromptEncoder,\n mask_... | import numpy as np
import torch
import cv2 # type: ignore # noqa: F401
from torchvision.ops.boxes import batched_nms, box_area # type: ignore
from typing import Any, Dict, List, Optional, Tuple
from .modeling import Sam
from .predictor import SamPredictor
from .utils.amg import (
MaskData,
area_from_rle,
batch_iterator,
batched_mask_to_box,
box_xyxy_to_xywh,
build_all_layer_point_grids,
calculate_stability_score,
coco_encode_rle,
generate_crop_boxes,
is_box_near_crop_edge,
mask_to_rle_pytorch,
remove_small_regions,
rle_to_mask,
uncrop_boxes_xyxy,
uncrop_masks,
uncrop_points,
)
from pycocotools import mask as mask_utils # type: ignore # noqa: F401 | 11,326 | "point_coords": [mask_data["points"][idx].tolist()],
"stability_score": mask_data["stability_score"][idx].item(),
"crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
}
curr_anns.append(ann)
return curr_anns
def _generate_masks(self, image: np.ndarray) -> MaskData:
orig_size = image.shape[:2]
crop_boxes, layer_idxs = generate_crop_boxes(
orig_size, self.crop_n_layers, self.crop_overlap_ratio
)
# Iterate over image crops
data = MaskData()
for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
data.cat(crop_data)
# Remove duplicate masks between crops
if len(crop_boxes) > 1:
# Prefer masks from smaller crops
scores = 1 / box_area(data["crop_boxes"])
scores = scores.to(data["boxes"].device)
keep_by_nms = batched_nms(
data["boxes"].float(),
scores,
torch.zeros(len(data["boxes"])), # categories
iou_threshold=self.crop_nms_thresh,
)
data.filter(keep_by_nms)
data.to_numpy()
return data
def _process_crop(
self,
image: np.ndarray,
crop_box: List[int],
crop_layer_idx: int,
orig_size: Tuple[int, ...],
) -> MaskData:
# Crop the image and calculate embeddings
x0, y0, x1, y1 = crop_box
cropped_im = image[y0:y1, x0:x1, :]
cropped_im_size = cropped_im.shape[:2]
self.predictor.set_image(cropped_im)
# Get points for this crop
points_scale = np.array(cropped_im_size)[None, ::-1]
points_for_image = self.point_grids[crop_layer_idx] * points_scale
# Generate masks for this crop in batches
data = MaskData()
for (points,) in batch_iterator(self.points_per_batch, points_for_image):
batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size)
data.cat(batch_data)
del batch_data
self.predictor.reset_image()
# Remove duplicates within this crop.
keep_by_nms = batched_nms(
data["boxes"].float(),
data["iou_preds"],
torch.zeros(len(data["boxes"])), # categories
iou_threshold=self.box_nms_thresh,
)
data.filter(keep_by_nms)
# Return to the original image frame
data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
data["points"] = uncrop_points(data["points"], crop_box)
data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])
return data
def _process_batch(
self,
points: np.ndarray,
im_size: Tuple[int, ...],
crop_box: List[int],
orig_size: Tuple[int, ...],
) -> MaskData:
orig_h, orig_w = orig_size
# Run model on this batch
transformed_points = self.predictor.transform.apply_coords(points, im_size)
in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
masks, iou_preds, _ = self.predictor.predict_torch(
in_points[:, None, :],
in_labels[:, None],
multimask_output=True,
return_logits=True,
)
# Serialize predictions and store in MaskData
data = MaskData(
masks=masks.flatten(0, 1),
iou_preds=iou_preds.flatten(0, 1),
points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
)
del masks
# Filter by predicted IoU
if self.pred_iou_thresh > 0.0:
keep_mask = data["iou_preds"] > self.pred_iou_thresh
data.filter(keep_mask)
# Calculate stability score
data["stability_score"] = calculate_stability_score(
data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
)
if self.stability_score_thresh > 0.0:
keep_mask = data["stability_score"] >= self.stability_score_thresh
data.filter(keep_mask)
# Threshold masks and calculate boxes
data["masks"] = data["masks"] > self.predictor.model.mask_threshold
| # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
class SamAutomaticMaskGenerator:
def __init__(
self,
model: Sam,
points_per_side: Optional[int] = 32,
points_per_batch: int = 64,
pred_iou_thresh: float = 0.88,
stability_score_thresh: float = 0.95,
stability_score_offset: float = 1.0,
box_nms_thresh: float = 0.7,
crop_n_layers: int = 0,
crop_nms_thresh: float = 0.7,
crop_overlap_ratio: float = 512 / 1500,
crop_n_points_downscale_factor: int = 1,
point_grids: Optional[List[np.ndarray]] = None,
min_mask_region_area: int = 0,
output_mode: str = "binary_mask",
) -> None:
"""
Using a SAM model, generates masks for the entire image.
Generates a grid of point prompts over the image, then filters
low quality and duplicate masks. The default settings are chosen
for SAM with a ViT-H backbone.
Arguments:
model (Sam): The SAM model to use for mask prediction.
points_per_side (int or None): The number of points to be sampled
along one side of the image. The total number of points is
points_per_side**2. If None, 'point_grids' must provide explicit
point sampling.
points_per_batch (int): Sets the number of points run simultaneously
by the model. Higher numbers may be faster but use more GPU memory.
pred_iou_thresh (float): A filtering threshold in [0,1], using the
model's predicted mask quality.
stability_score_thresh (float): A filtering threshold in [0,1], using
the stability of the mask under changes to the cutoff used to binarize
the model's mask predictions.
stability_score_offset (float): The amount to shift the cutoff when
calculated the stability score.
box_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks.
crops_n_layers (int): If >0, mask prediction will be run again on
crops of the image. Sets the number of layers to run, where each
layer has 2**i_layer number of image crops.
crops_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks between different crops.
crop_overlap_ratio (float): Sets the degree to which crops overlap.
In the first crop layer, crops will overlap by this fraction of
the image length. Later layers with more crops scale down this overlap.
crop_n_points_downscale_factor (int): The number of points-per-side
sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
point_grids (list(np.ndarray) or None): A list over explicit grids
of points used for sampling, normalized to [0,1]. The nth grid in the
list is used in the nth crop layer. Exclusive with points_per_side.
min_mask_region_area (int): If >0, postprocessing will be applied
to remove disconnected regions and holes in masks with area smaller
than min_mask_region_area. Requires opencv.
output_mode (str): The form masks are returned in. Can be 'binary_mask',
'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
For large resolutions, 'binary_mask' may consume large amounts of
memory.
"""
assert (points_per_side is None) != (
point_grids is None
), "Exactly one of points_per_side or point_grid must be provided."
if points_per_side is not None:
self.point_grids = build_all_layer_point_grids(
points_per_side,
crop_n_layers,
crop_n_points_downscale_factor,
)
elif point_grids is not None:
self.point_grids = point_grids
else:
raise ValueError("Can't have both points_per_side and point_grid be None.")
assert output_mode in [
"binary_mask",
"uncompressed_rle",
"coco_rle",
], f"Unknown output_mode {output_mode}."
if output_mode == "coco_rle":
if min_mask_region_area > 0:
self.predictor = SamPredictor(model)
self.points_per_batch = points_per_batch
self.pred_iou_thresh = pred_iou_thresh
self.stability_score_thresh = stability_score_thresh
self.stability_score_offset = stability_score_offset
self.box_nms_thresh = box_nms_thresh
self.crop_n_layers = crop_n_layers
self.crop_nms_thresh = crop_nms_thresh
self.crop_overlap_ratio = crop_overlap_ratio
self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
self.min_mask_region_area = min_mask_region_area
self.output_mode = output_mode
@torch.no_grad()
def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
"""
Generates masks for the given image.
Arguments:
image (np.ndarray): The image to generate masks for, in HWC uint8 format.
Returns:
list(dict(str, any)): A list over records for masks. Each record is
a dict containing the following keys:
segmentation (dict(str, any) or np.ndarray): The mask. If
output_mode='binary_mask', is an array of shape HW. Otherwise,
is a dictionary containing the RLE.
bbox (list(float)): The box around the mask, in XYWH format.
area (int): The area in pixels of the mask.
predicted_iou (float): The model's own prediction of the mask's
quality. This is filtered by the pred_iou_thresh parameter.
point_coords (list(list(float))): The point coordinates input
to the model to generate this mask.
stability_score (float): A measure of the mask's quality. This
is filtered on using the stability_score_thresh parameter.
crop_box (list(float)): The crop of the image used to generate
the mask, given in XYWH format.
"""
# Generate masks
mask_data = self._generate_masks(image)
# Filter small disconnected regions and holes in masks
if self.min_mask_region_area > 0:
mask_data = self.postprocess_small_regions(
mask_data,
self.min_mask_region_area,
max(self.box_nms_thresh, self.crop_nms_thresh),
)
# Encode masks
if self.output_mode == "coco_rle":
mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]]
elif self.output_mode == "binary_mask":
mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
else:
mask_data["segmentations"] = mask_data["rles"]
# Write mask records
curr_anns = []
for idx in range(len(mask_data["segmentations"])):
ann = {
"segmentation": mask_data["segmentations"][idx],
"area": area_from_rle(mask_data["rles"][idx]),
"bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
"predicted_iou": mask_data["iou_preds"][idx].item(),
"point_coords": [mask_data["points"][idx].tolist()],
"stability_score": mask_data["stability_score"][idx].item(),
"crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
}
curr_anns.append(ann)
return curr_anns
def _generate_masks(self, image: np.ndarray) -> MaskData:
orig_size = image.shape[:2]
crop_boxes, layer_idxs = generate_crop_boxes(
orig_size, self.crop_n_layers, self.crop_overlap_ratio
)
# Iterate over image crops
data = MaskData()
for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
data.cat(crop_data)
# Remove duplicate masks between crops
if len(crop_boxes) > 1:
# Prefer masks from smaller crops
scores = 1 / box_area(data["crop_boxes"])
scores = scores.to(data["boxes"].device)
keep_by_nms = batched_nms(
data["boxes"].float(),
scores,
torch.zeros(len(data["boxes"])), # categories
iou_threshold=self.crop_nms_thresh,
)
data.filter(keep_by_nms)
data.to_numpy()
return data
def _process_crop(
self,
image: np.ndarray,
crop_box: List[int],
crop_layer_idx: int,
orig_size: Tuple[int, ...],
) -> MaskData:
# Crop the image and calculate embeddings
x0, y0, x1, y1 = crop_box
cropped_im = image[y0:y1, x0:x1, :]
cropped_im_size = cropped_im.shape[:2]
self.predictor.set_image(cropped_im)
# Get points for this crop
points_scale = np.array(cropped_im_size)[None, ::-1]
points_for_image = self.point_grids[crop_layer_idx] * points_scale
# Generate masks for this crop in batches
data = MaskData()
for (points,) in batch_iterator(self.points_per_batch, points_for_image):
batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size)
data.cat(batch_data)
del batch_data
self.predictor.reset_image()
# Remove duplicates within this crop.
keep_by_nms = batched_nms(
data["boxes"].float(),
data["iou_preds"],
torch.zeros(len(data["boxes"])), # categories
iou_threshold=self.box_nms_thresh,
)
data.filter(keep_by_nms)
# Return to the original image frame
data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
data["points"] = uncrop_points(data["points"], crop_box)
data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])
return data
def _process_batch(
self,
points: np.ndarray,
im_size: Tuple[int, ...],
crop_box: List[int],
orig_size: Tuple[int, ...],
) -> MaskData:
orig_h, orig_w = orig_size
# Run model on this batch
transformed_points = self.predictor.transform.apply_coords(points, im_size)
in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
masks, iou_preds, _ = self.predictor.predict_torch(
in_points[:, None, :],
in_labels[:, None],
multimask_output=True,
return_logits=True,
)
# Serialize predictions and store in MaskData
data = MaskData(
masks=masks.flatten(0, 1),
iou_preds=iou_preds.flatten(0, 1),
points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
)
del masks
# Filter by predicted IoU
if self.pred_iou_thresh > 0.0:
keep_mask = data["iou_preds"] > self.pred_iou_thresh
data.filter(keep_mask)
# Calculate stability score
data["stability_score"] = calculate_stability_score(
data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
)
if self.stability_score_thresh > 0.0:
keep_mask = data["stability_score"] >= self.stability_score_thresh
data.filter(keep_mask)
# Threshold masks and calculate boxes
data["masks"] = data["masks"] > self.predictor.model.mask_threshold | data["boxes"] = batched_mask_to_box(data["masks"]) | 5 | 2023-11-03 17:05:40+00:00 | 16k |
microsoft/PLEX | PLEX/finetuning.py | [
{
"identifier": "TrajectoryDataset",
"path": "PLEX/util/data.py",
"snippet": "class TrajectoryDataset:\n def __init__(self, trajectories, camera_names, contextual):\n self.trajectories = list(trajectories)\n if not globals.full_state_mode:\n self.camera_names = camera_names\n... | import os
import torch
import argparse
import sys
from PLEX.util.data import TrajectoryDataset, load_data, setup_batch_sampler, train_val_split
from PLEX.util.misc import parse_tasks, setup_essentials, setup_model, set_trainable_params, setup_trainer, run_training
from PLEX.util.evaluators import get_success_rate_evaluator, get_validation_error_evaluator
from PLEX.util.cmdline import add_common_args, add_conditioning_args
from PLEX.util.log import setup_wandb_logging | 11,382 |
def finetune(cmdline_args):
os.environ["NCCL_DEBUG"] = "INFO"
print("=== Finetuning ===")
parser = argparse.ArgumentParser()
# Add all relevant command-line arguments
add_common_args(parser)
add_conditioning_args(parser)
parser.add_argument('--finetune_learning_rate', type=float, default=1e-5)
parser.add_argument('--finetune_steps_per_iter', type=int, default=100)
parser.add_argument('--target_task', type=str, default=None)
parser.add_argument('--max_target_trajectories', type=int, default=None)
# Parse them and validate them
args = parser.parse_args(cmdline_args)
args = vars(args)
if not args['bc_learning_mode']:
assert 'reward' not in args['modalities_to_mask'], "If the model is expected to condition on returns, then they should not be masked out."
# NOTE: The arguments below aren't actual command-line arguments. We are just addeing them to args[] out of convenience.
# Note also that during finetuning we set predicted_inverse_dynamics_loss_weight=1, i.e., **in case the
# finetuning trajectories contain actions**, we adapt PLEX's based on the predicted observation latents
# from it planner PL rather than based on the actual ("grounded") observation latents contained
# in finetuning trajectories.
if args['model'] == 'PLEX':
args['grounded_inverse_dynamics_loss_weight'] = 0
args['predicted_inverse_dynamics_loss_weight'] = 1
args['future_prediction_loss_weight'] = 1
|
def finetune(cmdline_args):
os.environ["NCCL_DEBUG"] = "INFO"
print("=== Finetuning ===")
parser = argparse.ArgumentParser()
# Add all relevant command-line arguments
add_common_args(parser)
add_conditioning_args(parser)
parser.add_argument('--finetune_learning_rate', type=float, default=1e-5)
parser.add_argument('--finetune_steps_per_iter', type=int, default=100)
parser.add_argument('--target_task', type=str, default=None)
parser.add_argument('--max_target_trajectories', type=int, default=None)
# Parse them and validate them
args = parser.parse_args(cmdline_args)
args = vars(args)
if not args['bc_learning_mode']:
assert 'reward' not in args['modalities_to_mask'], "If the model is expected to condition on returns, then they should not be masked out."
# NOTE: The arguments below aren't actual command-line arguments. We are just addeing them to args[] out of convenience.
# Note also that during finetuning we set predicted_inverse_dynamics_loss_weight=1, i.e., **in case the
# finetuning trajectories contain actions**, we adapt PLEX's based on the predicted observation latents
# from it planner PL rather than based on the actual ("grounded") observation latents contained
# in finetuning trajectories.
if args['model'] == 'PLEX':
args['grounded_inverse_dynamics_loss_weight'] = 0
args['predicted_inverse_dynamics_loss_weight'] = 1
args['future_prediction_loss_weight'] = 1
| log, log_to_wandb, timer, data_shuffling_rng, device, camera_names, modalities_to_mask, data_dir, common_env_metadata_dict = setup_essentials(args) | 5 | 2023-11-06 09:38:09+00:00 | 16k |
Giftify-Bot/Giftify-Bot | cogs/giveaways/start.py | [
{
"identifier": "Giftify",
"path": "bot.py",
"snippet": "class Giftify(GiftifyHelper, commands.AutoShardedBot):\r\n user: discord.ClientUser\r\n\r\n colour: int = 0xCB3045\r\n __version_info__ = \"1.1.4\"\r\n\r\n def __init__(\r\n self,\r\n *,\r\n log_handler: LogHandler... | import datetime
import discord
from collections import ChainMap
from typing import Dict, List, Optional, Tuple
from discord import app_commands
from discord.app_commands import Transform
from discord.ext import commands
from bot import Giftify
from models.giveaway_settings import ChannelConfig
from models.giveaways import Giveaway, GiveawayAction
from utils.exceptions import GiveawayError
from utils.transformers import (
BonusRolesTransformer,
RolesTransformer,
TextChannelsTransformer,
TimeTransformer,
)
from utils.tree import Interaction | 11,054 |
class GiveawayStart(commands.GroupCog):
"""A cog for starting giveaways."""
bot: Giftify
@app_commands.command(name="start")
@app_commands.describe(
prize="The prize of the giveaway.",
duration="The duration of the giveaway.",
winners="The number of winners for the giveaway.",
required_roles="The roles required to participate in the giveaway.",
blacklisted_roles="The roles not allowed to participate in the giveaway.",
bypass_roles="The roles that can bypass participation restrictions",
multiplier_roles="Use the format <role:number_of_multiplier_roles_entries> split using a ':' (colon).",
messages_required="The number of messages required to join the giveaway.",
allowed_message_channels="The channels where users are allowed to send messages.",
amari="The amari level required for the giveaway.",
weekly_amari="The weekly amari xp required for the giveaway.",
no_defaults="Flag to exclude default settings for the giveaway.",
image="The attached image for the giveaway.",
ping="Wheter to ping the server pingrole when the giveaway starts.",
donor="The donating member for the giveaway.",
message="The message to accompany the giveaway.",
)
@app_commands.checks.bot_has_permissions(
embed_links=True, send_messages=True, view_channel=True, add_reactions=True
)
@app_commands.checks.cooldown(1, 5, key=lambda i: (i.guild, i.user.id))
async def giveaway_start(
self,
interaction: Interaction,
|
class GiveawayStart(commands.GroupCog):
"""A cog for starting giveaways."""
bot: Giftify
@app_commands.command(name="start")
@app_commands.describe(
prize="The prize of the giveaway.",
duration="The duration of the giveaway.",
winners="The number of winners for the giveaway.",
required_roles="The roles required to participate in the giveaway.",
blacklisted_roles="The roles not allowed to participate in the giveaway.",
bypass_roles="The roles that can bypass participation restrictions",
multiplier_roles="Use the format <role:number_of_multiplier_roles_entries> split using a ':' (colon).",
messages_required="The number of messages required to join the giveaway.",
allowed_message_channels="The channels where users are allowed to send messages.",
amari="The amari level required for the giveaway.",
weekly_amari="The weekly amari xp required for the giveaway.",
no_defaults="Flag to exclude default settings for the giveaway.",
image="The attached image for the giveaway.",
ping="Wheter to ping the server pingrole when the giveaway starts.",
donor="The donating member for the giveaway.",
message="The message to accompany the giveaway.",
)
@app_commands.checks.bot_has_permissions(
embed_links=True, send_messages=True, view_channel=True, add_reactions=True
)
@app_commands.checks.cooldown(1, 5, key=lambda i: (i.guild, i.user.id))
async def giveaway_start(
self,
interaction: Interaction, | duration: Transform[datetime.datetime, TimeTransformer], | 8 | 2023-11-09 15:00:15+00:00 | 16k |
Zjy0401/CoCoFormer | train.py | [
{
"identifier": "create_jsf_datasets",
"path": "dataset/jsf.py",
"snippet": "def create_jsf_datasets(dataset_root, max_seq, random_seq=True):\n\n train_root = os.path.join(dataset_root, \"train\")\n # val_root = os.path.join(dataset_root, \"val\")\n test_root = os.path.join(dataset_root, \"test... | import os
import csv
import shutil
import torch
import torch.nn as nn
import pickle
from thop import profile
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from torch.optim import Adam
from dataset.jsf import create_jsf_datasets
from model.CoCoFormer import CoCoformer, Discriminator, PureTransformer
from model.loss import SmoothCrossEntropyLoss
from utilities.constants import *
from utilities.device import get_device, use_cuda
from utilities.lr_scheduling import LrStepTracker, get_lr
from utilities.argument_funcs import parse_train_args, print_train_args, write_model_params
from utilities.run_model import train_epoch, train_with_adv, eval_model, get_metrics, train_with_pure_transformer, params
from tensorboardX import SummaryWriter | 10,976 |
# from dataset.e_piano import create_epiano_datasets, compute_epiano_accuracy, split_train_test
CSV_HEADER = ["Epoch", "Learn rate", "Avg Train loss", "Train Accuracy", "Avg Eval loss", "Eval accuracy"]
# Baseline is an untrained epoch that we evaluate as a baseline loss and accuracy
BASELINE_EPOCH = -1
# main
def main():
"""
----------
Author: Damon Gwinn
----------
Entry point. Trains a model specified by command line arguments
----------
"""
args = parse_train_args()
print_train_args(args)
if args.force_cpu:
use_cuda(False)
print("WARNING: Forced CPU usage, expect model to perform slower")
print("")
os.makedirs(args.output_dir, exist_ok=True)
##### Output prep #####
params_file = os.path.join(args.output_dir, "model_params.txt")
write_model_params(args, params_file)
weights_folder = os.path.join(args.output_dir, "weights")
os.makedirs(weights_folder, exist_ok=True)
results_folder = os.path.join(args.output_dir, "results")
os.makedirs(results_folder, exist_ok=True)
results_file = os.path.join(results_folder, "results.csv")
best_loss_file = os.path.join(results_folder, "best_loss_weights.pickle")
best_acc_file = os.path.join(results_folder, "best_acc_weights.pickle")
best_text = os.path.join(results_folder, "best_epochs.txt")
##### Tensorboard #####
if args.no_tensorboard:
tensorboard_summary = None
else:
tensorboad_dir = os.path.join(args.output_dir, "tensorboard")
tensorboard_summary = SummaryWriter(log_dir=tensorboad_dir)
##### Datasets #####
# train_dataset, val_dataset, test_dataset = create_epiano_datasets(args.input_dir, args.max_sequence)
train_dataset, test_dataset = create_jsf_datasets(args.input_dir, args.max_sequence)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size * len(args.gpu), num_workers=args.n_workers,
shuffle=True)
# val_loader = DataLoader(val_dataset, batch_size=args.batch_size, num_workers=args.n_workers)
test_loader = DataLoader(test_dataset, batch_size=args.batch_size, num_workers=args.n_workers)
##### read word2event event2word
f = open(args.word2event, 'rb')
word2event = pickle.load(f)
# reverse the vector event2word
event2word = {}
for key, val in word2event.items():
event2word[val] = key
if args.only_Transformer:
model = PureTransformer(n_layers=args.n_layers, num_heads=args.num_heads,
d_model=args.d_model, dim_feedforward=args.dim_feedforward, dropout=args.dropout,
max_sequence=args.max_sequence, rpr=args.rpr, word2event=word2event,
event2word=event2word)
else:
model = CoCoformer(n_layers=args.n_layers, num_heads=args.num_heads,
d_model=args.d_model, dim_feedforward=args.dim_feedforward, dropout=args.dropout,
max_sequence=args.max_sequence, rpr=args.rpr, word2event=word2event,
event2word=event2word)
model_disc = Discriminator()
if args.gpu[0] != -1:
model = torch.nn.DataParallel(model, device_ids=args.gpu)
model = model.cuda(device=args.gpu[0])
model_disc = torch.nn.DataParallel(model_disc, device_ids=args.gpu)
model_disc = model_disc.cuda(device=args.gpu[0])
|
# from dataset.e_piano import create_epiano_datasets, compute_epiano_accuracy, split_train_test
CSV_HEADER = ["Epoch", "Learn rate", "Avg Train loss", "Train Accuracy", "Avg Eval loss", "Eval accuracy"]
# Baseline is an untrained epoch that we evaluate as a baseline loss and accuracy
BASELINE_EPOCH = -1
# main
def main():
"""
----------
Author: Damon Gwinn
----------
Entry point. Trains a model specified by command line arguments
----------
"""
args = parse_train_args()
print_train_args(args)
if args.force_cpu:
use_cuda(False)
print("WARNING: Forced CPU usage, expect model to perform slower")
print("")
os.makedirs(args.output_dir, exist_ok=True)
##### Output prep #####
params_file = os.path.join(args.output_dir, "model_params.txt")
write_model_params(args, params_file)
weights_folder = os.path.join(args.output_dir, "weights")
os.makedirs(weights_folder, exist_ok=True)
results_folder = os.path.join(args.output_dir, "results")
os.makedirs(results_folder, exist_ok=True)
results_file = os.path.join(results_folder, "results.csv")
best_loss_file = os.path.join(results_folder, "best_loss_weights.pickle")
best_acc_file = os.path.join(results_folder, "best_acc_weights.pickle")
best_text = os.path.join(results_folder, "best_epochs.txt")
##### Tensorboard #####
if args.no_tensorboard:
tensorboard_summary = None
else:
tensorboad_dir = os.path.join(args.output_dir, "tensorboard")
tensorboard_summary = SummaryWriter(log_dir=tensorboad_dir)
##### Datasets #####
# train_dataset, val_dataset, test_dataset = create_epiano_datasets(args.input_dir, args.max_sequence)
train_dataset, test_dataset = create_jsf_datasets(args.input_dir, args.max_sequence)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size * len(args.gpu), num_workers=args.n_workers,
shuffle=True)
# val_loader = DataLoader(val_dataset, batch_size=args.batch_size, num_workers=args.n_workers)
test_loader = DataLoader(test_dataset, batch_size=args.batch_size, num_workers=args.n_workers)
##### read word2event event2word
f = open(args.word2event, 'rb')
word2event = pickle.load(f)
# reverse the vector event2word
event2word = {}
for key, val in word2event.items():
event2word[val] = key
if args.only_Transformer:
model = PureTransformer(n_layers=args.n_layers, num_heads=args.num_heads,
d_model=args.d_model, dim_feedforward=args.dim_feedforward, dropout=args.dropout,
max_sequence=args.max_sequence, rpr=args.rpr, word2event=word2event,
event2word=event2word)
else:
model = CoCoformer(n_layers=args.n_layers, num_heads=args.num_heads,
d_model=args.d_model, dim_feedforward=args.dim_feedforward, dropout=args.dropout,
max_sequence=args.max_sequence, rpr=args.rpr, word2event=word2event,
event2word=event2word)
model_disc = Discriminator()
if args.gpu[0] != -1:
model = torch.nn.DataParallel(model, device_ids=args.gpu)
model = model.cuda(device=args.gpu[0])
model_disc = torch.nn.DataParallel(model_disc, device_ids=args.gpu)
model_disc = model_disc.cuda(device=args.gpu[0])
| params(train_loader, model, model_disc) | 17 | 2023-11-01 08:33:08+00:00 | 16k |
tiendatnguyen-vision/Orbit-symmetrize | RotatedMNIST/LPS_orbit/emlp-pytorch/emlp_pytorch/reps/representation.py | [
{
"identifier": "Group",
"path": "RotatedMNIST/LPS_orbit/emlp-pytorch/emlp_pytorch/groups.py",
"snippet": "class Group(nn.Module):\n \"\"\" Abstract Group Object which new groups should inherit from. \"\"\"\n\n def __init__(self):\n super().__init__()\n self.lie_algebra = NotImplemen... | import math
import logging
import itertools
import torch
from functools import lru_cache as cache, reduce
from collections import defaultdict
from plum import dispatch
from torch import nn
from ..groups import Group
from .linear_operator_base import LinearOperator
from .linear_operators import ConcatLazy, I, lazify, densify, LazyJVP, LazyPerm, \
LazyDirectSum, LazyKron, LazyKronsum, lazy_direct_matmat, product
from .utils import orthogonal_complement, krylov_constraint_solve, get_device | 11,093 | if rb == 1:
return ra
if rb == 0:
return 0
if ra.concrete():
return SumRep(*(rb*[ra]))
return DeferredSumRep(*(rb*[ra]))
@dispatch
def mul_reps(ra: int, rb): # pylint: disable=function-redefined
""" Product of a scalar and a representation. """
return mul_reps(rb, ra) # pylint: disable=W1114:arguments-out-of-order
class ScalarRep(Rep):
""" The trivial representation of the group G. """
def __init__(self, G=None):
super().__init__()
self.G = G
self.is_permutation = True
def forward(self, G):
self.G = G
return self
def size(self):
return 1
def canonicalize(self):
return self, torch.zeros(1, dtype=torch.long)
def __repr__(self):
return "V⁰"
def t(self):
return self
def rho(self, M):
return torch.eye(1, device=self.G.device)
def drho(self, A):
return 0*torch.eye(1, device=self.G.device)
def __hash__(self):
return 0
def __eq__(self, other):
return isinstance(other, ScalarRep)
def __mul__(self, other):
if isinstance(other, int):
return super().__mul__(other)
return other
def __rmul__(self, other):
if isinstance(other, int):
return super().__rmul__(other)
return other
def concrete(self):
return True
class Base(Rep):
""" Base representation V of a group."""
def __init__(self, G=None):
super().__init__()
self.G = G
if G is not None:
self.is_permutation = G.is_permutation
def forward(self, G):
return self.__class__(G)
def rho(self, M):
if isinstance(self.G, Group) and isinstance(M, dict):
M = M[self.G]
return M
def drho(self, A):
if isinstance(self.G, Group) and isinstance(A, dict):
A = A[self.G]
return A
def size(self):
assert self.G is not None, f"must know G to find size for rep={self}"
return self.G.d
def __repr__(self):
return "V"
def __hash__(self):
return hash((type(self), self.G))
def __eq__(self, other):
return type(other) is type(self) and self.G == other.G
def __lt__(self, other):
if isinstance(other, Dual):
return True
return super().__lt__(other)
class Dual(Rep):
""" Dual representation V*, rho*, drho*."""
def __init__(self, rep):
super().__init__()
self.rep = rep
self.G = rep.G
if hasattr(rep, "is_permutation"):
self.is_permutation = rep.is_permutation
def forward(self, G):
return self.rep(G).t()
def rho(self, M):
rho = self.rep.rho(M)
| """ The base Representation class. """
class Rep(nn.Module):
""" The base Representation class. Representation objects formalize the vector space V
on which the group acts, the group representation matrix ρ(g), and the Lie Algebra
representation dρ(A) in a single object. Representations act as types for vectors coming
from V. These types can be manipulated and transformed with the built in operators
⊕,⊗,dual, as well as incorporating custom representations. Representation objects should
be immutable.
At minimum, new representations need to implement ``rho``, ``__str__``."""
def __init__(self):
super().__init__()
self.is_permutation = False
self._size = None
self.G = None
def rho(self, M):
""" Group representation of the matrix M of shape (d,d)"""
raise NotImplementedError
def drho(self, A):
""" Lie Algebra representation of the matrix A of shape (d,d)"""
In = torch.eye(A.size(0), dtype=A.dtype, device=A.device)
return LazyJVP(self.rho, In, A)
def forward(self, G):
""" Instantiate (nonconcrete) representation with a symmetry group (forward) """
raise NotImplementedError
def __str__(self):
return repr(self)
def __repr__(self):
raise NotImplementedError
def __eq__(self, other):
if type(self) is not type(other): # pylint: disable=unidiomatic-typecheck
return False
return self.__hash__() == other.__hash__()
def __hash__(self):
raise NotImplementedError
def size(self):
""" Dimension dim(V) of the representation """
if self._size is not None:
return self._size
if self.concrete() and isinstance(self.G, Group):
self._size = self.rho(self.G.sample()).size(-1)
return self._size
raise NotImplementedError
def canonicalize(self):
""" An optional method to convert the representation into a canonical form
in order to reuse equivalent solutions in the solver. Should return
both the canonically ordered representation, along with a permutation
which can be applied to vectors of the current representation to achieve
that ordering. """
# return canonicalized rep
return self, torch.arange(self.size())
def rho_dense(self, M):
""" A convenience function which returns rho(M) as a dense matrix."""
return densify(self.rho(M))
def drho_dense(self, A):
""" A convenience function which returns drho(A) as a dense matrix."""
return densify(self.drho(A))
def constraint_matrix(self):
""" Constructs the equivariance constrant matrix (lazily) by concatenating
the constraints (ρ(hᵢ)-I) for i=1,...M and dρ(Aₖ) for k=1,..,D from the generators
of the symmetry group. """
n = self.size()
constraints = []
constraints.extend([lazify(self.rho(h)).to(self.G.device)-I(n, device=self.G.device) \
for h in self.G.discrete_generators])
constraints.extend([lazify(self.drho(A)).to(self.G.device) for A in self.G.lie_algebra])
return ConcatLazy(constraints) if constraints else lazify(
torch.zeros((1, n), device=self.G.device))
solcache = {}
def equivariant_basis(self):
""" Computes the equivariant solution basis for the given representation of size N.
Canonicalizes problems and caches solutions for reuse. Output [Q (N,r)] """
if self == Scalar:
return torch.ones((1, 1), device=self.G.device)
canon_rep, perm = self.canonicalize()
invperm = torch.argsort(perm)
if canon_rep not in self.solcache:
logging.info("%r cache miss", canon_rep)
logging.info("Solving basis for %r%s", self,
f", for G={self.G}" if self.G is not None else "")
C_lazy = canon_rep.constraint_matrix()
if C_lazy.size(0)*C_lazy.size(1) > 3e7: # Too large to use SVD
result = krylov_constraint_solve(C_lazy)
else:
C_dense = C_lazy.to_dense()
result = orthogonal_complement(C_dense)
self.solcache[canon_rep] = result
return self.solcache[canon_rep][invperm]
def equivariant_projector(self):
""" Computes the (lazy) projection matrix P=QQᵀ that projects to the equivariant basis."""
Q = self.equivariant_basis()
Q_lazy = lazify(Q)
P = Q_lazy@Q_lazy.H()
return P
def concrete(self):
""" Concreteness """
return isinstance(self.G, Group)
def __add__(self, other):
""" Direct sum (⊕) of representations. """
if isinstance(other, int):
if other == 0:
return self
return self+other*Scalar
if both_concrete(self, other):
return SumRep(self, other)
return DeferredSumRep(self, other)
def __radd__(self, other):
if isinstance(other, int):
if other == 0:
return self
return other*Scalar+self
return NotImplemented
def __mul__(self, other):
""" Tensor sum (⊗) of representations. """
return mul_reps(self, other)
def __rmul__(self, other):
return mul_reps(other, self)
def __pow__(self, other):
""" Iterated tensor product. """
assert isinstance(other, int), \
f"Power only supported for integers, not {type(other)}"
assert other >= 0, f"Negative powers {other} not supported"
return reduce(lambda a, b: a*b, other*[self], Scalar)
def __rshift__(self, other):
""" Linear maps from self -> other """
return other*self.t()
def __lshift__(self, other):
""" Linear maps from other -> self """
return self*other.t()
def __lt__(self, other):
""" less than defined to disambiguate ordering multiple different representations.
Canonical ordering is determined first by Group, then by size, then by hash"""
if other == Scalar:
return False
try:
if self.G < other.G:
return True
if self.G > other.G:
return False
except (AttributeError, TypeError):
pass
if self.size() < other.size():
return True
if self.size() > other.size():
return False
return hash(self) < hash(other) # For sorting purposes only
def t(self):
""" Dual representation V*, rho*, drho*."""
if isinstance(self.G, Group) and self.G.is_orthogonal:
return self
return Dual(self)
@dispatch
def mul_reps(ra, rb: int):
""" Product of a scalar and a representation. """
if rb == 1:
return ra
if rb == 0:
return 0
if ra.concrete():
return SumRep(*(rb*[ra]))
return DeferredSumRep(*(rb*[ra]))
@dispatch
def mul_reps(ra: int, rb): # pylint: disable=function-redefined
""" Product of a scalar and a representation. """
return mul_reps(rb, ra) # pylint: disable=W1114:arguments-out-of-order
class ScalarRep(Rep):
""" The trivial representation of the group G. """
def __init__(self, G=None):
super().__init__()
self.G = G
self.is_permutation = True
def forward(self, G):
self.G = G
return self
def size(self):
return 1
def canonicalize(self):
return self, torch.zeros(1, dtype=torch.long)
def __repr__(self):
return "V⁰"
def t(self):
return self
def rho(self, M):
return torch.eye(1, device=self.G.device)
def drho(self, A):
return 0*torch.eye(1, device=self.G.device)
def __hash__(self):
return 0
def __eq__(self, other):
return isinstance(other, ScalarRep)
def __mul__(self, other):
if isinstance(other, int):
return super().__mul__(other)
return other
def __rmul__(self, other):
if isinstance(other, int):
return super().__rmul__(other)
return other
def concrete(self):
return True
class Base(Rep):
""" Base representation V of a group."""
def __init__(self, G=None):
super().__init__()
self.G = G
if G is not None:
self.is_permutation = G.is_permutation
def forward(self, G):
return self.__class__(G)
def rho(self, M):
if isinstance(self.G, Group) and isinstance(M, dict):
M = M[self.G]
return M
def drho(self, A):
if isinstance(self.G, Group) and isinstance(A, dict):
A = A[self.G]
return A
def size(self):
assert self.G is not None, f"must know G to find size for rep={self}"
return self.G.d
def __repr__(self):
return "V"
def __hash__(self):
return hash((type(self), self.G))
def __eq__(self, other):
return type(other) is type(self) and self.G == other.G
def __lt__(self, other):
if isinstance(other, Dual):
return True
return super().__lt__(other)
class Dual(Rep):
""" Dual representation V*, rho*, drho*."""
def __init__(self, rep):
super().__init__()
self.rep = rep
self.G = rep.G
if hasattr(rep, "is_permutation"):
self.is_permutation = rep.is_permutation
def forward(self, G):
return self.rep(G).t()
def rho(self, M):
rho = self.rep.rho(M) | rhoinvt = rho.invt() if isinstance(rho, LinearOperator) else torch.linalg.inv(rho).t() | 1 | 2023-11-01 07:19:02+00:00 | 16k |
mbreuss/consistency_trajectory_models_toy_task | ctm_train.py | [
{
"identifier": "ConsistencyTrajectoryModel",
"path": "ctm/ctm.py",
"snippet": "class ConsistencyTrajectoryModel(nn.Module):\n\n def __init__(\n self, \n data_dim: int,\n cond_dim: int,\n sampler_type: str,\n sigma_data: float,\n sigma... | from tqdm import tqdm
from ctm.ctm import ConsistencyTrajectoryModel
from ctm.toy_tasks.data_generator import DataGenerator
from ctm.visualization.vis_utils import plot_main_figure | 11,448 |
"""
Discrete consistency distillation training of the consistency model on a toy task.
We train a diffusion model and the consistency model at the same time and iteratively
update the weights of the consistency model and the diffusion model.
"""
if __name__ == "__main__":
device = 'cpu'
n_sampling_steps = 10
use_pretraining = True
|
"""
Discrete consistency distillation training of the consistency model on a toy task.
We train a diffusion model and the consistency model at the same time and iteratively
update the weights of the consistency model and the diffusion model.
"""
if __name__ == "__main__":
device = 'cpu'
n_sampling_steps = 10
use_pretraining = True | cm = ConsistencyTrajectoryModel( | 0 | 2023-11-07 15:30:11+00:00 | 16k |
awslabs/optimizing-multitask-training-through-dynamic-pipelines | dynapipe/pipe/data_loader.py | [
{
"identifier": "ProfileBasedCostModelWithRC",
"path": "dynapipe/data_opt/cost_models.py",
"snippet": "class ProfileBasedCostModelWithRC(object):\n \"\"\"\n Wrapper class for multiple ProfileBasedCostModel objects, one for each\n tensor parallel degree and recomputation method.\n \"\"\"\n\n ... | import json
import logging
import multiprocessing as mp
import os
import time
import traceback
import torch
import pickle
from dataclasses import dataclass, field, fields
from queue import Empty
from typing import List, Optional
from torch.utils.data import DataLoader as PTDataLoader
from dynapipe.data_opt.cost_models import ProfileBasedCostModelWithRC
from dynapipe.data_opt.optimizer import DataAssignmentOptimizer
from dynapipe.model import DynaPipeCluster, TransformerModelSpec
from dynapipe.pipe.instructions import (
deserialize_list_of_eps,
serialize_list_of_eps,
)
from dynapipe.schedule_opt.execution_planner import ExecutionPlanner
from dynapipe.utils.logger import create_logger, logger
from .kv_redis import RedisKVStore
from .utils import validate_device_assignment | 12,407 | return kv_store, host, port
def _checked_delete_key(kv_store: RedisKVStore, key: str, logger=None):
result = kv_store.delete_key(key)
if not result:
raise RuntimeError(
"Internal error: failed to delete key " "{}.".format(key)
)
if logger is not None:
logger.debug("Deleted key: {}".format(key))
def _get_from_shared_kv_store(
kv_store: RedisKVStore,
key: str,
reader_idx: int,
n_total_readers: int,
decode: bool = True,
logger=None,
):
reader_count_key = key + "_rc"
reader_ack_key = key + "_r{}_ack".format(reader_idx)
# wait for reader ack
if logger is not None:
logger.debug("Waiting for reader ack key: {}".format(reader_ack_key))
kv_store.get(reader_ack_key)
if logger is not None:
logger.debug(
"Got reader ack key: {}, waiting for data key: {}".format(
reader_ack_key, key
)
)
data = kv_store.get(key)
if logger is not None:
logger.debug("Removing reader ack key: {}".format(reader_ack_key))
# remove reader ack
_checked_delete_key(kv_store, reader_ack_key, logger=logger)
# get reader count
reader_count = kv_store.add(reader_count_key, 1)
if reader_count == n_total_readers:
if logger is not None:
logger.debug(
"Last reader, reset reader count: {}".format(reader_count_key)
)
# reset reader count
result_readers = kv_store.add(reader_count_key, -n_total_readers)
assert result_readers == 0
if logger is not None:
logger.debug("Last reader, remove data key: {}".format(key))
# remove data key
_checked_delete_key(kv_store, key, logger=logger)
if logger is not None:
logger.debug("Last reader, set ack key: {}".format(key + "_ack"))
# set all reader ack keys
keys_to_reset = [
key + "_r{}_ack".format(i) for i in range(n_total_readers)
]
if logger is not None:
logger.debug("Last reader, reset keys: {}".format(keys_to_reset))
for reset_key in keys_to_reset:
val = kv_store.add(reset_key, 1)
# make sure the key is set
got_val = int(kv_store.get(reset_key).decode())
if not val == got_val:
raise RuntimeError(
"Failed to set reader ack key: {}".format(reset_key)
)
if logger is not None:
logger.debug("Set reader ack key: {}".format(reset_key))
# set data ack key
kv_store.add(key + "_ack", 1)
if decode:
return data.decode()
return data
def _put_to_shared_kv_store(
kv_store: RedisKVStore, key: str, data, logger=None
):
# put execution plan into local kv store
ack_key = key + "_ack"
if logger is not None:
logger.debug("Wait for data ack key: {}".format(ack_key))
# wait for ack key
kv_store.get(ack_key)
# remove ack key
_checked_delete_key(kv_store, ack_key, logger=logger)
if logger is not None:
logger.debug("Set data key: {}".format(key))
# set data key
kv_store.set(key, data)
@dataclass
class WorkerData:
round_seqlen_multiple: Optional[int] = None
logger: Optional[logging.Logger] = None
kv_store: Optional[RedisKVStore] = None
processed_batches: Optional[int] = None
kv_buffer_size: Optional[int] = None
seqlen_offset: Optional[int] = 0
def check_initialized(self):
cls_fields = fields(self.__class__)
for fld in cls_fields:
if getattr(self, fld.name) is None:
raise RuntimeError(
"Worker data not initialized: {}".format(fld.name)
)
@dataclass
class PreprocessingWorkerData(WorkerData):
# required at initialization:
node_rank: Optional[int] = None
profile_path: Optional[str] = None
# filled later in worker init:
dataopt: Optional[DataAssignmentOptimizer] = None
| # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
MANAGER_PROCESS_TIMEOUT = 1
RECEIVER_PROCESS_TIMEOUT = 1
KVSTORE_TIMEOUT = 1800 # 30 minutes
# ONLY USED FOR DEBUG PURPOSES
DEBUG_USE_DUMMY_EP = False
DEBUG_DUMP_EP_STATS = os.getenv(
"DYNAPIPE_DEBUG_DUMP_EP_STATS", "False"
).lower() in ("true", "1", "t")
DEBUG_DUMP_EP_PREFIX = os.environ.get("DYNAPIPE_DEBUG_DUMP_EP_PREFIX", None)
if DEBUG_DUMP_EP_STATS and DEBUG_DUMP_EP_PREFIX is None:
raise ValueError(
"DYNAPIPE_DEBUG_DUMP_EP_PREFIX must be set if "
"DYNAPIPE_DEBUG_DUMP_EP_STATS is set."
)
_kvstore_handle = None
def _init_kv_store(is_master, logger=None):
host = os.environ.get("DYNAPIPE_KV_HOST", "localhost")
port = os.environ.get("DYNAPIPE_KV_PORT", 29500)
if logger is not None:
logger.debug(
"Init kv store, is_master: {}, host: {}, port: {}".format(
is_master, host, port
)
)
# kv_store = torch.distributed.TCPStore(
# "127.0.0.1",
# port,
# is_master=is_master,
# timeout=timedelta(seconds=KVSTORE_TIMEOUT),
# )
kv_store = RedisKVStore(host, port, is_master=is_master)
return kv_store, host, port
def _checked_delete_key(kv_store: RedisKVStore, key: str, logger=None):
result = kv_store.delete_key(key)
if not result:
raise RuntimeError(
"Internal error: failed to delete key " "{}.".format(key)
)
if logger is not None:
logger.debug("Deleted key: {}".format(key))
def _get_from_shared_kv_store(
kv_store: RedisKVStore,
key: str,
reader_idx: int,
n_total_readers: int,
decode: bool = True,
logger=None,
):
reader_count_key = key + "_rc"
reader_ack_key = key + "_r{}_ack".format(reader_idx)
# wait for reader ack
if logger is not None:
logger.debug("Waiting for reader ack key: {}".format(reader_ack_key))
kv_store.get(reader_ack_key)
if logger is not None:
logger.debug(
"Got reader ack key: {}, waiting for data key: {}".format(
reader_ack_key, key
)
)
data = kv_store.get(key)
if logger is not None:
logger.debug("Removing reader ack key: {}".format(reader_ack_key))
# remove reader ack
_checked_delete_key(kv_store, reader_ack_key, logger=logger)
# get reader count
reader_count = kv_store.add(reader_count_key, 1)
if reader_count == n_total_readers:
if logger is not None:
logger.debug(
"Last reader, reset reader count: {}".format(reader_count_key)
)
# reset reader count
result_readers = kv_store.add(reader_count_key, -n_total_readers)
assert result_readers == 0
if logger is not None:
logger.debug("Last reader, remove data key: {}".format(key))
# remove data key
_checked_delete_key(kv_store, key, logger=logger)
if logger is not None:
logger.debug("Last reader, set ack key: {}".format(key + "_ack"))
# set all reader ack keys
keys_to_reset = [
key + "_r{}_ack".format(i) for i in range(n_total_readers)
]
if logger is not None:
logger.debug("Last reader, reset keys: {}".format(keys_to_reset))
for reset_key in keys_to_reset:
val = kv_store.add(reset_key, 1)
# make sure the key is set
got_val = int(kv_store.get(reset_key).decode())
if not val == got_val:
raise RuntimeError(
"Failed to set reader ack key: {}".format(reset_key)
)
if logger is not None:
logger.debug("Set reader ack key: {}".format(reset_key))
# set data ack key
kv_store.add(key + "_ack", 1)
if decode:
return data.decode()
return data
def _put_to_shared_kv_store(
kv_store: RedisKVStore, key: str, data, logger=None
):
# put execution plan into local kv store
ack_key = key + "_ack"
if logger is not None:
logger.debug("Wait for data ack key: {}".format(ack_key))
# wait for ack key
kv_store.get(ack_key)
# remove ack key
_checked_delete_key(kv_store, ack_key, logger=logger)
if logger is not None:
logger.debug("Set data key: {}".format(key))
# set data key
kv_store.set(key, data)
@dataclass
class WorkerData:
round_seqlen_multiple: Optional[int] = None
logger: Optional[logging.Logger] = None
kv_store: Optional[RedisKVStore] = None
processed_batches: Optional[int] = None
kv_buffer_size: Optional[int] = None
seqlen_offset: Optional[int] = 0
def check_initialized(self):
cls_fields = fields(self.__class__)
for fld in cls_fields:
if getattr(self, fld.name) is None:
raise RuntimeError(
"Worker data not initialized: {}".format(fld.name)
)
@dataclass
class PreprocessingWorkerData(WorkerData):
# required at initialization:
node_rank: Optional[int] = None
profile_path: Optional[str] = None
# filled later in worker init:
dataopt: Optional[DataAssignmentOptimizer] = None | exec_planner: Optional[ExecutionPlanner] = None | 6 | 2023-11-08 07:58:20+00:00 | 16k |
SqueezeAILab/LLMCompiler | src/llm_compiler/llm_compiler.py | [
{
"identifier": "AsyncStatsCallbackHandler",
"path": "src/callbacks/callbacks.py",
"snippet": "class AsyncStatsCallbackHandler(AsyncCallbackHandler):\n \"\"\"Collect useful stats about the run.\n Add more stats as needed.\"\"\"\n\n def __init__(self, stream: bool = False) -> None:\n supe... | import asyncio
from typing import Any, Dict, List, Mapping, Optional, Sequence, Union, cast
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.llms import BaseLLM
from langchain.prompts.base import StringPromptValue
from src.callbacks.callbacks import AsyncStatsCallbackHandler
from src.chains.chain import Chain
from src.llm_compiler.constants import JOINNER_REPLAN
from src.llm_compiler.planner import Planner
from src.llm_compiler.task_fetching_unit import Task, TaskFetchingUnit
from src.tools.base import StructuredTool, Tool
from src.utils.logger_utils import log | 12,299 | return stats
def reset_all_stats(self):
if self.planner_callback:
self.planner_callback.reset()
if self.executor_callback:
self.executor_callback.reset()
@property
def input_keys(self) -> List[str]:
return [self.input_key]
@property
def output_keys(self) -> List[str]:
return [self.output_key]
# TODO(sk): move all join related functions to a separate class
def _parse_joinner_output(self, raw_answer: str) -> str:
"""We expect the joinner output format to be:
```
Thought: xxx
Action: Finish/Replan(yyy)
```
Returns:
thought (xxx)
answer (yyy)
is_replan (True/False)
"""
thought, answer, is_replan = "", "", False # default values
raw_answers = raw_answer.split("\n")
for ans in raw_answers:
if ans.startswith("Action:"):
answer = ans[ans.find("(") + 1 : ans.find(")")]
is_replan = JOINNER_REPLAN in ans
elif ans.startswith("Thought:"):
thought = ans.split("Thought:")[1].strip()
return thought, answer, is_replan
def _generate_context_for_replanner(
self, tasks: Mapping[int, Task], joinner_thought: str
) -> str:
"""Formatted like this:
```
1. action 1
Observation: xxx
2. action 2
Observation: yyy
...
Thought: joinner_thought
```
"""
previous_plan_and_observations = "\n".join(
[
task.get_though_action_observation(
include_action=True, include_action_idx=True
)
for task in tasks.values()
if not task.is_join
]
)
joinner_thought = f"Thought: {joinner_thought}"
context = "\n\n".join([previous_plan_and_observations, joinner_thought])
return context
def _format_contexts(self, contexts: Sequence[str]) -> str:
"""contexts is a list of context
each context is formatted as the description of _generate_context_for_replanner
"""
formatted_contexts = ""
for context in contexts:
formatted_contexts += f"Previous Plan:\n\n{context}\n\n"
formatted_contexts += "Current Plan:\n\n"
return formatted_contexts
async def join(
self, input_query: str, agent_scratchpad: str, is_final: bool
) -> str:
if is_final:
joinner_prompt = self.joinner_prompt_final
else:
joinner_prompt = self.joinner_prompt
prompt = (
f"{joinner_prompt}\n" # Instructions and examples
f"Question: {input_query}\n\n" # User input query
f"{agent_scratchpad}\n" # T-A-O
# "---\n"
)
log("Joining prompt:\n", prompt, block=True)
response = await self.agent.arun(
prompt, callbacks=[self.executor_callback] if self.benchmark else None
)
raw_answer = cast(str, response.generations[0][0].message.content)
log("Question: \n", input_query, block=True)
log("Raw Answer: \n", raw_answer, block=True)
thought, answer, is_replan = self._parse_joinner_output(raw_answer)
if is_final:
# If final, we don't need to replan
is_replan = False
return thought, answer, is_replan
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
):
raise NotImplementedError("LLMCompiler is async only.")
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
contexts = []
joinner_thought = ""
agent_scratchpad = ""
for i in range(self.max_replans):
is_first_iter = i == 0
is_final_iter = i == self.max_replans - 1
|
class LLMCompilerAgent:
"""Self defined agent for LLM Compiler."""
def __init__(self, llm: BaseLLM) -> None:
self.llm = llm
async def arun(self, prompt: str, callbacks=None) -> str:
return await self.llm.agenerate_prompt(
prompts=[StringPromptValue(text=prompt)],
stop=None,
callbacks=callbacks,
)
class LLMCompiler(Chain, extra="allow"):
"""LLMCompuler Engine."""
"""The step container to use."""
input_key: str = "input"
output_key: str = "output"
def __init__(
self,
tools: Sequence[Union[Tool, StructuredTool]],
planner_llm: BaseLLM,
planner_example_prompt: str,
planner_example_prompt_replan: Optional[str],
planner_stop: Optional[list[str]],
planner_stream: bool,
agent_llm: BaseLLM,
joinner_prompt: str,
joinner_prompt_final: Optional[str],
max_replans: int,
benchmark: bool,
**kwargs,
) -> None:
"""
Args:
tools: List of tools to use.
max_replans: Maximum number of replans to do.
benchmark: Whether to collect benchmark stats.
Planner Args:
planner_llm: LLM to use for planning.
planner_example_prompt: Example prompt for planning.
planner_example_prompt_replan: Example prompt for replanning.
Assign this if you want to use different example prompt for replanning.
If not assigned, default to `planner_example_prompt`.
planner_stop: Stop tokens for planning.
planner_stream: Whether to stream the planning.
Agent Args:
agent_llm: LLM to use for agent.
joinner_prompt: Prompt to use for joinner.
joinner_prompt_final: Prompt to use for joinner at the final replanning iter.
If not assigned, default to `joinner_prompt`.
"""
super().__init__(**kwargs)
if not planner_example_prompt_replan:
log(
"Replan example prompt not specified, using the same prompt as the planner."
)
planner_example_prompt_replan = planner_example_prompt
self.planner = Planner(
llm=planner_llm,
example_prompt=planner_example_prompt,
example_prompt_replan=planner_example_prompt_replan,
tools=tools,
stop=planner_stop,
)
self.agent = LLMCompilerAgent(agent_llm)
self.joinner_prompt = joinner_prompt
self.joinner_prompt_final = joinner_prompt_final or joinner_prompt
self.planner_stream = planner_stream
self.max_replans = max_replans
# callbacks
self.benchmark = benchmark
if benchmark:
self.planner_callback = AsyncStatsCallbackHandler(stream=planner_stream)
self.executor_callback = AsyncStatsCallbackHandler(stream=False)
else:
self.planner_callback = None
self.executor_callback = None
def get_all_stats(self):
stats = {}
if self.benchmark:
stats["planner"] = self.planner_callback.get_stats()
stats["executor"] = self.executor_callback.get_stats()
stats["total"] = {
k: v + stats["executor"][k] for k, v in stats["planner"].items()
}
return stats
def reset_all_stats(self):
if self.planner_callback:
self.planner_callback.reset()
if self.executor_callback:
self.executor_callback.reset()
@property
def input_keys(self) -> List[str]:
return [self.input_key]
@property
def output_keys(self) -> List[str]:
return [self.output_key]
# TODO(sk): move all join related functions to a separate class
def _parse_joinner_output(self, raw_answer: str) -> str:
"""We expect the joinner output format to be:
```
Thought: xxx
Action: Finish/Replan(yyy)
```
Returns:
thought (xxx)
answer (yyy)
is_replan (True/False)
"""
thought, answer, is_replan = "", "", False # default values
raw_answers = raw_answer.split("\n")
for ans in raw_answers:
if ans.startswith("Action:"):
answer = ans[ans.find("(") + 1 : ans.find(")")]
is_replan = JOINNER_REPLAN in ans
elif ans.startswith("Thought:"):
thought = ans.split("Thought:")[1].strip()
return thought, answer, is_replan
def _generate_context_for_replanner(
self, tasks: Mapping[int, Task], joinner_thought: str
) -> str:
"""Formatted like this:
```
1. action 1
Observation: xxx
2. action 2
Observation: yyy
...
Thought: joinner_thought
```
"""
previous_plan_and_observations = "\n".join(
[
task.get_though_action_observation(
include_action=True, include_action_idx=True
)
for task in tasks.values()
if not task.is_join
]
)
joinner_thought = f"Thought: {joinner_thought}"
context = "\n\n".join([previous_plan_and_observations, joinner_thought])
return context
def _format_contexts(self, contexts: Sequence[str]) -> str:
"""contexts is a list of context
each context is formatted as the description of _generate_context_for_replanner
"""
formatted_contexts = ""
for context in contexts:
formatted_contexts += f"Previous Plan:\n\n{context}\n\n"
formatted_contexts += "Current Plan:\n\n"
return formatted_contexts
async def join(
self, input_query: str, agent_scratchpad: str, is_final: bool
) -> str:
if is_final:
joinner_prompt = self.joinner_prompt_final
else:
joinner_prompt = self.joinner_prompt
prompt = (
f"{joinner_prompt}\n" # Instructions and examples
f"Question: {input_query}\n\n" # User input query
f"{agent_scratchpad}\n" # T-A-O
# "---\n"
)
log("Joining prompt:\n", prompt, block=True)
response = await self.agent.arun(
prompt, callbacks=[self.executor_callback] if self.benchmark else None
)
raw_answer = cast(str, response.generations[0][0].message.content)
log("Question: \n", input_query, block=True)
log("Raw Answer: \n", raw_answer, block=True)
thought, answer, is_replan = self._parse_joinner_output(raw_answer)
if is_final:
# If final, we don't need to replan
is_replan = False
return thought, answer, is_replan
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
):
raise NotImplementedError("LLMCompiler is async only.")
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
contexts = []
joinner_thought = ""
agent_scratchpad = ""
for i in range(self.max_replans):
is_first_iter = i == 0
is_final_iter = i == self.max_replans - 1
| task_fetching_unit = TaskFetchingUnit() | 5 | 2023-12-06 21:12:54+00:00 | 16k |
bytedance/ImageDream | threestudio/models/geometry/tetrahedra_sdf_grid.py | [
{
"identifier": "BaseExplicitGeometry",
"path": "threestudio/models/geometry/base.py",
"snippet": "class BaseExplicitGeometry(BaseGeometry):\n @dataclass\n class Config(BaseGeometry.Config):\n radius: float = 1.0\n\n cfg: Config\n\n def configure(self) -> None:\n self.bbox: Flo... | from dataclasses import dataclass, field
from threestudio.models.geometry.base import (
BaseExplicitGeometry,
BaseGeometry,
contract_to_unisphere,
)
from threestudio.models.geometry.implicit_sdf import ImplicitSDF
from threestudio.models.geometry.implicit_volume import ImplicitVolume
from threestudio.models.isosurface import MarchingTetrahedraHelper
from threestudio.models.mesh import Mesh
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.ops import scale_tensor
from threestudio.utils.typing import *
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio | 14,365 | nn.Parameter(
torch.zeros_like(self.isosurface_helper.grid_vertices)
),
)
else:
self.deformation = None
else:
self.register_buffer(
"sdf",
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
),
)
if self.cfg.isosurface_deformable_grid:
self.register_buffer(
"deformation",
torch.zeros_like(self.isosurface_helper.grid_vertices),
)
else:
self.deformation = None
if not self.cfg.geometry_only:
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
self.mesh: Optional[Mesh] = None
def initialize_shape(self) -> None:
raise NotImplementedError
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
return {"features": features}
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "TetrahedraSDFGrid":
if isinstance(other, TetrahedraSDFGrid):
instance = TetrahedraSDFGrid(cfg, **kwargs)
assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution
instance.isosurface_bbox = other.isosurface_bbox.clone()
instance.sdf.data = other.sdf.data.clone()
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert (
instance.deformation is not None and other.deformation is not None
)
instance.deformation.data = other.deformation.data.clone()
if (
not instance.cfg.geometry_only
and not other.cfg.geometry_only
and copy_net
):
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
elif isinstance(other, ImplicitVolume):
instance = TetrahedraSDFGrid(cfg, **kwargs)
if other.cfg.isosurface_method != "mt":
other.cfg.isosurface_method = "mt"
threestudio.warn(
f"Override isosurface_method of the source geometry to 'mt'"
)
if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:
other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution
threestudio.warn(
f"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}"
)
mesh = other.isosurface()
instance.isosurface_bbox = mesh.extras["bbox"]
instance.sdf.data = (
mesh.extras["grid_level"].to(instance.sdf.data).clamp(-1, 1)
)
if not instance.cfg.geometry_only and copy_net:
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
|
@threestudio.register("tetrahedra-sdf-grid")
class TetrahedraSDFGrid(BaseExplicitGeometry):
@dataclass
class Config(BaseExplicitGeometry.Config):
isosurface_resolution: int = 128
isosurface_deformable_grid: bool = True
isosurface_remove_outliers: bool = False
isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01
n_input_dims: int = 3
n_feature_dims: int = 3
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
shape_init: Optional[str] = None
shape_init_params: Optional[Any] = None
force_shape_init: bool = False
geometry_only: bool = False
fix_geometry: bool = False
cfg: Config
def configure(self) -> None:
super().configure()
# this should be saved to state_dict, register as buffer
self.isosurface_bbox: Float[Tensor, "2 3"]
self.register_buffer("isosurface_bbox", self.bbox.clone())
self.isosurface_helper = MarchingTetrahedraHelper(
self.cfg.isosurface_resolution,
f"load/tets/{self.cfg.isosurface_resolution}_tets.npz",
)
self.sdf: Float[Tensor, "Nv 1"]
self.deformation: Optional[Float[Tensor, "Nv 3"]]
if not self.cfg.fix_geometry:
self.register_parameter(
"sdf",
nn.Parameter(
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
)
),
)
if self.cfg.isosurface_deformable_grid:
self.register_parameter(
"deformation",
nn.Parameter(
torch.zeros_like(self.isosurface_helper.grid_vertices)
),
)
else:
self.deformation = None
else:
self.register_buffer(
"sdf",
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
),
)
if self.cfg.isosurface_deformable_grid:
self.register_buffer(
"deformation",
torch.zeros_like(self.isosurface_helper.grid_vertices),
)
else:
self.deformation = None
if not self.cfg.geometry_only:
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
self.mesh: Optional[Mesh] = None
def initialize_shape(self) -> None:
raise NotImplementedError
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
return {"features": features}
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "TetrahedraSDFGrid":
if isinstance(other, TetrahedraSDFGrid):
instance = TetrahedraSDFGrid(cfg, **kwargs)
assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution
instance.isosurface_bbox = other.isosurface_bbox.clone()
instance.sdf.data = other.sdf.data.clone()
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert (
instance.deformation is not None and other.deformation is not None
)
instance.deformation.data = other.deformation.data.clone()
if (
not instance.cfg.geometry_only
and not other.cfg.geometry_only
and copy_net
):
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
elif isinstance(other, ImplicitVolume):
instance = TetrahedraSDFGrid(cfg, **kwargs)
if other.cfg.isosurface_method != "mt":
other.cfg.isosurface_method = "mt"
threestudio.warn(
f"Override isosurface_method of the source geometry to 'mt'"
)
if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:
other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution
threestudio.warn(
f"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}"
)
mesh = other.isosurface()
instance.isosurface_bbox = mesh.extras["bbox"]
instance.sdf.data = (
mesh.extras["grid_level"].to(instance.sdf.data).clamp(-1, 1)
)
if not instance.cfg.geometry_only and copy_net:
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance | elif isinstance(other, ImplicitSDF): | 3 | 2023-12-13 21:09:37+00:00 | 16k |
allenai/unified-io-2 | t5x/models_test.py | [
{
"identifier": "decoding",
"path": "t5x/decoding.py",
"snippet": "NEG_INF = np.array(-1.0e7)\nMIN_TEMPERATURE = np.array(1e-4)\nclass DecodingState:\nclass SamplingLoopState:\nclass BeamState:\ndef _is_tracer(value: Any):\ndef temperature_sample(\n inputs: jnp.ndarray,\n cache: Mapping[str, jnp.n... | import functools
import flax
import jax
import jax.numpy as jnp
import numpy as np
import t5.data.tasks # pylint:disable=unused-import
import tensorflow as tf
from unittest import mock
from absl import logging
from absl.testing import absltest
from absl.testing import parameterized
from flax import traverse_util
from t5x import decoding
from t5x import models
from t5x import partitioning
from t5x import test_utils
from t5x import trainer as trainer_lib
from t5x import utils | 12,627 |
@parameterized.named_parameters(
dict(testcase_name='no_force_decoding', prompt_with_targets=False),
dict(testcase_name='force_decoding', prompt_with_targets=True),
)
def test_prompt_with_targets(self, prompt_with_targets):
batch_size, encoder_len, max_decode_len, emb_dim = 2, 3, 4, 5
batch = {
'encoder_input_tokens':
np.zeros((batch_size, encoder_len), dtype=np.int32),
'decoder_input_tokens':
np.full([batch_size, max_decode_len], 2, dtype=np.int32)
}
# These dummy logits represent the probability distribution where all the
# probability mass is in one item (i.e., degenerate distribution). For
# batch element 0, it is vocabulary index 3.
# We test `_predict_step` to avoid having to define a task and its
# vocabulary.
dummy_logits = jnp.expand_dims(
jnp.array([[-1e7, -1e7, -1e7, 0, -1e7], [-1e7, -1e7, -1e7, -1e7, 0]]),
axis=1)
mock_decode_fn = mock.Mock()
mock_decode_fn.return_value = (np.full([batch_size, max_decode_len, 1],
3,
dtype=np.int32),
np.full([batch_size, 1],
1.0,
dtype=np.float32))
class MockModule:
def __init__(self):
self.dtype = jnp.float32
def apply(self, *args, method=None, **kwargs):
del args, kwargs
if method is None: # use for module.`__call__`
return (dummy_logits, {'cache': {}})
else:
return method()
def encode(self):
return jnp.zeros((batch_size, encoder_len, emb_dim))
def decode(self):
return (dummy_logits, {'cache': {}})
def mock_init(self):
self.module = MockModule()
self.module.scan_layers = False
self._input_vocabulary = mock.Mock(eos_id=1)
self._output_vocabulary = mock.Mock(eos_id=1)
self._decode_fn = mock_decode_fn
with mock.patch.object(
models.EncoderDecoderModel, '__init__', new=mock_init):
model = models.EncoderDecoderModel()
model.predict_batch_with_aux({},
batch,
prompt_with_targets=prompt_with_targets)
if prompt_with_targets:
expected_inputs = batch['decoder_input_tokens']
else:
expected_inputs = np.zeros([batch_size, max_decode_len], dtype=np.int32)
assert mock_decode_fn.call_count == 1
# Look at the kwargs call list for inputs, assert_called_with doesn't
# work well with np.array comparison.
np.testing.assert_array_equal(mock_decode_fn.mock_calls[0][2]['inputs'],
expected_inputs)
def test_predict_batch_loop_and_caches_are_equal(self):
vocab_size = 50
lengths = np.array([[2], [3]])
batch_size, beam_size, encoder_len, max_decode_len = 2, 2, 3, 7
batch = {
'encoder_input_tokens':
np.zeros((batch_size, encoder_len), dtype=np.int32),
'decoder_target_tokens':
np.zeros((batch_size, encoder_len), dtype=np.int32),
'decoder_input_tokens':
np.concatenate(
[
np.expand_dims(
np.concatenate(
[[0],
np.arange(9, 9 + lengths[0][0], dtype=np.int32),
np.zeros((max_decode_len - lengths[0][0] - 1),
dtype=np.int32)]),
axis=0), # First element
np.expand_dims(
np.concatenate(
[[0],
np.arange(3, 3 + lengths[1][0], dtype=np.int32),
np.zeros((max_decode_len - lengths[1][0] - 1),
dtype=np.int32)]),
axis=0) # Second element
],
axis=0),
}
model = test_utils.get_t5_test_model(vocab_size=50)
module = model.module
params = module.init(
jax.random.PRNGKey(0),
jnp.ones((batch_size, encoder_len)),
jnp.ones((batch_size, max_decode_len)),
jnp.ones((batch_size, max_decode_len)),
enable_dropout=False)['params']
def mock_init(self):
self.module = module
# Set the EOS token to be larger then the vocabulary size. This forces the
# model to decode all the way to `max_decode_length`, allowing us to test
# behavior when one element reaches the end before the others.
self._output_vocabulary = mock.Mock(eos_id=vocab_size + 12)
|
# Copyright 2022 The T5X Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for t5x.models."""
# Parse absl flags test_srcdir and test_tmpdir.
jax.config.parse_flags_with_absl()
PartitionSpec = partitioning.PartitionSpec
class ModelsTest(parameterized.TestCase):
def test_remove_prefix(self):
sequences = np.array([[1, 2, 3, 4, 5, 6, 7, 0], [6, 7, 8, 9, 10, 11, 0, 0]])
prefix_lengths = np.array([2, 4])
expected = [[3, 4, 5, 6, 7, 0, 0, 0], [10, 11, 0, 0, 0, 0, 0, 0]]
remove_prefix = jax.jit(models.remove_prefix)
actual = remove_prefix(sequences, prefix_lengths)
np.testing.assert_array_equal(actual, expected)
def test_remove_prefix_zero_len_prefix(self):
sequences = np.array([[1, 2, 3, 4, 5, 6, 7, 0], [6, 7, 8, 9, 10, 11, 0, 0]])
prefix_lengths = np.array([0, 0])
remove_prefix = jax.jit(models.remove_prefix)
actual = remove_prefix(sequences, prefix_lengths)
# The expected output is the original sequences.
np.testing.assert_array_equal(actual, sequences)
BATCH_SIZE, ENCODER_LEN, MAX_DECODE_LEN, EMBED_DIM = 2, 3, 4, 5
class EncoderDecoderModelTest(parameterized.TestCase):
@parameterized.named_parameters(
dict(
testcase_name='no_types',
shapes={
'encoder_input_tokens': [1, 512],
'decoder_input_tokens': [1, 62]
},
types=None),
dict(
testcase_name='int32',
shapes={
'encoder_input_tokens': [1, 512],
'decoder_input_tokens': [1, 62]
},
types={
'encoder_input_tokens': jnp.int32,
'decoder_input_tokens': jnp.int32
}),
dict(
testcase_name='float32',
shapes={
'encoder_input_tokens': [1, 512],
'decoder_input_tokens': [1, 62],
'encoder_positions': [1, 512],
'decoder_positions': [1, 62],
},
types={
'encoder_input_tokens': jnp.int32,
'decoder_input_tokens': jnp.int32,
'encoder_positions': jnp.int32,
'decoder_positions': jnp.int32
}),
dict(
testcase_name='float32_segment_ids',
shapes={
'encoder_input_tokens': [1, 512],
'decoder_input_tokens': [1, 62],
'encoder_segment_ids': [1, 512],
'decoder_segment_ids': [1, 62],
},
types={
'encoder_input_tokens': jnp.int32,
'decoder_input_tokens': jnp.int32,
'encoder_segment_ids': jnp.int32,
'decoder_segment_ids': jnp.int32
}),
)
def test_get_initial_variables_shapes_and_types(self, shapes, types):
mock_transformer = mock.Mock()
mock_transformer.init.return_value = {'params': {}}
mock_optimizer_def = mock.Mock()
rng = mock.Mock()
def mock_init(self):
self.module = mock_transformer
self.optimizer_def = mock_optimizer_def
with mock.patch.object(
models.EncoderDecoderModel, '__init__', new=mock_init):
model = models.EncoderDecoderModel()
model.get_initial_variables(rng, shapes, types)
if types is None:
encoder_input = jnp.ones(
shapes['encoder_input_tokens'], dtype=jnp.float32)
decoder_input = jnp.ones(
shapes['decoder_input_tokens'], dtype=jnp.float32)
else:
encoder_input = jnp.ones(
shapes['encoder_input_tokens'], dtype=types['encoder_input_tokens'])
decoder_input = jnp.ones(
shapes['decoder_input_tokens'], dtype=types['decoder_input_tokens'])
# Using `.assert_called_once_with` doesn't work because the simple
# comparison it does for the array arguments fail (truth value of an array
# is ambiguous).
called_with = mock_transformer.init.call_args
self.assertEqual(called_with[0][0], rng)
np.testing.assert_allclose(called_with[0][1], encoder_input)
np.testing.assert_allclose(called_with[0][2], decoder_input)
np.testing.assert_allclose(called_with[0][3], decoder_input)
if 'encoder_positions' in shapes:
encoder_positions = jnp.ones(
shapes['encoder_positions'], dtype=types['encoder_positions'])
np.testing.assert_allclose(called_with[1]['encoder_positions'],
encoder_positions)
else:
self.assertIsNone(called_with[1]['encoder_positions'])
if 'decoder_positions' in shapes:
decoder_positions = jnp.ones(
shapes['decoder_positions'], dtype=types['decoder_positions'])
np.testing.assert_allclose(called_with[1]['decoder_positions'],
decoder_positions)
else:
self.assertIsNone(called_with[1]['decoder_positions'])
if 'encoder_segment_ids' in shapes:
encoder_positions = jnp.ones(
shapes['encoder_segment_ids'], dtype=types['encoder_segment_ids'])
np.testing.assert_allclose(called_with[1]['encoder_segment_ids'],
encoder_positions)
else:
self.assertIsNone(called_with[1]['encoder_segment_ids'])
if 'decoder_segment_ids' in shapes:
decoder_segment_ids = jnp.ones(
shapes['decoder_segment_ids'], dtype=types['decoder_segment_ids'])
np.testing.assert_allclose(called_with[1]['decoder_segment_ids'],
decoder_segment_ids)
else:
self.assertIsNone(called_with[1]['decoder_segment_ids'])
self.assertFalse(called_with[1]['decode'])
self.assertFalse(called_with[1]['enable_dropout'])
@parameterized.named_parameters(
dict(testcase_name='no_force_decoding', prompt_with_targets=False),
dict(testcase_name='force_decoding', prompt_with_targets=True),
)
def test_prompt_with_targets(self, prompt_with_targets):
batch_size, encoder_len, max_decode_len, emb_dim = 2, 3, 4, 5
batch = {
'encoder_input_tokens':
np.zeros((batch_size, encoder_len), dtype=np.int32),
'decoder_input_tokens':
np.full([batch_size, max_decode_len], 2, dtype=np.int32)
}
# These dummy logits represent the probability distribution where all the
# probability mass is in one item (i.e., degenerate distribution). For
# batch element 0, it is vocabulary index 3.
# We test `_predict_step` to avoid having to define a task and its
# vocabulary.
dummy_logits = jnp.expand_dims(
jnp.array([[-1e7, -1e7, -1e7, 0, -1e7], [-1e7, -1e7, -1e7, -1e7, 0]]),
axis=1)
mock_decode_fn = mock.Mock()
mock_decode_fn.return_value = (np.full([batch_size, max_decode_len, 1],
3,
dtype=np.int32),
np.full([batch_size, 1],
1.0,
dtype=np.float32))
class MockModule:
def __init__(self):
self.dtype = jnp.float32
def apply(self, *args, method=None, **kwargs):
del args, kwargs
if method is None: # use for module.`__call__`
return (dummy_logits, {'cache': {}})
else:
return method()
def encode(self):
return jnp.zeros((batch_size, encoder_len, emb_dim))
def decode(self):
return (dummy_logits, {'cache': {}})
def mock_init(self):
self.module = MockModule()
self.module.scan_layers = False
self._input_vocabulary = mock.Mock(eos_id=1)
self._output_vocabulary = mock.Mock(eos_id=1)
self._decode_fn = mock_decode_fn
with mock.patch.object(
models.EncoderDecoderModel, '__init__', new=mock_init):
model = models.EncoderDecoderModel()
model.predict_batch_with_aux({},
batch,
prompt_with_targets=prompt_with_targets)
if prompt_with_targets:
expected_inputs = batch['decoder_input_tokens']
else:
expected_inputs = np.zeros([batch_size, max_decode_len], dtype=np.int32)
assert mock_decode_fn.call_count == 1
# Look at the kwargs call list for inputs, assert_called_with doesn't
# work well with np.array comparison.
np.testing.assert_array_equal(mock_decode_fn.mock_calls[0][2]['inputs'],
expected_inputs)
def test_predict_batch_loop_and_caches_are_equal(self):
vocab_size = 50
lengths = np.array([[2], [3]])
batch_size, beam_size, encoder_len, max_decode_len = 2, 2, 3, 7
batch = {
'encoder_input_tokens':
np.zeros((batch_size, encoder_len), dtype=np.int32),
'decoder_target_tokens':
np.zeros((batch_size, encoder_len), dtype=np.int32),
'decoder_input_tokens':
np.concatenate(
[
np.expand_dims(
np.concatenate(
[[0],
np.arange(9, 9 + lengths[0][0], dtype=np.int32),
np.zeros((max_decode_len - lengths[0][0] - 1),
dtype=np.int32)]),
axis=0), # First element
np.expand_dims(
np.concatenate(
[[0],
np.arange(3, 3 + lengths[1][0], dtype=np.int32),
np.zeros((max_decode_len - lengths[1][0] - 1),
dtype=np.int32)]),
axis=0) # Second element
],
axis=0),
}
model = test_utils.get_t5_test_model(vocab_size=50)
module = model.module
params = module.init(
jax.random.PRNGKey(0),
jnp.ones((batch_size, encoder_len)),
jnp.ones((batch_size, max_decode_len)),
jnp.ones((batch_size, max_decode_len)),
enable_dropout=False)['params']
def mock_init(self):
self.module = module
# Set the EOS token to be larger then the vocabulary size. This forces the
# model to decode all the way to `max_decode_length`, allowing us to test
# behavior when one element reaches the end before the others.
self._output_vocabulary = mock.Mock(eos_id=vocab_size + 12) | self._decode_fn = decoding.beam_search | 0 | 2023-12-12 20:23:33+00:00 | 16k |
zju3dv/EasyVolcap | tests/headless_opengl_tests.py | [
{
"identifier": "eglContextManager",
"path": "easyvolcap/utils/egl_utils.py",
"snippet": "class eglContextManager:\n # Manages the creation and destruction of an EGL context\n # Will resize if the size of the window changes\n # Will also manage gl.Viewport to render different parts of the scree... | from easyvolcap.utils.egl_utils import eglContextManager # must be imported before OpenGL.GL
from os.path import join, dirname
from easyvolcap.utils.console_utils import *
from easyvolcap.utils.gl_utils import Quad, Mesh
from easyvolcap.utils.viewer_utils import Camera
from easyvolcap.utils.data_utils import save_image
from easyvolcap.utils.gl_utils import common_opengl_options, linearize_depth
from easyvolcap.utils.test_utils import my_tests
import OpenGL.GL as gl
import os
import cv2
import torch
import numpy as np | 11,678 | from __future__ import absolute_import, division, print_function
# fmt: off
# fmt: on
WIDTH, HEIGHT = 512, 512
eglctx = eglContextManager(HEIGHT, WIDTH) # will create a new context
| from __future__ import absolute_import, division, print_function
# fmt: off
# fmt: on
WIDTH, HEIGHT = 512, 512
eglctx = eglContextManager(HEIGHT, WIDTH) # will create a new context | common_opengl_options() # common init | 5 | 2023-12-07 08:53:42+00:00 | 16k |
alibaba/animate-anything | utils/lora_handler.py | [
{
"identifier": "UNet3DConditionModel",
"path": "models/unet_3d_condition_mask.py",
"snippet": "class UNet3DConditionModel(ModelMixin, ConfigMixin):\n r\"\"\"\n UNet3DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep\n and returns samp... | import os
import torch
import uuid
from logging import warnings
from typing import Union
from types import SimpleNamespace
from models.unet_3d_condition_mask import UNet3DConditionModel
from transformers import CLIPTextModel
from utils.convert_diffusers_to_original_ms_text_to_video import convert_unet_state_dict, convert_text_enc_state_dict_v20
from .lora import (
extract_lora_ups_down,
inject_trainable_lora_extended,
save_lora_weight,
train_patch_pipe,
monkeypatch_or_replace_lora,
monkeypatch_or_replace_lora_extended
)
from stable_lora.lora import (
activate_lora_train,
add_lora_to,
save_lora,
load_lora,
set_mode_group
) | 11,099 |
FILE_BASENAMES = ['unet', 'text_encoder']
LORA_FILE_TYPES = ['.pt', '.safetensors']
CLONE_OF_SIMO_KEYS = ['model', 'loras', 'target_replace_module', 'r']
STABLE_LORA_KEYS = ['model', 'target_module', 'search_class', 'r', 'dropout', 'lora_bias']
lora_versions = dict(
stable_lora = "stable_lora",
cloneofsimo = "cloneofsimo"
)
lora_func_types = dict(
loader = "loader",
injector = "injector"
)
lora_args = dict(
model = None,
loras = None,
target_replace_module = [],
target_module = [],
r = 4,
search_class = [torch.nn.Linear],
dropout = 0,
lora_bias = 'none'
)
LoraVersions = SimpleNamespace(**lora_versions)
LoraFuncTypes = SimpleNamespace(**lora_func_types)
LORA_VERSIONS = [LoraVersions.stable_lora, LoraVersions.cloneofsimo]
LORA_FUNC_TYPES = [LoraFuncTypes.loader, LoraFuncTypes.injector]
def filter_dict(_dict, keys=[]):
if len(keys) == 0:
assert "Keys cannot empty for filtering return dict."
for k in keys:
if k not in lora_args.keys():
assert f"{k} does not exist in available LoRA arguments"
return {k: v for k, v in _dict.items() if k in keys}
class LoraHandler(object):
def __init__(
self,
version: LORA_VERSIONS = LoraVersions.cloneofsimo,
use_unet_lora: bool = False,
use_text_lora: bool = False,
save_for_webui: bool = False,
only_for_webui: bool = False,
lora_bias: str = 'none',
unet_replace_modules: list = ['UNet3DConditionModel'],
text_encoder_replace_modules: list = ['CLIPEncoderLayer']
):
self.version = version
self.lora_loader = self.get_lora_func(func_type=LoraFuncTypes.loader)
self.lora_injector = self.get_lora_func(func_type=LoraFuncTypes.injector)
self.lora_bias = lora_bias
self.use_unet_lora = use_unet_lora
self.use_text_lora = use_text_lora
self.save_for_webui = save_for_webui
self.only_for_webui = only_for_webui
self.unet_replace_modules = unet_replace_modules
self.text_encoder_replace_modules = text_encoder_replace_modules
self.use_lora = any([use_text_lora, use_unet_lora])
if self.use_lora:
print(f"Using LoRA Version: {self.version}")
def is_cloneofsimo_lora(self):
return self.version == LoraVersions.cloneofsimo
def is_stable_lora(self):
return self.version == LoraVersions.stable_lora
def get_lora_func(self, func_type: LORA_FUNC_TYPES = LoraFuncTypes.loader):
if self.is_cloneofsimo_lora():
if func_type == LoraFuncTypes.loader:
return monkeypatch_or_replace_lora_extended
if func_type == LoraFuncTypes.injector:
return inject_trainable_lora_extended
if self.is_stable_lora():
if func_type == LoraFuncTypes.loader:
return load_lora
if func_type == LoraFuncTypes.injector:
|
FILE_BASENAMES = ['unet', 'text_encoder']
LORA_FILE_TYPES = ['.pt', '.safetensors']
CLONE_OF_SIMO_KEYS = ['model', 'loras', 'target_replace_module', 'r']
STABLE_LORA_KEYS = ['model', 'target_module', 'search_class', 'r', 'dropout', 'lora_bias']
lora_versions = dict(
stable_lora = "stable_lora",
cloneofsimo = "cloneofsimo"
)
lora_func_types = dict(
loader = "loader",
injector = "injector"
)
lora_args = dict(
model = None,
loras = None,
target_replace_module = [],
target_module = [],
r = 4,
search_class = [torch.nn.Linear],
dropout = 0,
lora_bias = 'none'
)
LoraVersions = SimpleNamespace(**lora_versions)
LoraFuncTypes = SimpleNamespace(**lora_func_types)
LORA_VERSIONS = [LoraVersions.stable_lora, LoraVersions.cloneofsimo]
LORA_FUNC_TYPES = [LoraFuncTypes.loader, LoraFuncTypes.injector]
def filter_dict(_dict, keys=[]):
if len(keys) == 0:
assert "Keys cannot empty for filtering return dict."
for k in keys:
if k not in lora_args.keys():
assert f"{k} does not exist in available LoRA arguments"
return {k: v for k, v in _dict.items() if k in keys}
class LoraHandler(object):
def __init__(
self,
version: LORA_VERSIONS = LoraVersions.cloneofsimo,
use_unet_lora: bool = False,
use_text_lora: bool = False,
save_for_webui: bool = False,
only_for_webui: bool = False,
lora_bias: str = 'none',
unet_replace_modules: list = ['UNet3DConditionModel'],
text_encoder_replace_modules: list = ['CLIPEncoderLayer']
):
self.version = version
self.lora_loader = self.get_lora_func(func_type=LoraFuncTypes.loader)
self.lora_injector = self.get_lora_func(func_type=LoraFuncTypes.injector)
self.lora_bias = lora_bias
self.use_unet_lora = use_unet_lora
self.use_text_lora = use_text_lora
self.save_for_webui = save_for_webui
self.only_for_webui = only_for_webui
self.unet_replace_modules = unet_replace_modules
self.text_encoder_replace_modules = text_encoder_replace_modules
self.use_lora = any([use_text_lora, use_unet_lora])
if self.use_lora:
print(f"Using LoRA Version: {self.version}")
def is_cloneofsimo_lora(self):
return self.version == LoraVersions.cloneofsimo
def is_stable_lora(self):
return self.version == LoraVersions.stable_lora
def get_lora_func(self, func_type: LORA_FUNC_TYPES = LoraFuncTypes.loader):
if self.is_cloneofsimo_lora():
if func_type == LoraFuncTypes.loader:
return monkeypatch_or_replace_lora_extended
if func_type == LoraFuncTypes.injector:
return inject_trainable_lora_extended
if self.is_stable_lora():
if func_type == LoraFuncTypes.loader:
return load_lora
if func_type == LoraFuncTypes.injector: | return add_lora_to | 10 | 2023-12-07 08:26:29+00:00 | 16k |
octo-models/octo | scripts/finetune.py | [
{
"identifier": "make_single_dataset",
"path": "octo/data/dataset.py",
"snippet": "def make_single_dataset(\n dataset_kwargs: dict,\n *,\n train: bool,\n traj_transform_kwargs: dict = {},\n frame_transform_kwargs: dict = {},\n) -> dl.DLataset:\n \"\"\"Creates a single dataset from kwar... | import datetime
import imp
import os
import flax
import jax
import optax
import tensorflow as tf
import tqdm
import wandb
from functools import partial
from absl import app, flags, logging
from flax.traverse_util import flatten_dict
from jax.sharding import Mesh, NamedSharding, PartitionSpec
from ml_collections import config_flags, ConfigDict
from octo.data.dataset import make_single_dataset
from octo.model.octo_model import OctoModel
from octo.utils.jax_utils import initialize_compilation_cache
from octo.utils.spec import ModuleSpec
from octo.utils.train_callbacks import (
RolloutVisualizationCallback,
SaveCallback,
ValidationCallback,
VisualizationCallback,
)
from octo.utils.train_utils import (
check_config_diff,
create_optimizer,
format_name_with_config,
merge_params,
process_text,
Timer,
TrainState,
)
from jax_smi import initialise_tracking # type: ignore | 11,508 | wandb_id,
)
wandb.config.update(dict(save_dir=save_dir), allow_val_change=True)
logging.info("Saving to %s", save_dir)
save_callback = SaveCallback(save_dir)
# Add window_size to top of config, to make eval easier
new_config = ConfigDict(model.config)
new_config["window_size"] = example_batch["observation"]["pad_mask"].shape[1]
model = model.replace(config=new_config)
# Save finetuning config since it's not saved by SaveCallback, i.e. as part of model.save_pretrained()
with open(
tf.io.gfile.join(save_dir, "finetune_config.json"), "w"
) as config_file:
config_file.write(FLAGS.config.to_json_best_effort())
else:
save_dir = None
save_callback = SaveCallback(None)
logging.warning("save_dir not passed in, not saving checkpoints")
example_batch_spec = jax.tree_map(
lambda arr: (arr.shape, str(arr.dtype)), example_batch
)
wandb.config.update(
dict(example_batch_spec=example_batch_spec), allow_val_change=True
)
#########
#
# Define loss, train_step, and eval_step
#
#########
def loss_fn(params, batch, rng, train=True):
bound_module = model.module.bind({"params": params}, rngs={"dropout": rng})
transformer_embeddings = bound_module.octo_transformer(
batch["observation"],
batch["task"],
batch["observation"]["pad_mask"],
train=train,
)
action_loss, action_metrics = bound_module.heads["action"].loss(
transformer_embeddings, # Action head knows to pull out the action readout_key
batch["action"],
pad_mask=batch["observation"]["pad_mask"],
train=train,
)
return action_loss, action_metrics
# Data parallelism
# Model is replicated across devices, data is split across devices
@partial(
jax.jit,
in_shardings=[replicated_sharding, dp_sharding],
)
def train_step(state, batch):
rng, dropout_rng = jax.random.split(state.rng)
(loss, info), grads = jax.value_and_grad(loss_fn, has_aux=True)(
state.model.params, batch, dropout_rng, train=True
)
# Gradient Metrics (TODO: Does the finetuner need these?) ###
grad_norm = optax.global_norm(grads)
updates, _ = state.tx.update(grads, state.opt_state, state.model.params)
update_norm = optax.global_norm(updates)
info.update(
{
"grad_norm": grad_norm,
"update_norm": update_norm,
"param_norm": param_norm_callable(state.model.params),
"learning_rate": lr_callable(state.step),
}
)
# End Debug Metrics #
new_state = state.apply_gradients(grads=grads, rng=rng)
return new_state, info
#########
#
# Build validation & visualization callbacks
#
#########
if FLAGS.config.modality == "image_conditioned":
modes_to_evaluate = ["image_conditioned"]
elif FLAGS.config.modality == "text_conditioned":
modes_to_evaluate = ["text_conditioned"]
elif FLAGS.config.modality == "multimodal":
modes_to_evaluate = ["image_conditioned", "text_conditioned"]
else:
modes_to_evaluate = ["base"]
dataset_kwargs_list = [FLAGS.config.dataset_kwargs]
val_callback = ValidationCallback(
loss_fn=loss_fn,
process_batch_fn=process_batch,
text_processor=text_processor,
val_dataset_kwargs_list=dataset_kwargs_list,
dataset_kwargs=FLAGS.config,
modes_to_evaluate=modes_to_evaluate,
**FLAGS.config.val_kwargs,
)
viz_callback = VisualizationCallback(
text_processor=text_processor,
val_dataset_kwargs_list=dataset_kwargs_list,
dataset_kwargs=FLAGS.config,
modes_to_evaluate=modes_to_evaluate,
**FLAGS.config.viz_kwargs,
)
#########
#
# Optionally build visualizers for sim env evals
#
#########
if "rollout_kwargs" in FLAGS.config:
|
try:
initialise_tracking()
except ImportError:
pass
FLAGS = flags.FLAGS
flags.DEFINE_string("name", "experiment", "Experiment name.")
flags.DEFINE_bool("debug", False, "Debug config (no wandb logging)")
default_config_file = os.path.join(
os.path.dirname(__file__), "configs/finetune_config.py"
)
config_flags.DEFINE_config_file(
"config",
default_config_file,
"File path to the training hyperparameter configuration.",
lock_config=False,
)
def main(_):
initialize_compilation_cache()
devices = jax.devices()
logging.info(
f"""
Octo Finetuning Script
======================
Pretrained model: {FLAGS.config.pretrained_path}
Finetuning Dataset: {FLAGS.config.dataset_kwargs.name}
Data dir: {FLAGS.config.dataset_kwargs.data_dir}
Task Modality: {FLAGS.config.modality}
Finetuning Mode: {FLAGS.config.finetuning_mode}
# Devices: {jax.device_count()}
Batch size: {FLAGS.config.batch_size} ({FLAGS.config.batch_size // len(devices) } per device)
# Steps: {FLAGS.config.num_steps}
"""
)
#########
#
# Setup Jax Data Parallelism
#
#########
assert (
FLAGS.config.batch_size % len(devices) == 0
), f"Batch size ({FLAGS.config.batch_size}) must be divisible by the number of devices ({len(devices)})"
assert (
FLAGS.config.viz_kwargs.eval_batch_size % len(devices) == 0
), f"Eval batch size ({FLAGS.config.viz_kwargs.eval_batch_size}) must be divisible by the number of devices ({len(devices)})"
# create a 1D mesh with a single axis named "batch"
mesh = Mesh(jax.devices(), axis_names="batch")
# Our batches will be data-parallel sharded -- each device will get a slice of the batch
dp_sharding = NamedSharding(mesh, PartitionSpec("batch"))
# Our model will be replicated across devices (we are only doing data parallelism, not model parallelism)
replicated_sharding = NamedSharding(mesh, PartitionSpec())
# prevent tensorflow from using GPU memory since it's only used for data loading
tf.config.set_visible_devices([], "GPU")
#########
#
# Setup WandB
#
#########
name = format_name_with_config(
FLAGS.name,
FLAGS.config.to_dict(),
)
wandb_id = "{name}_{time}".format(
name=name,
time=datetime.datetime.now().strftime("%Y%m%d_%H%M%S"),
)
wandb.init(
config=FLAGS.config.to_dict(),
id=wandb_id,
name=name,
mode="disabled" if FLAGS.debug else None,
**FLAGS.config.wandb,
)
#########
#
# Load Pretrained model + optionally modify config
#
#########
pretrained_model = OctoModel.load_pretrained(
FLAGS.config.pretrained_path,
step=FLAGS.config.pretrained_step,
)
flat_config = flax.traverse_util.flatten_dict(
pretrained_model.config, keep_empty_nodes=True
)
for d_key in flax.traverse_util.flatten_dict(
FLAGS.config.get("config_delete_keys", ConfigDict()).to_dict()
):
for c_key in list(flat_config.keys()):
if ".".join(c_key).startswith(".".join(d_key)):
del flat_config[c_key]
config = ConfigDict(flax.traverse_util.unflatten_dict(flat_config))
config.update(FLAGS.config.get("update_config", ConfigDict()))
config = config.to_dict()
check_config_diff(config, pretrained_model.config)
#########
#
# Setup Data Loader
#
#########
# create text processor
if config["text_processor"] is None:
text_processor = None
else:
text_processor = ModuleSpec.instantiate(config["text_processor"])()
def process_batch(batch):
batch = process_text(batch, text_processor)
del batch["dataset_name"]
return batch
# load standardize_fn from `path/to/file.py:fn_name` format
if (
standardize_fn := FLAGS.config["dataset_kwargs"].get("standardize_fn", None)
) is not None:
path, name = standardize_fn.split(":")
# imp is deprecated, but it's also what ml_collections uses
standardize_fn = getattr(imp.load_source("standardize_fn", path), name)
del FLAGS.config["dataset_kwargs"]["standardize_fn"]
FLAGS.config["dataset_kwargs"]["standardize_fn"] = standardize_fn
dataset = make_single_dataset(
FLAGS.config.dataset_kwargs,
traj_transform_kwargs=FLAGS.config.traj_transform_kwargs,
frame_transform_kwargs=FLAGS.config.frame_transform_kwargs,
train=True,
)
train_data_iter = (
dataset.repeat()
.unbatch()
.shuffle(FLAGS.config.shuffle_buffer_size)
.batch(FLAGS.config.batch_size)
.iterator()
)
train_data_iter = map(process_batch, train_data_iter)
example_batch = next(train_data_iter)
#########
#
# Load Pretrained Model
#
#########
rng = jax.random.PRNGKey(FLAGS.config.seed)
rng, init_rng = jax.random.split(rng)
model = OctoModel.from_config(
config,
example_batch,
text_processor,
rng=init_rng,
dataset_statistics=dataset.dataset_statistics,
)
merged_params = merge_params(model.params, pretrained_model.params)
model = model.replace(params=merged_params)
del pretrained_model
#########
#
# Setup Optimizer and Train State
#
#########
params = model.params
if FLAGS.config.optimizer.frozen_keys is None:
FLAGS.config.optimizer.frozen_keys = model.config["optimizer"]["frozen_keys"]
tx, lr_callable, param_norm_callable = create_optimizer(
params,
**FLAGS.config.optimizer.to_dict(),
)
train_state = TrainState.create(
model=model,
tx=tx,
rng=rng,
)
#########
#
# Save all metadata
#
#########
if FLAGS.config.save_dir is not None:
save_dir = tf.io.gfile.join(
FLAGS.config.save_dir,
FLAGS.config.wandb.project,
FLAGS.config.wandb.group or "",
wandb_id,
)
wandb.config.update(dict(save_dir=save_dir), allow_val_change=True)
logging.info("Saving to %s", save_dir)
save_callback = SaveCallback(save_dir)
# Add window_size to top of config, to make eval easier
new_config = ConfigDict(model.config)
new_config["window_size"] = example_batch["observation"]["pad_mask"].shape[1]
model = model.replace(config=new_config)
# Save finetuning config since it's not saved by SaveCallback, i.e. as part of model.save_pretrained()
with open(
tf.io.gfile.join(save_dir, "finetune_config.json"), "w"
) as config_file:
config_file.write(FLAGS.config.to_json_best_effort())
else:
save_dir = None
save_callback = SaveCallback(None)
logging.warning("save_dir not passed in, not saving checkpoints")
example_batch_spec = jax.tree_map(
lambda arr: (arr.shape, str(arr.dtype)), example_batch
)
wandb.config.update(
dict(example_batch_spec=example_batch_spec), allow_val_change=True
)
#########
#
# Define loss, train_step, and eval_step
#
#########
def loss_fn(params, batch, rng, train=True):
bound_module = model.module.bind({"params": params}, rngs={"dropout": rng})
transformer_embeddings = bound_module.octo_transformer(
batch["observation"],
batch["task"],
batch["observation"]["pad_mask"],
train=train,
)
action_loss, action_metrics = bound_module.heads["action"].loss(
transformer_embeddings, # Action head knows to pull out the action readout_key
batch["action"],
pad_mask=batch["observation"]["pad_mask"],
train=train,
)
return action_loss, action_metrics
# Data parallelism
# Model is replicated across devices, data is split across devices
@partial(
jax.jit,
in_shardings=[replicated_sharding, dp_sharding],
)
def train_step(state, batch):
rng, dropout_rng = jax.random.split(state.rng)
(loss, info), grads = jax.value_and_grad(loss_fn, has_aux=True)(
state.model.params, batch, dropout_rng, train=True
)
# Gradient Metrics (TODO: Does the finetuner need these?) ###
grad_norm = optax.global_norm(grads)
updates, _ = state.tx.update(grads, state.opt_state, state.model.params)
update_norm = optax.global_norm(updates)
info.update(
{
"grad_norm": grad_norm,
"update_norm": update_norm,
"param_norm": param_norm_callable(state.model.params),
"learning_rate": lr_callable(state.step),
}
)
# End Debug Metrics #
new_state = state.apply_gradients(grads=grads, rng=rng)
return new_state, info
#########
#
# Build validation & visualization callbacks
#
#########
if FLAGS.config.modality == "image_conditioned":
modes_to_evaluate = ["image_conditioned"]
elif FLAGS.config.modality == "text_conditioned":
modes_to_evaluate = ["text_conditioned"]
elif FLAGS.config.modality == "multimodal":
modes_to_evaluate = ["image_conditioned", "text_conditioned"]
else:
modes_to_evaluate = ["base"]
dataset_kwargs_list = [FLAGS.config.dataset_kwargs]
val_callback = ValidationCallback(
loss_fn=loss_fn,
process_batch_fn=process_batch,
text_processor=text_processor,
val_dataset_kwargs_list=dataset_kwargs_list,
dataset_kwargs=FLAGS.config,
modes_to_evaluate=modes_to_evaluate,
**FLAGS.config.val_kwargs,
)
viz_callback = VisualizationCallback(
text_processor=text_processor,
val_dataset_kwargs_list=dataset_kwargs_list,
dataset_kwargs=FLAGS.config,
modes_to_evaluate=modes_to_evaluate,
**FLAGS.config.viz_kwargs,
)
#########
#
# Optionally build visualizers for sim env evals
#
#########
if "rollout_kwargs" in FLAGS.config: | rollout_callback = RolloutVisualizationCallback( | 4 | 2023-12-13 09:58:56+00:00 | 16k |
LinShan-Bin/OccNeRF | run.py | [
{
"identifier": "Runer",
"path": "runner.py",
"snippet": "class Runer:\n\n def __init__(self, options):\n\n self.opt = options\n self.opt.B = self.opt.batch_size // self.opt.cam_N\n if self.opt.debug:\n self.opt.voxels_size = [8, 128, 128]\n self.opt.render_... | import torch
import numpy as np
from runner import Runer
from options import MonodepthOptions | 13,641 | # Copyright Niantic 2019. Patent Pending. All rights reserved.
#
# This software is licensed under the terms of the Monodepth2 licence
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
from __future__ import absolute_import, division, print_function
options = MonodepthOptions()
opts = options.parse()
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
torch.backends.cudnn.deterministic = True
if __name__ == "__main__":
setup_seed(42)
| # Copyright Niantic 2019. Patent Pending. All rights reserved.
#
# This software is licensed under the terms of the Monodepth2 licence
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
from __future__ import absolute_import, division, print_function
options = MonodepthOptions()
opts = options.parse()
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
torch.backends.cudnn.deterministic = True
if __name__ == "__main__":
setup_seed(42) | trainer = Runer(opts) | 0 | 2023-12-14 15:00:21+00:00 | 16k |
modelscope/richdreamer | extern/ldm_zero123/models/diffusion/ddpm.py | [
{
"identifier": "AutoencoderKL",
"path": "extern/ldm_zero123/models/autoencoder.py",
"snippet": "class AutoencoderKL(pl.LightningModule):\n def __init__(\n self,\n ddconfig,\n lossconfig,\n embed_dim,\n ckpt_path=None,\n ignore_keys=[],\n image_key=\"i... | import itertools
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn as nn
from contextlib import contextmanager, nullcontext
from einops import rearrange, repeat
from functools import partial
from omegaconf import ListConfig
from pytorch_lightning.utilities.rank_zero import rank_zero_only
from torch.optim.lr_scheduler import LambdaLR
from torchvision.utils import make_grid
from tqdm import tqdm
from extern.ldm_zero123.models.autoencoder import (AutoencoderKL,
IdentityFirstStage,
VQModelInterface,)
from extern.ldm_zero123.models.diffusion.ddim import DDIMSampler
from extern.ldm_zero123.modules.attention import CrossAttention
from extern.ldm_zero123.modules.diffusionmodules.util import (
extract_into_tensor, make_beta_schedule, noise_like,)
from extern.ldm_zero123.modules.distributions.distributions import (
DiagonalGaussianDistribution, normal_kl,)
from extern.ldm_zero123.modules.ema import LitEma
from extern.ldm_zero123.util import (count_params, default, exists,
instantiate_from_config, isimage, ismap,
log_txt_as_img, mean_flat,) | 12,580 | new_param[i, j] = old_param[
i % old_shape[0], j % old_shape[1]
]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
missing, unexpected = (
self.load_state_dict(sd, strict=False)
if not only_model
else self.model.load_state_dict(sd, strict=False)
)
print(
f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
)
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(
self.log_one_minus_alphas_cumprod, t, x_start.shape
)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
- extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
* noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+ extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(
self.posterior_log_variance_clipped, t, x_t.shape
)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1.0, 1.0)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
x_start=x_recon, x_t=x, t=t
)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(
x=x, t=t, clip_denoised=clip_denoised
)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(
reversed(range(0, self.num_timesteps)),
desc="Sampling t",
total=self.num_timesteps,
):
img = self.p_sample(
img,
torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised,
)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop(
(batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates,
)
def q_sample(self, x_start, t, noise=None):
| """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {"concat": "c_concat", "crossattn": "c_crossattn", "adm": "y"}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(
self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.0,
v_posterior=0.0, # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.0,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.0,
make_it_fit=False,
ucg_training=None,
):
super().__init__()
assert parameterization in [
"eps",
"x0",
], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(
f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode"
)
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
self.make_it_fit = make_it_fit
if ckpt_path is not None:
self.init_from_ckpt(
ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet
)
self.register_schedule(
given_betas=given_betas,
beta_schedule=beta_schedule,
timesteps=timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s,
)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
self.ucg_training = ucg_training or dict()
if self.ucg_training:
self.ucg_prng = np.random.RandomState()
def register_schedule(
self,
given_betas=None,
beta_schedule="linear",
timesteps=1000,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(
beta_schedule,
timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s,
)
alphas = 1.0 - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
(timesteps,) = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert (
alphas_cumprod.shape[0] == self.num_timesteps
), "alphas have to be defined for each timestep"
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer("betas", to_torch(betas))
self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer(
"sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
)
self.register_buffer(
"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
)
self.register_buffer(
"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
)
self.register_buffer(
"sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
)
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (
1.0 - alphas_cumprod_prev
) / (1.0 - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer("posterior_variance", to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer(
"posterior_log_variance_clipped",
to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
)
self.register_buffer(
"posterior_mean_coef1",
to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
)
self.register_buffer(
"posterior_mean_coef2",
to_torch(
(1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
),
)
if self.parameterization == "eps":
lvlb_weights = self.betas**2 / (
2
* self.posterior_variance
* to_torch(alphas)
* (1 - self.alphas_cumprod)
)
elif self.parameterization == "x0":
lvlb_weights = (
0.5
* np.sqrt(torch.Tensor(alphas_cumprod))
/ (2.0 * 1 - torch.Tensor(alphas_cumprod))
)
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer("lvlb_weights", lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
@torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
if self.make_it_fit:
n_params = len(
[
name
for name, _ in itertools.chain(
self.named_parameters(), self.named_buffers()
)
]
)
for name, param in tqdm(
itertools.chain(self.named_parameters(), self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params,
):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape) == len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[
i % old_shape[0], j % old_shape[1]
]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
missing, unexpected = (
self.load_state_dict(sd, strict=False)
if not only_model
else self.model.load_state_dict(sd, strict=False)
)
print(
f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
)
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(
self.log_one_minus_alphas_cumprod, t, x_start.shape
)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
- extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
* noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+ extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(
self.posterior_log_variance_clipped, t, x_t.shape
)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1.0, 1.0)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
x_start=x_recon, x_t=x, t=t
)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(
x=x, t=t, clip_denoised=clip_denoised
)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(
reversed(range(0, self.num_timesteps)),
desc="Sampling t",
total=self.num_timesteps,
):
img = self.p_sample(
img,
torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised,
)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop(
(batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates,
)
def q_sample(self, x_start, t, noise=None): | noise = default(noise, lambda: torch.randn_like(x_start)) | 12 | 2023-12-06 07:53:11+00:00 | 16k |
rehg-lab/RAVE | annotator/oneformer/detectron2/export/caffe2_patch.py | [
{
"identifier": "poolers",
"path": "annotator/oneformer/detectron2/modeling/poolers.py",
"snippet": "def assign_boxes_to_levels(\r\n box_lists: List[Boxes],\r\n min_level: int,\r\n max_level: int,\r\n canonical_box_size: int,\r\n canonical_level: int,\r\n):\r\ndef _convert_boxes_to_pooler... | import contextlib
import torch
from unittest import mock
from annotator.oneformer.detectron2.modeling import poolers
from annotator.oneformer.detectron2.modeling.proposal_generator import rpn
from annotator.oneformer.detectron2.modeling.roi_heads import keypoint_head, mask_head
from annotator.oneformer.detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
from .c10 import (
Caffe2Compatible,
Caffe2FastRCNNOutputsInference,
Caffe2KeypointRCNNInference,
Caffe2MaskRCNNInference,
Caffe2ROIPooler,
Caffe2RPN,
)
| 11,187 | # Copyright (c) Facebook, Inc. and its affiliates.
class GenericMixin(object):
pass
class Caffe2CompatibleConverter(object):
"""
A GenericUpdater which implements the `create_from` interface, by modifying
module object and assign it with another class replaceCls.
"""
def __init__(self, replaceCls):
self.replaceCls = replaceCls
def create_from(self, module):
# update module's class to the new class
assert isinstance(module, torch.nn.Module)
if issubclass(self.replaceCls, GenericMixin):
# replaceCls should act as mixin, create a new class on-the-fly
new_class = type(
"{}MixedWith{}".format(self.replaceCls.__name__, module.__class__.__name__),
(self.replaceCls, module.__class__),
{}, # {"new_method": lambda self: ...},
)
module.__class__ = new_class
else:
# replaceCls is complete class, this allow arbitrary class swap
module.__class__ = self.replaceCls
# initialize Caffe2Compatible
if isinstance(module, Caffe2Compatible):
module.tensor_mode = False
return module
def patch(model, target, updater, *args, **kwargs):
"""
recursively (post-order) update all modules with the target type and its
subclasses, make a initialization/composition/inheritance/... via the
updater.create_from.
"""
for name, module in model.named_children():
model._modules[name] = patch(module, target, updater, *args, **kwargs)
if isinstance(model, target):
return updater.create_from(model, *args, **kwargs)
return model
def patch_generalized_rcnn(model):
ccc = Caffe2CompatibleConverter
model = patch(model, rpn.RPN, ccc(Caffe2RPN))
model = patch(model, poolers.ROIPooler, ccc(Caffe2ROIPooler))
return model
@contextlib.contextmanager
def mock_fastrcnn_outputs_inference(
tensor_mode, check=True, box_predictor_type=FastRCNNOutputLayers
):
with mock.patch.object(
box_predictor_type,
"inference",
autospec=True,
side_effect=Caffe2FastRCNNOutputsInference(tensor_mode),
) as mocked_func:
yield
if check:
assert mocked_func.call_count > 0
@contextlib.contextmanager
def mock_mask_rcnn_inference(tensor_mode, patched_module, check=True):
with mock.patch(
"{}.mask_rcnn_inference".format(patched_module), side_effect=Caffe2MaskRCNNInference()
) as mocked_func:
yield
if check:
assert mocked_func.call_count > 0
@contextlib.contextmanager
def mock_keypoint_rcnn_inference(tensor_mode, patched_module, use_heatmap_max_keypoint, check=True):
with mock.patch(
"{}.keypoint_rcnn_inference".format(patched_module),
| # Copyright (c) Facebook, Inc. and its affiliates.
class GenericMixin(object):
pass
class Caffe2CompatibleConverter(object):
"""
A GenericUpdater which implements the `create_from` interface, by modifying
module object and assign it with another class replaceCls.
"""
def __init__(self, replaceCls):
self.replaceCls = replaceCls
def create_from(self, module):
# update module's class to the new class
assert isinstance(module, torch.nn.Module)
if issubclass(self.replaceCls, GenericMixin):
# replaceCls should act as mixin, create a new class on-the-fly
new_class = type(
"{}MixedWith{}".format(self.replaceCls.__name__, module.__class__.__name__),
(self.replaceCls, module.__class__),
{}, # {"new_method": lambda self: ...},
)
module.__class__ = new_class
else:
# replaceCls is complete class, this allow arbitrary class swap
module.__class__ = self.replaceCls
# initialize Caffe2Compatible
if isinstance(module, Caffe2Compatible):
module.tensor_mode = False
return module
def patch(model, target, updater, *args, **kwargs):
"""
recursively (post-order) update all modules with the target type and its
subclasses, make a initialization/composition/inheritance/... via the
updater.create_from.
"""
for name, module in model.named_children():
model._modules[name] = patch(module, target, updater, *args, **kwargs)
if isinstance(model, target):
return updater.create_from(model, *args, **kwargs)
return model
def patch_generalized_rcnn(model):
ccc = Caffe2CompatibleConverter
model = patch(model, rpn.RPN, ccc(Caffe2RPN))
model = patch(model, poolers.ROIPooler, ccc(Caffe2ROIPooler))
return model
@contextlib.contextmanager
def mock_fastrcnn_outputs_inference(
tensor_mode, check=True, box_predictor_type=FastRCNNOutputLayers
):
with mock.patch.object(
box_predictor_type,
"inference",
autospec=True,
side_effect=Caffe2FastRCNNOutputsInference(tensor_mode),
) as mocked_func:
yield
if check:
assert mocked_func.call_count > 0
@contextlib.contextmanager
def mock_mask_rcnn_inference(tensor_mode, patched_module, check=True):
with mock.patch(
"{}.mask_rcnn_inference".format(patched_module), side_effect=Caffe2MaskRCNNInference()
) as mocked_func:
yield
if check:
assert mocked_func.call_count > 0
@contextlib.contextmanager
def mock_keypoint_rcnn_inference(tensor_mode, patched_module, use_heatmap_max_keypoint, check=True):
with mock.patch(
"{}.keypoint_rcnn_inference".format(patched_module),
| side_effect=Caffe2KeypointRCNNInference(use_heatmap_max_keypoint),
| 7 | 2023-12-05 02:51:53+00:00 | 16k |
u2seg/U2Seg | detectron2/data/build.py | [
{
"identifier": "configurable",
"path": "detectron2/config/config.py",
"snippet": "def configurable(init_func=None, *, from_config=None):\n \"\"\"\n Decorate a function or a class's __init__ method so that it can be called\n with a :class:`CfgNode` object using a :func:`from_config` function th... | import itertools
import logging
import numpy as np
import operator
import pickle
import torch
import torch.utils.data as torchdata
from collections import OrderedDict, defaultdict
from typing import Any, Callable, Dict, List, Optional, Union
from tabulate import tabulate
from termcolor import colored
from detectron2.config import configurable
from detectron2.structures import BoxMode
from detectron2.utils.comm import get_world_size
from detectron2.utils.env import seed_all_rng
from detectron2.utils.file_io import PathManager
from detectron2.utils.logger import _log_api_usage, log_first_n
from .catalog import DatasetCatalog, MetadataCatalog
from .common import AspectRatioGroupedDataset, DatasetFromList, MapDataset, ToIterableDataset
from .dataset_mapper import DatasetMapper
from .detection_utils import check_metadata_consistency
from .samplers import (
InferenceSampler,
RandomSubsetTrainingSampler,
RepeatFactorTrainingSampler,
TrainingSampler,
) | 11,729 | Args:
dataset_dicts (list[dict]): annotations in Detectron2 Dataset format.
proposal_file (str): file path of pre-computed proposals, in pkl format.
Returns:
list[dict]: the same format as dataset_dicts, but added proposal field.
"""
logger = logging.getLogger(__name__)
logger.info("Loading proposals from: {}".format(proposal_file))
with PathManager.open(proposal_file, "rb") as f:
proposals = pickle.load(f, encoding="latin1")
# Rename the key names in D1 proposal files
rename_keys = {"indexes": "ids", "scores": "objectness_logits"}
for key in rename_keys:
if key in proposals:
proposals[rename_keys[key]] = proposals.pop(key)
# Fetch the indexes of all proposals that are in the dataset
# Convert image_id to str since they could be int.
img_ids = set({str(record["image_id"]) for record in dataset_dicts})
id_to_index = {str(id): i for i, id in enumerate(proposals["ids"]) if str(id) in img_ids}
# Assuming default bbox_mode of precomputed proposals are 'XYXY_ABS'
bbox_mode = BoxMode(proposals["bbox_mode"]) if "bbox_mode" in proposals else BoxMode.XYXY_ABS
for record in dataset_dicts:
# Get the index of the proposal
i = id_to_index[str(record["image_id"])]
boxes = proposals["boxes"][i]
objectness_logits = proposals["objectness_logits"][i]
# Sort the proposals in descending order of the scores
inds = objectness_logits.argsort()[::-1]
record["proposal_boxes"] = boxes[inds]
record["proposal_objectness_logits"] = objectness_logits[inds]
record["proposal_bbox_mode"] = bbox_mode
return dataset_dicts
def print_instances_class_histogram(dataset_dicts, class_names):
"""
Args:
dataset_dicts (list[dict]): list of dataset dicts.
class_names (list[str]): list of class names (zero-indexed).
"""
num_classes = len(class_names)
hist_bins = np.arange(num_classes + 1)
histogram = np.zeros((num_classes,), dtype=int)
for entry in dataset_dicts:
annos = entry["annotations"]
classes = np.asarray(
[x["category_id"] for x in annos if not x.get("iscrowd", 0)], dtype=int
)
if len(classes):
assert classes.min() >= 0, f"Got an invalid category_id={classes.min()}"
assert (
classes.max() < num_classes
), f"Got an invalid category_id={classes.max()} for a dataset of {num_classes} classes"
histogram += np.histogram(classes, bins=hist_bins)[0]
N_COLS = min(6, len(class_names) * 2)
def short_name(x):
# make long class names shorter. useful for lvis
if len(x) > 13:
return x[:11] + ".."
return x
data = list(
itertools.chain(*[[short_name(class_names[i]), int(v)] for i, v in enumerate(histogram)])
)
total_num_instances = sum(data[1::2])
data.extend([None] * (N_COLS - (len(data) % N_COLS)))
if num_classes > 1:
data.extend(["total", total_num_instances])
data = itertools.zip_longest(*[data[i::N_COLS] for i in range(N_COLS)])
table = tabulate(
data,
headers=["category", "#instances"] * (N_COLS // 2),
tablefmt="pipe",
numalign="left",
stralign="center",
)
log_first_n(
logging.INFO,
"Distribution of instances among all {} categories:\n".format(num_classes)
+ colored(table, "cyan"),
key="message",
)
def get_detection_dataset_dicts(
names,
filter_empty=True,
min_keypoints=0,
proposal_files=None,
check_consistency=True,
):
"""
Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation.
Args:
names (str or list[str]): a dataset name or a list of dataset names
filter_empty (bool): whether to filter out images without instance annotations
min_keypoints (int): filter out images with fewer keypoints than
`min_keypoints`. Set to 0 to do nothing.
proposal_files (list[str]): if given, a list of object proposal files
that match each dataset in `names`.
check_consistency (bool): whether to check if datasets have consistent metadata.
Returns:
list[dict]: a list of dicts following the standard dataset dict format.
"""
if isinstance(names, str):
names = [names]
assert len(names), names
| # Copyright (c) Facebook, Inc. and its affiliates.
"""
This file contains the default logic to build a dataloader for training or testing.
"""
__all__ = [
"build_batch_data_loader",
"build_detection_train_loader",
"build_detection_test_loader",
"get_detection_dataset_dicts",
"load_proposals_into_dataset",
"print_instances_class_histogram",
]
def filter_images_with_only_crowd_annotations(dataset_dicts):
"""
Filter out images with none annotations or only crowd annotations
(i.e., images without non-crowd annotations).
A common training-time preprocessing on COCO dataset.
Args:
dataset_dicts (list[dict]): annotations in Detectron2 Dataset format.
Returns:
list[dict]: the same format, but filtered.
"""
num_before = len(dataset_dicts)
def valid(anns):
for ann in anns:
if ann.get("iscrowd", 0) == 0:
return True
return False
dataset_dicts = [x for x in dataset_dicts if valid(x["annotations"])]
num_after = len(dataset_dicts)
logger = logging.getLogger(__name__)
logger.info(
"Removed {} images with no usable annotations. {} images left.".format(
num_before - num_after, num_after
)
)
return dataset_dicts
def filter_images_with_few_keypoints(dataset_dicts, min_keypoints_per_image):
"""
Filter out images with too few number of keypoints.
Args:
dataset_dicts (list[dict]): annotations in Detectron2 Dataset format.
Returns:
list[dict]: the same format as dataset_dicts, but filtered.
"""
num_before = len(dataset_dicts)
def visible_keypoints_in_image(dic):
# Each keypoints field has the format [x1, y1, v1, ...], where v is visibility
annotations = dic["annotations"]
return sum(
(np.array(ann["keypoints"][2::3]) > 0).sum()
for ann in annotations
if "keypoints" in ann
)
dataset_dicts = [
x for x in dataset_dicts if visible_keypoints_in_image(x) >= min_keypoints_per_image
]
num_after = len(dataset_dicts)
logger = logging.getLogger(__name__)
logger.info(
"Removed {} images with fewer than {} keypoints.".format(
num_before - num_after, min_keypoints_per_image
)
)
return dataset_dicts
def load_proposals_into_dataset(dataset_dicts, proposal_file):
"""
Load precomputed object proposals into the dataset.
The proposal file should be a pickled dict with the following keys:
- "ids": list[int] or list[str], the image ids
- "boxes": list[np.ndarray], each is an Nx4 array of boxes corresponding to the image id
- "objectness_logits": list[np.ndarray], each is an N sized array of objectness scores
corresponding to the boxes.
- "bbox_mode": the BoxMode of the boxes array. Defaults to ``BoxMode.XYXY_ABS``.
Args:
dataset_dicts (list[dict]): annotations in Detectron2 Dataset format.
proposal_file (str): file path of pre-computed proposals, in pkl format.
Returns:
list[dict]: the same format as dataset_dicts, but added proposal field.
"""
logger = logging.getLogger(__name__)
logger.info("Loading proposals from: {}".format(proposal_file))
with PathManager.open(proposal_file, "rb") as f:
proposals = pickle.load(f, encoding="latin1")
# Rename the key names in D1 proposal files
rename_keys = {"indexes": "ids", "scores": "objectness_logits"}
for key in rename_keys:
if key in proposals:
proposals[rename_keys[key]] = proposals.pop(key)
# Fetch the indexes of all proposals that are in the dataset
# Convert image_id to str since they could be int.
img_ids = set({str(record["image_id"]) for record in dataset_dicts})
id_to_index = {str(id): i for i, id in enumerate(proposals["ids"]) if str(id) in img_ids}
# Assuming default bbox_mode of precomputed proposals are 'XYXY_ABS'
bbox_mode = BoxMode(proposals["bbox_mode"]) if "bbox_mode" in proposals else BoxMode.XYXY_ABS
for record in dataset_dicts:
# Get the index of the proposal
i = id_to_index[str(record["image_id"])]
boxes = proposals["boxes"][i]
objectness_logits = proposals["objectness_logits"][i]
# Sort the proposals in descending order of the scores
inds = objectness_logits.argsort()[::-1]
record["proposal_boxes"] = boxes[inds]
record["proposal_objectness_logits"] = objectness_logits[inds]
record["proposal_bbox_mode"] = bbox_mode
return dataset_dicts
def print_instances_class_histogram(dataset_dicts, class_names):
"""
Args:
dataset_dicts (list[dict]): list of dataset dicts.
class_names (list[str]): list of class names (zero-indexed).
"""
num_classes = len(class_names)
hist_bins = np.arange(num_classes + 1)
histogram = np.zeros((num_classes,), dtype=int)
for entry in dataset_dicts:
annos = entry["annotations"]
classes = np.asarray(
[x["category_id"] for x in annos if not x.get("iscrowd", 0)], dtype=int
)
if len(classes):
assert classes.min() >= 0, f"Got an invalid category_id={classes.min()}"
assert (
classes.max() < num_classes
), f"Got an invalid category_id={classes.max()} for a dataset of {num_classes} classes"
histogram += np.histogram(classes, bins=hist_bins)[0]
N_COLS = min(6, len(class_names) * 2)
def short_name(x):
# make long class names shorter. useful for lvis
if len(x) > 13:
return x[:11] + ".."
return x
data = list(
itertools.chain(*[[short_name(class_names[i]), int(v)] for i, v in enumerate(histogram)])
)
total_num_instances = sum(data[1::2])
data.extend([None] * (N_COLS - (len(data) % N_COLS)))
if num_classes > 1:
data.extend(["total", total_num_instances])
data = itertools.zip_longest(*[data[i::N_COLS] for i in range(N_COLS)])
table = tabulate(
data,
headers=["category", "#instances"] * (N_COLS // 2),
tablefmt="pipe",
numalign="left",
stralign="center",
)
log_first_n(
logging.INFO,
"Distribution of instances among all {} categories:\n".format(num_classes)
+ colored(table, "cyan"),
key="message",
)
def get_detection_dataset_dicts(
names,
filter_empty=True,
min_keypoints=0,
proposal_files=None,
check_consistency=True,
):
"""
Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation.
Args:
names (str or list[str]): a dataset name or a list of dataset names
filter_empty (bool): whether to filter out images without instance annotations
min_keypoints (int): filter out images with fewer keypoints than
`min_keypoints`. Set to 0 to do nothing.
proposal_files (list[str]): if given, a list of object proposal files
that match each dataset in `names`.
check_consistency (bool): whether to check if datasets have consistent metadata.
Returns:
list[dict]: a list of dicts following the standard dataset dict format.
"""
if isinstance(names, str):
names = [names]
assert len(names), names
| available_datasets = DatasetCatalog.keys() | 7 | 2023-12-05 01:13:31+00:00 | 16k |
upfusion3d/upfusion | control_net/ldm/models/diffusion/ddpm.py | [
{
"identifier": "log_txt_as_img",
"path": "control_net/ldm/util.py",
"snippet": "def log_txt_as_img(wh, xc, size=10):\n # wh a tuple of (width, height)\n # xc a list of captions to plot\n b = len(xc)\n txts = list()\n for bi in range(b):\n txt = Image.new(\"RGB\", wh, color=\"white... | import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
import itertools
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager, nullcontext
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from omegaconf import ListConfig
from control_net.ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from control_net.ldm.modules.ema import LitEma
from control_net.ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from control_net.ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
from control_net.ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from control_net.ldm.models.diffusion.ddim import DDIMSampler | 11,165 | """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
make_it_fit=False,
ucg_training=None,
reset_ema=False,
reset_num_ema_updates=False,
):
super().__init__()
assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
self.parameterization = parameterization
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
self.make_it_fit = make_it_fit
| """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
make_it_fit=False,
ucg_training=None,
reset_ema=False,
reset_num_ema_updates=False,
):
super().__init__()
assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
self.parameterization = parameterization
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
self.make_it_fit = make_it_fit | if reset_ema: assert exists(ckpt_path) | 1 | 2023-12-12 00:49:11+00:00 | 16k |
modelscope/normal-depth-diffusion | scripts/t2i.py | [
{
"identifier": "DDIMSampler",
"path": "ldm/models/diffusion/ddim.py",
"snippet": "class DDIMSampler(object):\n\n def __init__(self, model, schedule='linear', **kwargs):\n super().__init__()\n self.model = model\n self.ddpm_num_timesteps = model.num_timesteps\n self.schedu... | import argparse
import glob
import os
import pdb
import sys
import time
import cv2
import numpy as np
import torch
from contextlib import contextmanager, nullcontext
from itertools import islice
from diffusers.pipelines.stable_diffusion.safety_checker import \
StableDiffusionSafetyChecker
from einops import rearrange, repeat
from imwatermark import WatermarkEncoder
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.dpm_solver import DPMSolverSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.util import instantiate_from_config
from model_zoo import build_model
from omegaconf import OmegaConf
from PIL import Image
from pytorch_lightning import seed_everything
from torch import autocast
from torchvision.utils import make_grid
from tqdm import tqdm, trange
from transformers import AutoFeatureExtractor
from utils.color_transfer import (map_2_16bit, map_16bit_2_8, split_rgbd,
split_rgbd_only_tensor, split_rgbd_tensor) | 10,863 | default=42,
help='the seed (for reproducible sampling)',
)
parser.add_argument(
'--precision',
type=str,
help='evaluate at this precision',
choices=['full', 'autocast'],
default='autocast')
opt = parser.parse_args()
seed_everything(opt.seed)
ckpt_name = os.path.splitext(os.path.basename(opt.ckpt))[0]
outdir = os.path.join(opt.save_dir, ckpt_name)
os.makedirs(outdir, exist_ok=True)
outpath = outdir
# config = OmegaConf.load(f"{opt.config}")
# model = load_model_from_config(config, f"{opt.ckpt}")
model = build_model('nd', opt.ckpt, strict=False)
device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
model = model.to(device)
if opt.dpm_solver:
sampler = DPMSolverSampler(model)
elif opt.plms:
sampler = PLMSSampler(model)
else:
sampler = DDIMSampler(model)
print(
'Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...'
)
wm = 'StableDiffusionV1'
wm_encoder = WatermarkEncoder()
wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
batch_size = opt.n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
prompt = opt.prompt
assert prompt is not None
data = [batch_size * [prompt]]
else:
print(f'reading prompts from {opt.from_file}')
with open(opt.from_file, 'r') as f:
data = f.read().splitlines()
data = list(chunk(data, batch_size))
if opt.prompt is None:
prompts = [
'a close up of a sheet of pizza on a table.',
'A picture of some lemons on a table.',
'A little girl with a pink bow in her hair eating broccoli.',
'A highly detailed stone bust of Theodoros Kolokotronis',
]
else:
prompts = [opt.prompt]
sub = ''
for prompt_id, prompt in enumerate(prompts):
if prompt[-1] == '.':
prompt = prompt[:-1]
data = [batch_size * [prompt + sub]]
save_path = os.path.join(outpath, 'normal-depth')
os.makedirs(save_path, exist_ok=True)
base_count = prompt_id
grid_count = prompt_id
start_code = None
if opt.fixed_code:
start_code = torch.randn(
[opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f],
device=device)
precision_scope = autocast if opt.precision == 'autocast' else nullcontext
with torch.no_grad():
with precision_scope('cuda'):
tic = time.time()
all_samples = list()
for n in trange(opt.n_iter, desc='Sampling'):
for prompts in tqdm(data, desc='data'):
uc = None
if opt.scale != 1.0:
# uc = model.get_learned_conditioning(batch_size * [""])
uc = model.get_learned_conditioning(
batch_size * [NEGATIVE_PROMPTS])
if isinstance(prompts, tuple):
prompts = list(prompts)
c = model.get_learned_conditioning(prompts)
shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
samples_ddim, _ = sampler.sample(
S=50,
conditioning=c,
batch_size=opt.n_samples,
shape=shape,
verbose=False,
unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,
eta=opt.ddim_eta,
x_T=start_code)
'''
rgbd= (samples_ddim[0]).permute(1,2,0)
rgb = rgbd[...,:3]
depth = rgbd[...,3]
cv2.imwrite('rgb.png',((rgb.clamp(-1,1).detach().cpu().numpy()+1)[...,::-1] /2 * 255).astype(np.uint8))
cv2.imwrite('depth.png', ((depth.clamp(-1,1).detach().cpu().numpy()+1) /2 * 255).astype(np.uint8))
'''
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp(
(x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
| sys.path.append('./')
# load safety model
'''
safety_model_id = "CompVis/stable-diffusion-safety-checker"
safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
'''
NEGATIVE_PROMPTS = 'ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, disfigured, deformed, body out of frame, blurry, bad anatomy, blurred, watermark, grainy, signature, cut off, draft.'
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def numpy_to_pil(images):
"""
Convert a numpy image or a batch of images to a PIL image.
"""
if images.ndim == 3:
images = images[None, ...]
images = (images * 255).round().astype('uint8')
pil_images = [Image.fromarray(image) for image in images]
return pil_images
def load_model_from_config(config, ckpt, verbose=False):
print(f'Loading model from {ckpt}')
pl_sd = torch.load(ckpt, map_location='cpu')
if 'global_step' in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd['state_dict']
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print('missing keys:')
print(m)
if len(u) > 0 and verbose:
print('unexpected keys:')
print(u)
model.cuda()
model.eval()
return model
def put_watermark(img, wm_encoder=None):
if wm_encoder is not None:
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
img = wm_encoder.encode(img, 'dwtDct')
img = Image.fromarray(img[:, :, ::-1])
return img
def load_replacement(x):
try:
hwc = x.shape
y = Image.open('assets/rick.jpeg').convert('RGB').resize(
(hwc[1], hwc[0]))
y = (np.array(y) / 255.0).astype(x.dtype)
assert y.shape == x.shape
return y
except Exception:
return x
def check_safety(x_image):
return x_image, False
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--prompt',
type=str,
nargs='?',
default=None,
help='the prompt to render')
parser.add_argument(
'--save_dir',
type=str,
nargs='?',
help='dir to write results to',
default='outputs/txt2img-samples')
parser.add_argument(
'--skip_grid',
action='store_true',
help=
'do not save a grid, only individual samples. Helpful when evaluating lots of samples',
)
parser.add_argument(
'--skip_save',
action='store_true',
help='do not save individual samples. For speed measurements.',
)
parser.add_argument(
'--ddim_steps',
type=int,
default=50,
help='number of ddim sampling steps',
)
parser.add_argument(
'--plms',
action='store_true',
help='use plms sampling',
)
parser.add_argument(
'--dpm_solver',
action='store_true',
help='use dpm_solver sampling',
)
parser.add_argument(
'--laion400m',
action='store_true',
help='uses the LAION400M model',
)
parser.add_argument(
'--fixed_code',
action='store_true',
help='if enabled, uses the same starting code across samples ',
)
parser.add_argument(
'--ddim_eta',
type=float,
default=0.0,
help='ddim eta (eta=0.0 corresponds to deterministic sampling',
)
parser.add_argument(
'--n_iter',
type=int,
default=1,
help='sample this often',
)
parser.add_argument(
'--H',
type=int,
default=512,
help='image height, in pixel space',
)
parser.add_argument(
'--W',
type=int,
default=512,
help='image width, in pixel space',
)
parser.add_argument(
'--C',
type=int,
default=4,
help='latent channels',
)
parser.add_argument(
'--f',
type=int,
default=8,
help='downsampling factor',
)
parser.add_argument(
'--n_samples',
type=int,
default=1,
help=
'how many samples to produce for each given prompt. A.k.a. batch size',
)
parser.add_argument(
'--n_rows',
type=int,
default=0,
help='rows in the grid (default: n_samples)',
)
parser.add_argument(
'--scale',
type=float,
default=7.5,
help=
'unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))',
)
parser.add_argument(
'--from-file',
type=str,
help='if specified, load prompts from this file',
)
parser.add_argument(
'--config',
type=str,
default='./configs/inference/nd/nd-1.5-inference.yaml',
help='path to config which constructs model',
)
parser.add_argument(
'--ckpt',
type=str,
default='models/ldm/txt2depth/last.ckpt',
help='path to checkpoint of model',
)
parser.add_argument(
'--seed',
type=int,
default=42,
help='the seed (for reproducible sampling)',
)
parser.add_argument(
'--precision',
type=str,
help='evaluate at this precision',
choices=['full', 'autocast'],
default='autocast')
opt = parser.parse_args()
seed_everything(opt.seed)
ckpt_name = os.path.splitext(os.path.basename(opt.ckpt))[0]
outdir = os.path.join(opt.save_dir, ckpt_name)
os.makedirs(outdir, exist_ok=True)
outpath = outdir
# config = OmegaConf.load(f"{opt.config}")
# model = load_model_from_config(config, f"{opt.ckpt}")
model = build_model('nd', opt.ckpt, strict=False)
device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
model = model.to(device)
if opt.dpm_solver:
sampler = DPMSolverSampler(model)
elif opt.plms:
sampler = PLMSSampler(model)
else:
sampler = DDIMSampler(model)
print(
'Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...'
)
wm = 'StableDiffusionV1'
wm_encoder = WatermarkEncoder()
wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
batch_size = opt.n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
prompt = opt.prompt
assert prompt is not None
data = [batch_size * [prompt]]
else:
print(f'reading prompts from {opt.from_file}')
with open(opt.from_file, 'r') as f:
data = f.read().splitlines()
data = list(chunk(data, batch_size))
if opt.prompt is None:
prompts = [
'a close up of a sheet of pizza on a table.',
'A picture of some lemons on a table.',
'A little girl with a pink bow in her hair eating broccoli.',
'A highly detailed stone bust of Theodoros Kolokotronis',
]
else:
prompts = [opt.prompt]
sub = ''
for prompt_id, prompt in enumerate(prompts):
if prompt[-1] == '.':
prompt = prompt[:-1]
data = [batch_size * [prompt + sub]]
save_path = os.path.join(outpath, 'normal-depth')
os.makedirs(save_path, exist_ok=True)
base_count = prompt_id
grid_count = prompt_id
start_code = None
if opt.fixed_code:
start_code = torch.randn(
[opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f],
device=device)
precision_scope = autocast if opt.precision == 'autocast' else nullcontext
with torch.no_grad():
with precision_scope('cuda'):
tic = time.time()
all_samples = list()
for n in trange(opt.n_iter, desc='Sampling'):
for prompts in tqdm(data, desc='data'):
uc = None
if opt.scale != 1.0:
# uc = model.get_learned_conditioning(batch_size * [""])
uc = model.get_learned_conditioning(
batch_size * [NEGATIVE_PROMPTS])
if isinstance(prompts, tuple):
prompts = list(prompts)
c = model.get_learned_conditioning(prompts)
shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
samples_ddim, _ = sampler.sample(
S=50,
conditioning=c,
batch_size=opt.n_samples,
shape=shape,
verbose=False,
unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,
eta=opt.ddim_eta,
x_T=start_code)
'''
rgbd= (samples_ddim[0]).permute(1,2,0)
rgb = rgbd[...,:3]
depth = rgbd[...,3]
cv2.imwrite('rgb.png',((rgb.clamp(-1,1).detach().cpu().numpy()+1)[...,::-1] /2 * 255).astype(np.uint8))
cv2.imwrite('depth.png', ((depth.clamp(-1,1).detach().cpu().numpy()+1) /2 * 255).astype(np.uint8))
'''
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp(
(x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) | x_samples_ddim = split_rgbd_only_tensor(x_samples_ddim) | 8 | 2023-12-06 07:29:34+00:00 | 16k |
nox-410/tvm.tl | python/tvm/topi/arm_cpu/conv2d_gemm.py | [
{
"identifier": "get_const_tuple",
"path": "python/tvm/topi/utils.py",
"snippet": "def get_const_tuple(in_tuple):\n \"\"\"Verifies input tuple is IntImm or Var, returns tuple of int or Var.\n\n Parameters\n ----------\n in_tuple : tuple of Expr\n The input.\n\n Returns\n -------... | import tvm
from tvm.target import Target
from tvm import te
from tvm.topi import nn
from tvm.autotvm.task.space import AnnotateEntity, ReorderEntity, OtherOptionEntity
from ..utils import get_const_tuple, get_const_int
from ..nn.utils import get_pad_tuple
from .tensor_intrin import (
gemm_4x4_int8_int8_int32,
gemm_acc_4x4_int8_int8_int32,
gemm_acc_nx16_int8_int8_int32,
gemm_acc_2x2_int8_int8_int32,
) | 13,310 | )
zero = tvm.tir.const(0)
else:
# No need to pack/unpack, execute GEMM directly
C = te.compute(
(batches, M_padded, N_padded),
lambda b, x, y: te.sum(
A[b, x, k].astype("int32")
* B_interleaved_t[
y // tile_rows_B, k // tile_cols_B, idxm(y, tile_rows_B), idxm(k, tile_cols_B)
].astype("int32"),
axis=k,
),
name="C",
)
# We need to ensure that infer bound pass does not remove the padding
# which is necessary for the tensorizations to work. So we need to
# add a dummy reference to the padding area of the result
zero = (
tvm.tir.const(1, C.dtype) * C[0, M_padded - 1, N_padded - 1]
- tvm.tir.const(1, C.dtype) * C[0, M_padded - 1, N_padded - 1]
)
# Reshape the result into a convolution output
out_shape = (batches, OH, OW, OC)
out = te.compute(
out_shape,
lambda b, x, y, z: (C(b, y + OW * x, z) + zero).astype(out_dtype),
name="conv2d_gemm_output",
)
return out
def schedule_conv2d_gemm_interleaved(cfg, s, out, final_out):
"""Schedule the conv2d_gemm interleaved strategy"""
C = out.op.input_tensors[0]
C_interleaved = C.op.input_tensors[0]
A_interleaved = C_interleaved.op.input_tensors[0]
# Input transform
A_interleaved_input = A_interleaved.op.input_tensors[0]
if A_interleaved_input.op.name == "A_padded_K" or A_interleaved_input.op.name == "A_padded_M":
s[A_interleaved_input].compute_at(s[A_interleaved], A_interleaved.op.axis[3])
s[A_interleaved_input].vectorize(A_interleaved_input.op.axis[2])
s[A_interleaved_input].compute_inline()
data_im2col = A_interleaved_input.op.input_tensors[0]
else:
data_im2col = A_interleaved_input
b, m, n = data_im2col.op.axis
if data_im2col.op.name == "data_im2col":
n_size = data_im2col.shape[2]
if n_size % 16 == 0:
split_factor = 16
else:
split_factor = 8
n_outer, n_inner = s[data_im2col].split(n, split_factor)
s[data_im2col].unroll(n_outer)
s[data_im2col].vectorize(n_inner)
b_m_fused = s[data_im2col].fuse(b, m)
s[data_im2col].parallel(b_m_fused)
else:
s[data_im2col].compute_inline()
# Computation(through tensorize)
b, xo, yo, xi, yi = C_interleaved.op.axis[0:5]
outer_gemm, inner_gemm = cfg["reorder_gemm"].apply(s, C_interleaved, [xo, yo])
b_outer_gemm_fused = s[C_interleaved].fuse(b, outer_gemm)
s[C_interleaved].parallel(b_outer_gemm_fused)
s[A_interleaved].compute_at(s[C_interleaved], b_outer_gemm_fused)
_, _, _, outer_A_interleaved, inner_A_interleaved = A_interleaved.op.axis
cfg["A_interleaved_unroll_vec"].apply(
s, A_interleaved, [outer_A_interleaved, inner_A_interleaved]
)
in_type = A_interleaved.dtype
out_type = C.dtype
k = C_interleaved.op.reduce_axis[0]
_, M, N = C.shape
if in_type in ["int8", "uint8"]:
target = Target.current(allow_none=False)
if target.features.has_matmul_i8:
gemm_acc = gemm_acc_2x2_int8_int8_int32(in_type)
xi_inner, yi_inner = C_interleaved.op.axis[-2:]
k_outer, k_inner = s[C_interleaved].split(k, 8)
s[C_interleaved].reorder(
b_outer_gemm_fused, inner_gemm, k_outer, xi, yi, xi_inner, yi_inner, k_inner
)
s[C_interleaved].tensorize(xi_inner, gemm_acc)
s[C_interleaved].unroll(xi)
s[C_interleaved].unroll(yi)
elif target.features.has_dotprod:
gemm_acc = gemm_acc_4x4_int8_int8_int32(in_type)
xi_outer, yi_outer, xi_inner, yi_inner = s[C_interleaved].tile(
xi, yi, x_factor=8, y_factor=4
)
k_outer, k_inner = s[C_interleaved].split(k, 4)
xi_inner_outer, xi_inner_inner = s[C_interleaved].split(xi_inner, 4)
s[C_interleaved].reorder(
b_outer_gemm_fused,
inner_gemm,
xi_outer,
yi_outer,
k_outer,
xi_inner_outer,
xi_inner_inner,
yi_inner,
k_inner,
)
s[C_interleaved].tensorize(xi_inner_inner, gemm_acc)
s[C_interleaved].unroll(xi_inner_outer)
elif target.features.has_asimd:
s[C_interleaved].reorder(yi, xi)
K = A_interleaved_input.shape[2]
assert in_type in ["int8", "uint8"], "Only int8 and uint8 gemm are supported"
unroll = cfg["gemm_quantized_unroll"].val
| # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# pylint: disable=invalid-name, unused-variable, too-many-locals
# pylint: disable=unused-argument, redefined-builtin
"""GEMM Convolution schedule on ARM"""
def configure_knobs(cfg, M, K, target):
"""Configure auto-tuning knobs for the interleaved strategy"""
x, y = cfg.axis(M // 4), cfg.axis(K // 16)
cfg.define_reorder("reorder_gemm", [x, y], policy="candidate", candidate=[[x, y], [y, x]])
outer_loop, inner_loop = cfg.axis(4), cfg.axis(16)
cfg.define_annotate(
"A_interleaved_unroll_vec", [outer_loop, inner_loop], policy="try_unroll_vec"
)
# Fallback configuration
if cfg.is_fallback:
cfg["reorder_gemm"] = ReorderEntity([0, 1])
cfg["A_interleaved_unroll_vec"] = AnnotateEntity(["unroll", "vec"])
if not target.features.has_dotprod:
cfg.define_knob("gemm_quantized_unroll", [True, False])
if cfg.is_fallback:
cfg["gemm_quantized_unroll"] = OtherOptionEntity(False)
# Compute function
def compute_conv2d_gemm_without_weight_transform(
cfg,
data,
B_interleaved_t,
strides,
padding,
dilation,
out_dtype,
kernel_size,
output_channels,
interleave_A,
):
"""Compute conv2d by transforming the input,
executing GEMM and transforming the output back"""
batches, IH, IW, IC = get_const_tuple(data.shape)
KH, KW = get_const_tuple(kernel_size)
OC = get_const_int(output_channels)
kernel_area = KH * KW
if isinstance(dilation, int):
dilation_h = dilation_w = dilation
else:
dilation_h, dilation_w = get_const_tuple(dilation)
dilated_kernel_h = (KH - 1) * dilation_h + 1
dilated_kernel_w = (KW - 1) * dilation_w + 1
pad_top, pad_left, pad_down, pad_right = get_pad_tuple(
padding, (dilated_kernel_h, dilated_kernel_w)
)
HSTR, WSTR = strides if isinstance(strides, (tuple, list)) else (strides, strides)
OH = (IH + pad_top + pad_down - dilated_kernel_h) // HSTR + 1
OW = (IW + pad_left + pad_right - dilated_kernel_w) // WSTR + 1
if pad_top or pad_left:
data_pad = nn.pad(
data, [0, pad_top, pad_left, 0], [0, pad_down, pad_right, 0], name="data_pad"
)
else:
data_pad = data
# Im2col
M = OH * OW
K = IC * kernel_area
N = OC
A_shape = (batches, M, K)
if kernel_area == 1:
A = tvm.topi.reshape(data_pad, A_shape)
else:
A = te.compute(
A_shape,
lambda n, x, y: data_pad[
n,
HSTR * (x // OW) + dilation_h * ((y // IC) // KW),
WSTR * (x % OW) + dilation_w * ((y // IC) % KW),
y % IC,
],
name="data_im2col",
)
# Pad if necessary
N_transformed = B_interleaved_t.shape[0]
tile_rows_B = B_interleaved_t.shape[2]
tile_cols_B = B_interleaved_t.shape[3]
# Select the tiling strategy for A.
# The tiling information is chosen to maximize register usage during
# the tile computation.
#
# Please refer to:
# - https://discuss.tvm.apache.org/t/rfc-improve-quantized-convolution-performance-for-armv8-architectures # pylint: disable=line-too-long
# - https://discuss.tvm.apache.org/t/rfc-accelerate-quantized-convolution-through-dot-product
# - https://discuss.tvm.apache.org/t/rfc-improve-quantized-convolution-through-mmla-instruction
# - Conv2DGemmWeightTransformRel in src/relay/op/nn/convolution.h
# In order to have more information
#
target = Target.current(allow_none=False)
if target.features.has_matmul_i8:
# If smmla/ummla is enabled, we are loading 8 rows from A. Each row
# will contain 8 elements
tile_rows_A = 8
tile_cols_A = 8
elif target.features.has_dotprod and interleave_A:
# If dot product has been enabled, and we are interleaving A
# tile size should be 8x4
tile_rows_A = 8
tile_cols_A = 4
else:
# If either there is no dot product or if we are using a native strategy
# tile size should be 4x16
tile_rows_A = 4
tile_cols_A = 16
pad_M = 0
pad_K = 0
if M % tile_rows_A != 0:
pad_M = tile_rows_A - (M % tile_rows_A)
if K % tile_cols_A != 0:
pad_K = tile_cols_A - (K % tile_cols_A)
M_padded = M + pad_M
K_padded = K + pad_K
N_padded = N_transformed * tile_rows_B
pad_before = (0, 0, 0)
pad_after = (0, pad_M, pad_K)
if pad_K != 0:
A = nn.pad(A, pad_before=pad_before, pad_after=pad_after, name="A_padded_K")
elif pad_M != 0:
A = nn.pad(A, pad_before=pad_before, pad_after=pad_after, name="A_padded_M")
idxm = tvm.tir.indexmod
k = te.reduce_axis((0, K_padded), "k")
if interleave_A:
# Configuration space
configure_knobs(cfg, M_padded, K_padded, target)
# Pack the input data
A_interleaved = te.compute(
(batches, M_padded // tile_rows_A, K_padded // tile_cols_A, tile_rows_A, tile_cols_A),
lambda b, x, y, z, w: A[b, z + tile_rows_A * x, w + tile_cols_A * y],
name="A_interleaved",
)
target = Target.current(allow_none=False)
if target.features.has_matmul_i8:
# Execute GEMM. In the case of mmla, we need to enforce the tiling
# from the compute. This is because mmla is doing a tiled computation
# as well. So we have a big 8x12 tile, with small 2x2 sub-tiles
# generated by mmla. In theory we could make the tile 2x2 and
# fuse and split during scheduling, but this would not work
# because of possible padding
C_interleaved = te.compute(
(
batches,
M_padded // tile_rows_A,
N_transformed,
tile_rows_A // 2,
tile_rows_B // 2,
2,
2,
),
lambda b, x, y, w, z, s, t: te.sum(
A_interleaved[b, x, k // tile_cols_A, 2 * w + s, idxm(k, tile_cols_A)].astype(
"int32"
)
* B_interleaved_t[y, k // tile_cols_B, 2 * z + t, idxm(k, tile_cols_B)].astype(
"int32"
),
axis=k,
),
name="C_interleaved",
)
# Ensure the padding needed for tensorize does not get removed during tir passes
# by adding a dummy reference to the specific padded area of the result
zero = (
tvm.tir.const(1, C_interleaved.dtype)
* C_interleaved[
batches - 1,
M // tile_rows_A,
N_transformed - 1,
idxm(M, tile_rows_A) // 2,
tile_rows_B // 2 - 1,
1,
1,
]
- tvm.tir.const(1, C_interleaved.dtype)
* C_interleaved[
batches - 1,
M // tile_rows_A,
N_transformed - 1,
idxm(M, tile_rows_A) // 2,
tile_rows_B // 2 - 1,
1,
1,
]
)
# Unpack the result
C = te.compute(
(batches, M, N),
lambda b, x, y: (
C_interleaved[
b,
x // tile_rows_A,
y // tile_rows_B,
idxm(x, tile_rows_A) // 2,
idxm(y, tile_rows_B) // 2,
idxm(idxm(x, tile_rows_A), 2),
idxm(idxm(y, tile_rows_B), 2),
]
+ zero
).astype(out_dtype),
name="C",
)
else:
# Execute GEMM
C_interleaved = te.compute(
(batches, M_padded // tile_rows_A, N_transformed, tile_rows_A, tile_rows_B),
lambda b, x, y, w, z: te.sum(
A_interleaved[b, x, k // tile_cols_A, w, idxm(k, tile_cols_A)].astype("int32")
* B_interleaved_t[y, k // tile_cols_B, z, idxm(k, tile_cols_B)].astype("int32"),
axis=k,
),
name="C_interleaved",
)
# Unpack the result
C = te.compute(
(batches, M, N),
lambda b, x, y: C_interleaved[
b,
x // tile_rows_A,
y // tile_rows_B,
idxm(x, tile_rows_A),
idxm(y, tile_rows_B),
].astype(out_dtype),
name="C",
)
zero = tvm.tir.const(0)
else:
# No need to pack/unpack, execute GEMM directly
C = te.compute(
(batches, M_padded, N_padded),
lambda b, x, y: te.sum(
A[b, x, k].astype("int32")
* B_interleaved_t[
y // tile_rows_B, k // tile_cols_B, idxm(y, tile_rows_B), idxm(k, tile_cols_B)
].astype("int32"),
axis=k,
),
name="C",
)
# We need to ensure that infer bound pass does not remove the padding
# which is necessary for the tensorizations to work. So we need to
# add a dummy reference to the padding area of the result
zero = (
tvm.tir.const(1, C.dtype) * C[0, M_padded - 1, N_padded - 1]
- tvm.tir.const(1, C.dtype) * C[0, M_padded - 1, N_padded - 1]
)
# Reshape the result into a convolution output
out_shape = (batches, OH, OW, OC)
out = te.compute(
out_shape,
lambda b, x, y, z: (C(b, y + OW * x, z) + zero).astype(out_dtype),
name="conv2d_gemm_output",
)
return out
def schedule_conv2d_gemm_interleaved(cfg, s, out, final_out):
"""Schedule the conv2d_gemm interleaved strategy"""
C = out.op.input_tensors[0]
C_interleaved = C.op.input_tensors[0]
A_interleaved = C_interleaved.op.input_tensors[0]
# Input transform
A_interleaved_input = A_interleaved.op.input_tensors[0]
if A_interleaved_input.op.name == "A_padded_K" or A_interleaved_input.op.name == "A_padded_M":
s[A_interleaved_input].compute_at(s[A_interleaved], A_interleaved.op.axis[3])
s[A_interleaved_input].vectorize(A_interleaved_input.op.axis[2])
s[A_interleaved_input].compute_inline()
data_im2col = A_interleaved_input.op.input_tensors[0]
else:
data_im2col = A_interleaved_input
b, m, n = data_im2col.op.axis
if data_im2col.op.name == "data_im2col":
n_size = data_im2col.shape[2]
if n_size % 16 == 0:
split_factor = 16
else:
split_factor = 8
n_outer, n_inner = s[data_im2col].split(n, split_factor)
s[data_im2col].unroll(n_outer)
s[data_im2col].vectorize(n_inner)
b_m_fused = s[data_im2col].fuse(b, m)
s[data_im2col].parallel(b_m_fused)
else:
s[data_im2col].compute_inline()
# Computation(through tensorize)
b, xo, yo, xi, yi = C_interleaved.op.axis[0:5]
outer_gemm, inner_gemm = cfg["reorder_gemm"].apply(s, C_interleaved, [xo, yo])
b_outer_gemm_fused = s[C_interleaved].fuse(b, outer_gemm)
s[C_interleaved].parallel(b_outer_gemm_fused)
s[A_interleaved].compute_at(s[C_interleaved], b_outer_gemm_fused)
_, _, _, outer_A_interleaved, inner_A_interleaved = A_interleaved.op.axis
cfg["A_interleaved_unroll_vec"].apply(
s, A_interleaved, [outer_A_interleaved, inner_A_interleaved]
)
in_type = A_interleaved.dtype
out_type = C.dtype
k = C_interleaved.op.reduce_axis[0]
_, M, N = C.shape
if in_type in ["int8", "uint8"]:
target = Target.current(allow_none=False)
if target.features.has_matmul_i8:
gemm_acc = gemm_acc_2x2_int8_int8_int32(in_type)
xi_inner, yi_inner = C_interleaved.op.axis[-2:]
k_outer, k_inner = s[C_interleaved].split(k, 8)
s[C_interleaved].reorder(
b_outer_gemm_fused, inner_gemm, k_outer, xi, yi, xi_inner, yi_inner, k_inner
)
s[C_interleaved].tensorize(xi_inner, gemm_acc)
s[C_interleaved].unroll(xi)
s[C_interleaved].unroll(yi)
elif target.features.has_dotprod:
gemm_acc = gemm_acc_4x4_int8_int8_int32(in_type)
xi_outer, yi_outer, xi_inner, yi_inner = s[C_interleaved].tile(
xi, yi, x_factor=8, y_factor=4
)
k_outer, k_inner = s[C_interleaved].split(k, 4)
xi_inner_outer, xi_inner_inner = s[C_interleaved].split(xi_inner, 4)
s[C_interleaved].reorder(
b_outer_gemm_fused,
inner_gemm,
xi_outer,
yi_outer,
k_outer,
xi_inner_outer,
xi_inner_inner,
yi_inner,
k_inner,
)
s[C_interleaved].tensorize(xi_inner_inner, gemm_acc)
s[C_interleaved].unroll(xi_inner_outer)
elif target.features.has_asimd:
s[C_interleaved].reorder(yi, xi)
K = A_interleaved_input.shape[2]
assert in_type in ["int8", "uint8"], "Only int8 and uint8 gemm are supported"
unroll = cfg["gemm_quantized_unroll"].val | gemm = gemm_4x4_int8_int8_int32(M, N, K, unroll, in_type) | 3 | 2023-12-14 02:37:47+00:00 | 16k |
yolain/ComfyUI-Easy-Use | py/easyNodes.py | [
{
"identifier": "advanced_encode",
"path": "py/adv_encode.py",
"snippet": "def advanced_encode(clip, text, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5,\n apply_to_pooled=True):\n tokenized = clip.tokenize(text, return_word_ids=True)\n if isinstance(cli... | import sys
import os
import re
import json
import time
import math
import torch
import psutil
import random
import datetime
import comfy.sd
import comfy.utils
import numpy as np
import folder_paths
import comfy.samplers
import comfy.controlnet
import latent_preview
import comfy.model_base
import comfy.model_management
from pathlib import Path
from comfy.sd import CLIP, VAE
from comfy.cli_args import args
from urllib.request import urlopen
from collections import defaultdict
from PIL.PngImagePlugin import PngInfo
from PIL import Image, ImageDraw, ImageFont
from comfy.model_patcher import ModelPatcher
from comfy_extras.chainner_models import model_loading
from typing import Dict, List, Optional, Tuple, Union, Any
from .adv_encode import advanced_encode, advanced_encode_XL
from server import PromptServer
from nodes import VAELoader, MAX_RESOLUTION, RepeatLatentBatch, NODE_CLASS_MAPPINGS as ALL_NODE_CLASS_MAPPINGS, ConditioningSetMask
from comfy_extras.nodes_mask import LatentCompositeMasked
from .config import BASE_RESOLUTIONS
from .log import log_node_info, log_node_error, log_node_warn, log_node_success
from .wildcards import process_with_loras, get_wildcard_list
from comfy_extras.nodes_stable3d import camera_embeddings
from .gradual_latent_hires_fix import sample_dpmpp_2s_ancestral, sample_dpmpp_2m_sde, sample_lcm, sample_euler_ancestral
from .dynthres_core import DynThresh | 10,886 |
self.image_list, self.max_width, self.max_height, self.latents_plot = self.sample_plot_image(
plot_image_vars, latent_image, preview_latent, self.latents_plot, self.image_list,
disable_noise, start_step, last_step, force_full_denoise, x_value, y_value)
self.num += 1
else:
# ttNl(f'{CC.GREY}X: {x_value_label}').t(f'Plot Values {self.num}/{self.total} ->').p()
self.image_list, self.max_width, self.max_height, self.latents_plot = self.sample_plot_image(
plot_image_vars, latent_image, preview_latent, self.latents_plot, self.image_list, disable_noise,
start_step, last_step, force_full_denoise, x_value)
self.num += 1
# Rearrange latent array to match preview image grid
self.latents_plot = self.rearrange_tensors(self.latents_plot, self.num_cols, self.num_rows)
# Concatenate the tensors along the first dimension (dim=0)
self.latents_plot = torch.cat(self.latents_plot, dim=0)
return self.latents_plot
def plot_images_and_labels(self):
# Calculate the background dimensions
bg_width, bg_height, x_offset_initial, y_offset = self.calculate_background_dimensions()
# Create the white background image
background = Image.new('RGBA', (int(bg_width), int(bg_height)), color=(255, 255, 255, 255))
output_image = []
for row_index in range(self.num_rows):
x_offset = x_offset_initial
for col_index in range(self.num_cols):
index = col_index * self.num_rows + row_index
img = self.image_list[index]
output_image.append(sampler.pil2tensor(img))
background.paste(img, (x_offset, y_offset))
# Handle X label
if row_index == 0 and self.x_type != "None":
label_bg = self.create_label(img, self.x_label[col_index], int(48 * img.width / 512))
label_y = (y_offset - label_bg.height) // 2
background.alpha_composite(label_bg, (x_offset, label_y))
# Handle Y label
if col_index == 0 and self.y_type != "None":
label_bg = self.create_label(img, self.y_label[row_index], int(48 * img.height / 512), False)
label_bg = label_bg.rotate(90, expand=True)
label_x = (x_offset - label_bg.width) // 2
label_y = y_offset + (img.height - label_bg.height) // 2
background.alpha_composite(label_bg, (label_x, label_y))
x_offset += img.width + self.grid_spacing
y_offset += img.height + self.grid_spacing
return (sampler.pil2tensor(background), output_image)
easyCache = easyLoader()
sampler = easySampler()
def check_link_to_clip(node_id, clip_id, visited=None, node=None):
"""Check if a given node links directly or indirectly to a loader node."""
if visited is None:
visited = set()
if node_id in visited:
return False
visited.add(node_id)
if "pipe" in node["inputs"]:
link_ids = node["inputs"]["pipe"]
for id in link_ids:
if id != 0 and id == str(clip_id):
return True
return False
def find_nearest_steps(clip_id, prompt):
"""Find the nearest KSampler or preSampling node that references the given id."""
for id in prompt:
node = prompt[id]
if "Sampler" in node["class_type"] or "sampler" in node["class_type"] or "Sampling" in node["class_type"]:
# Check if this KSampler node directly or indirectly references the given CLIPTextEncode node
if check_link_to_clip(id, clip_id, None, node):
steps = node["inputs"]["steps"] if "steps" in node["inputs"] else 1
return steps
return 1
def find_wildcards_seed(text, prompt):
if "__" in text:
for i in prompt:
if "wildcards" in prompt[i]['class_type'] and text == prompt[i]['inputs']['text']:
return prompt[i]['inputs']['seed_num'] if "seed_num" in prompt[i]['inputs'] else None
else:
return None
class easySave:
def __init__(self, my_unique_id=0, prompt=None, extra_pnginfo=None, number_padding=5, overwrite_existing=False,
output_dir=folder_paths.get_temp_directory()):
self.number_padding = int(number_padding) if number_padding not in [None, "None", 0] else None
self.overwrite_existing = overwrite_existing
self.my_unique_id = my_unique_id
self.prompt = prompt
self.extra_pnginfo = extra_pnginfo
self.type = 'temp'
self.output_dir = output_dir
if self.output_dir != folder_paths.get_temp_directory():
self.output_dir = self.folder_parser(self.output_dir, self.prompt, self.my_unique_id)
if not os.path.exists(self.output_dir):
self._create_directory(self.output_dir)
@staticmethod
def _create_directory(folder: str):
"""Try to create the directory and log the status."""
log_node_warn("", f"Folder {folder} does not exist. Attempting to create...")
if not os.path.exists(folder):
try:
os.makedirs(folder)
log_node_success("",f"{folder} Created Successfully")
except OSError:
|
# 加载器
class easyLoader:
def __init__(self):
self.loaded_objects = {
"ckpt": defaultdict(tuple), # {ckpt_name: (model, ...)}
"clip": defaultdict(tuple),
"clip_vision": defaultdict(tuple),
"bvae": defaultdict(tuple),
"vae": defaultdict(object),
"lora": defaultdict(dict), # {lora_name: {UID: (model_lora, clip_lora)}}
}
self.memory_threshold = self.determine_memory_threshold(0.7)
def clean_values(self, values: str):
original_values = values.split("; ")
cleaned_values = []
for value in original_values:
cleaned_value = value.strip(';').strip()
if cleaned_value == "":
continue
try:
cleaned_value = int(cleaned_value)
except ValueError:
try:
cleaned_value = float(cleaned_value)
except ValueError:
pass
cleaned_values.append(cleaned_value)
return cleaned_values
def clear_unused_objects(self, desired_names: set, object_type: str):
keys = set(self.loaded_objects[object_type].keys())
for key in keys - desired_names:
del self.loaded_objects[object_type][key]
def get_input_value(self, entry, key):
val = entry["inputs"][key]
return val if isinstance(val, str) else val[0]
def process_pipe_loader(self, entry,
desired_ckpt_names, desired_vae_names,
desired_lora_names, desired_lora_settings, num_loras=3, suffix=""):
for idx in range(1, num_loras + 1):
lora_name_key = f"{suffix}lora{idx}_name"
desired_lora_names.add(self.get_input_value(entry, lora_name_key))
setting = f'{self.get_input_value(entry, lora_name_key)};{entry["inputs"][f"{suffix}lora{idx}_model_strength"]};{entry["inputs"][f"{suffix}lora{idx}_clip_strength"]}'
desired_lora_settings.add(setting)
desired_ckpt_names.add(self.get_input_value(entry, f"{suffix}ckpt_name"))
desired_vae_names.add(self.get_input_value(entry, f"{suffix}vae_name"))
def update_loaded_objects(self, prompt):
desired_ckpt_names = set()
desired_vae_names = set()
desired_lora_names = set()
desired_lora_settings = set()
for entry in prompt.values():
class_type = entry["class_type"]
if class_type == "easy a1111Loader" or class_type == "easy comfyLoader":
lora_name = self.get_input_value(entry, "lora_name")
desired_lora_names.add(lora_name)
setting = f'{lora_name};{entry["inputs"]["lora_model_strength"]};{entry["inputs"]["lora_clip_strength"]}'
desired_lora_settings.add(setting)
desired_ckpt_names.add(self.get_input_value(entry, "ckpt_name"))
desired_vae_names.add(self.get_input_value(entry, "vae_name"))
elif class_type == "easy zero123Loader" or class_type == 'easy svdLoader':
desired_ckpt_names.add(self.get_input_value(entry, "ckpt_name"))
desired_vae_names.add(self.get_input_value(entry, "vae_name"))
elif class_type == "easy XYInputs: ModelMergeBlocks":
desired_ckpt_names.add(self.get_input_value(entry, "ckpt_name_1"))
desired_ckpt_names.add(self.get_input_value(entry, "ckpt_name_2"))
vae_use = self.get_input_value(entry, "vae_use")
if vae_use != 'Use Model 1' and vae_use != 'Use Model 2':
desired_vae_names.add(vae_use)
object_types = ["ckpt", "clip", "bvae", "vae", "lora"]
for object_type in object_types:
desired_names = desired_ckpt_names if object_type in ["ckpt", "clip", "bvae"] else desired_vae_names if object_type == "vae" else desired_lora_names
self.clear_unused_objects(desired_names, object_type)
def add_to_cache(self, obj_type, key, value):
"""
Add an item to the cache with the current timestamp.
"""
timestamped_value = (value, time.time())
self.loaded_objects[obj_type][key] = timestamped_value
def determine_memory_threshold(self, percentage=0.8):
"""
Determines the memory threshold as a percentage of the total available memory.
Args:
- percentage (float): The fraction of total memory to use as the threshold.
Should be a value between 0 and 1. Default is 0.8 (80%).
Returns:
- memory_threshold (int): Memory threshold in bytes.
"""
total_memory = psutil.virtual_memory().total
memory_threshold = total_memory * percentage
return memory_threshold
def get_memory_usage(self):
"""
Returns the memory usage of the current process in bytes.
"""
process = psutil.Process(os.getpid())
return process.memory_info().rss
def eviction_based_on_memory(self):
"""
Evicts objects from cache based on memory usage and priority.
"""
current_memory = self.get_memory_usage()
if current_memory < self.memory_threshold:
return
eviction_order = ["vae", "lora", "bvae", "clip", "ckpt"]
for obj_type in eviction_order:
if current_memory < self.memory_threshold:
break
# Sort items based on age (using the timestamp)
items = list(self.loaded_objects[obj_type].items())
items.sort(key=lambda x: x[1][1]) # Sorting by timestamp
for item in items:
if current_memory < self.memory_threshold:
break
del self.loaded_objects[obj_type][item[0]]
current_memory = self.get_memory_usage()
def load_checkpoint(self, ckpt_name, config_name=None, load_vision=False):
cache_name = ckpt_name
if config_name not in [None, "Default"]:
cache_name = ckpt_name + "_" + config_name
if cache_name in self.loaded_objects["ckpt"]:
cache_out = self.loaded_objects["clip_vision"][cache_name][0] if load_vision else self.loaded_objects["clip"][cache_name][0]
return self.loaded_objects["ckpt"][cache_name][0], cache_out, self.loaded_objects["bvae"][cache_name][0]
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
output_clip = False if load_vision else True
output_clipvision = True if load_vision else False
if config_name not in [None, "Default"]:
config_path = folder_paths.get_full_path("configs", config_name)
loaded_ckpt = comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=output_clip, output_clipvision=output_clipvision,
embedding_directory=folder_paths.get_folder_paths("embeddings"))
else:
loaded_ckpt = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=output_clip, output_clipvision=output_clipvision, embedding_directory=folder_paths.get_folder_paths("embeddings"))
self.add_to_cache("ckpt", cache_name, loaded_ckpt[0])
self.add_to_cache("bvae", cache_name, loaded_ckpt[2])
if load_vision:
out = loaded_ckpt[3]
self.add_to_cache("clip_vision", cache_name, out)
else:
out = loaded_ckpt[1]
self.add_to_cache("clip", cache_name, loaded_ckpt[1])
self.eviction_based_on_memory()
return loaded_ckpt[0], out, loaded_ckpt[2]
def load_vae(self, vae_name):
if vae_name in self.loaded_objects["vae"]:
return self.loaded_objects["vae"][vae_name][0]
vae_path = folder_paths.get_full_path("vae", vae_name)
sd = comfy.utils.load_torch_file(vae_path)
loaded_vae = comfy.sd.VAE(sd=sd)
self.add_to_cache("vae", vae_name, loaded_vae)
self.eviction_based_on_memory()
return loaded_vae
def load_lora(self, lora_name, model, clip, strength_model, strength_clip):
model_hash = str(model)[44:-1]
clip_hash = str(clip)[25:-1]
unique_id = f'{model_hash};{clip_hash};{lora_name};{strength_model};{strength_clip}'
if unique_id in self.loaded_objects["lora"] and unique_id in self.loaded_objects["lora"][lora_name]:
return self.loaded_objects["lora"][unique_id][0]
lora_path = folder_paths.get_full_path("loras", lora_name)
lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
self.add_to_cache("lora", unique_id, (model_lora, clip_lora))
self.eviction_based_on_memory()
return model_lora, clip_lora
# 采样器
class easySampler:
def __init__(self):
self.last_helds: dict[str, list] = {
"results": [],
"pipe_line": [],
}
@staticmethod
def tensor2pil(image: torch.Tensor) -> Image.Image:
"""Convert a torch tensor to a PIL image."""
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
@staticmethod
def pil2tensor(image: Image.Image) -> torch.Tensor:
"""Convert a PIL image to a torch tensor."""
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
@staticmethod
def enforce_mul_of_64(d):
d = int(d)
if d <= 7:
d = 8
leftover = d % 8 # 8 is the number of pixels per byte
if leftover != 0: # if the number of pixels is not a multiple of 8
if (leftover < 4): # if the number of pixels is less than 4
d -= leftover # remove the leftover pixels
else: # if the number of pixels is more than 4
d += 8 - leftover # add the leftover pixels
return int(d)
@staticmethod
def safe_split(to_split: str, delimiter: str) -> List[str]:
"""Split the input string and return a list of non-empty parts."""
parts = to_split.split(delimiter)
parts = [part for part in parts if part not in ('', ' ', ' ')]
while len(parts) < 2:
parts.append('None')
return parts
def common_ksampler(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0,
disable_noise=False, start_step=None, last_step=None, force_full_denoise=False,
preview_latent=True, disable_pbar=False):
device = comfy.model_management.get_torch_device()
latent_image = latent["samples"]
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
else:
batch_inds = latent["batch_index"] if "batch_index" in latent else None
noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
preview_format = "JPEG"
if preview_format not in ["JPEG", "PNG"]:
preview_format = "JPEG"
previewer = False
if preview_latent:
previewer = latent_preview.get_previewer(device, model.model.latent_format)
pbar = comfy.utils.ProgressBar(steps)
def callback(step, x0, x, total_steps):
preview_bytes = None
if previewer:
preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
pbar.update_absolute(step + 1, total_steps, preview_bytes)
samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image,
denoise=denoise, disable_noise=disable_noise, start_step=start_step,
last_step=last_step,
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback,
disable_pbar=disable_pbar, seed=seed)
out = latent.copy()
out["samples"] = samples
return out
def custom_ksampler(self, model, seed, steps, cfg, _sampler, sigmas, positive, negative, latent,
disable_noise=False, preview_latent=True, disable_pbar=False):
device = comfy.model_management.get_torch_device()
latent_image = latent["samples"]
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
else:
batch_inds = latent["batch_index"] if "batch_index" in latent else None
noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
preview_format = "JPEG"
if preview_format not in ["JPEG", "PNG"]:
preview_format = "JPEG"
previewer = False
if preview_latent:
previewer = latent_preview.get_previewer(device, model.model.latent_format)
pbar = comfy.utils.ProgressBar(steps)
def callback(step, x0, x, total_steps):
preview_bytes = None
if previewer:
preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
pbar.update_absolute(step + 1, total_steps, preview_bytes)
samples = comfy.sample.sample_custom(model, noise, cfg, _sampler, sigmas, positive, negative, latent_image,
noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar,
seed=seed)
out = latent.copy()
out["samples"] = samples
return out
def get_value_by_id(self, key: str, my_unique_id: Any) -> Optional[Any]:
"""Retrieve value by its associated ID."""
try:
for value, id_ in self.last_helds[key]:
if id_ == my_unique_id:
return value
except KeyError:
return None
def update_value_by_id(self, key: str, my_unique_id: Any, new_value: Any) -> Union[bool, None]:
"""Update the value associated with a given ID. Return True if updated, False if appended, None if key doesn't exist."""
try:
for i, (value, id_) in enumerate(self.last_helds[key]):
if id_ == my_unique_id:
self.last_helds[key][i] = (new_value, id_)
return True
self.last_helds[key].append((new_value, my_unique_id))
return False
except KeyError:
return False
def upscale(self, samples, upscale_method, scale_by, crop):
s = samples.copy()
width = self.enforce_mul_of_64(round(samples["samples"].shape[3] * scale_by))
height = self.enforce_mul_of_64(round(samples["samples"].shape[2] * scale_by))
if (width > MAX_RESOLUTION):
width = MAX_RESOLUTION
if (height > MAX_RESOLUTION):
height = MAX_RESOLUTION
s["samples"] = comfy.utils.common_upscale(samples["samples"], width, height, upscale_method, crop)
return (s,)
def handle_upscale(self, samples: dict, upscale_method: str, factor: float, crop: bool) -> dict:
"""Upscale the samples if the upscale_method is not set to 'None'."""
if upscale_method != "None":
samples = self.upscale(samples, upscale_method, factor, crop)[0]
return samples
def init_state(self, my_unique_id: Any, key: str, default: Any) -> Any:
"""Initialize the state by either fetching the stored value or setting a default."""
value = self.get_value_by_id(key, my_unique_id)
if value is not None:
return value
return default
def get_output(self, pipe: dict,) -> Tuple:
"""Return a tuple of various elements fetched from the input pipe dictionary."""
return (
pipe,
pipe.get("images"),
pipe.get("model"),
pipe.get("positive"),
pipe.get("negative"),
pipe.get("samples"),
pipe.get("vae"),
pipe.get("clip"),
pipe.get("seed"),
)
def get_output_sdxl(self, sdxl_pipe: dict) -> Tuple:
"""Return a tuple of various elements fetched from the input sdxl_pipe dictionary."""
return (
sdxl_pipe,
sdxl_pipe.get("model"),
sdxl_pipe.get("positive"),
sdxl_pipe.get("negative"),
sdxl_pipe.get("vae"),
sdxl_pipe.get("refiner_model"),
sdxl_pipe.get("refiner_positive"),
sdxl_pipe.get("refiner_negative"),
sdxl_pipe.get("refiner_vae"),
sdxl_pipe.get("samples"),
sdxl_pipe.get("clip"),
sdxl_pipe.get("images"),
sdxl_pipe.get("seed")
)
# XY图表
class easyXYPlot:
def __init__(self, xyPlotData, save_prefix, image_output, prompt, extra_pnginfo, my_unique_id):
self.x_node_type, self.x_type = easySampler.safe_split(xyPlotData.get("x_axis"), ': ')
self.y_node_type, self.y_type = easySampler.safe_split(xyPlotData.get("y_axis"), ': ')
self.x_values = xyPlotData.get("x_vals") if self.x_type != "None" else []
self.y_values = xyPlotData.get("y_vals") if self.y_type != "None" else []
self.grid_spacing = xyPlotData.get("grid_spacing")
self.latent_id = 0
self.output_individuals = xyPlotData.get("output_individuals")
self.x_label, self.y_label = [], []
self.max_width, self.max_height = 0, 0
self.latents_plot = []
self.image_list = []
self.num_cols = len(self.x_values) if len(self.x_values) > 0 else 1
self.num_rows = len(self.y_values) if len(self.y_values) > 0 else 1
self.total = self.num_cols * self.num_rows
self.num = 0
self.save_prefix = save_prefix
self.image_output = image_output
self.prompt = prompt
self.extra_pnginfo = extra_pnginfo
self.my_unique_id = my_unique_id
# Helper Functions
@staticmethod
def define_variable(plot_image_vars, value_type, value, index):
plot_image_vars[value_type] = value
if value_type in ["seed", "Seeds++ Batch"]:
value_label = f"{value}"
else:
value_label = f"{value_type}: {value}"
if "ControlNet" in value_type:
if "," in value:
line = value.split(',')
value_label = f"{value_type}: {line[2]}"
if value_type in ["ModelMergeBlocks"]:
if ":" in value:
line = value.split(':')
value_label = f"{line[0]}"
elif len(value) > 16:
value_label = f"ModelMergeBlocks {index + 1}"
else:
value_label = f"MMB: {value}"
if value_type in ["Positive Prompt S/R"]:
value_label = f"pos prompt {index + 1}" if index>0 else f"pos prompt"
if value_type in ["Negative Prompt S/R"]:
value_label = f"neg prompt {index + 1}" if index>0 else f"neg prompt"
if value_type in ["steps", "cfg", "denoise", "clip_skip",
"lora_model_strength", "lora_clip_strength"]:
value_label = f"{value_type}: {value}"
if value_type == "positive":
value_label = f"pos prompt {index + 1}"
elif value_type == "negative":
value_label = f"neg prompt {index + 1}"
return plot_image_vars, value_label
@staticmethod
def get_font(font_size):
return ImageFont.truetype(str(Path(os.path.join(Path(__file__).parent.parent, 'resources/OpenSans-Medium.ttf'))), font_size)
@staticmethod
def update_label(label, value, num_items):
if len(label) < num_items:
return [*label, value]
return label
@staticmethod
def rearrange_tensors(latent, num_cols, num_rows):
new_latent = []
for i in range(num_rows):
for j in range(num_cols):
index = j * num_rows + i
new_latent.append(latent[index])
return new_latent
def calculate_background_dimensions(self):
border_size = int((self.max_width // 8) * 1.5) if self.y_type != "None" or self.x_type != "None" else 0
bg_width = self.num_cols * (self.max_width + self.grid_spacing) - self.grid_spacing + border_size * (
self.y_type != "None")
bg_height = self.num_rows * (self.max_height + self.grid_spacing) - self.grid_spacing + border_size * (
self.x_type != "None")
x_offset_initial = border_size if self.y_type != "None" else 0
y_offset = border_size if self.x_type != "None" else 0
return bg_width, bg_height, x_offset_initial, y_offset
def adjust_font_size(self, text, initial_font_size, label_width):
font = self.get_font(initial_font_size)
text_width, _ = font.getsize(text)
scaling_factor = 0.9
if text_width > (label_width * scaling_factor):
return int(initial_font_size * (label_width / text_width) * scaling_factor)
else:
return initial_font_size
def create_label(self, img, text, initial_font_size, is_x_label=True, max_font_size=70, min_font_size=10):
label_width = img.width if is_x_label else img.height
# Adjust font size
font_size = self.adjust_font_size(text, initial_font_size, label_width)
font_size = min(max_font_size, font_size) # Ensure font isn't too large
font_size = max(min_font_size, font_size) # Ensure font isn't too small
label_height = int(font_size * 1.5) if is_x_label else font_size
label_bg = Image.new('RGBA', (label_width, label_height), color=(255, 255, 255, 0))
d = ImageDraw.Draw(label_bg)
font = self.get_font(font_size)
# Check if text will fit, if not insert ellipsis and reduce text
if d.textsize(text, font=font)[0] > label_width:
while d.textsize(text + '...', font=font)[0] > label_width and len(text) > 0:
text = text[:-1]
text = text + '...'
# Compute text width and height for multi-line text
text_lines = text.split('\n')
text_widths, text_heights = zip(*[d.textsize(line, font=font) for line in text_lines])
max_text_width = max(text_widths)
total_text_height = sum(text_heights)
# Compute position for each line of text
lines_positions = []
current_y = 0
for line, line_width, line_height in zip(text_lines, text_widths, text_heights):
text_x = (label_width - line_width) // 2
text_y = current_y + (label_height - total_text_height) // 2
current_y += line_height
lines_positions.append((line, (text_x, text_y)))
# Draw each line of text
for line, (text_x, text_y) in lines_positions:
d.text((text_x, text_y), line, fill='black', font=font)
return label_bg
def sample_plot_image(self, plot_image_vars, samples, preview_latent, latents_plot, image_list, disable_noise,
start_step, last_step, force_full_denoise, x_value=None, y_value=None):
model, clip, vae, positive, negative, seed, steps, cfg = None, None, None, None, None, None, None, None
sampler_name, scheduler, denoise = None, None, None
# 高级用法
if plot_image_vars["x_node_type"] == "advanced" or plot_image_vars["y_node_type"] == "advanced":
if self.x_type == "Seeds++ Batch" or self.y_type == "Seeds++ Batch":
seed = int(x_value) if self.x_type == "Seeds++ Batch" else int(y_value)
if self.x_type == "Steps" or self.y_type == "Steps":
steps = int(x_value) if self.x_type == "Steps" else int(y_value)
if self.x_type == "StartStep" or self.y_type == "StartStep":
start_step = int(x_value) if self.x_type == "StartStep" else int(y_value)
if self.x_type == "EndStep" or self.y_type == "EndStep":
last_step = int(x_value) if self.x_type == "EndStep" else int(y_value)
if self.x_type == "CFG Scale" or self.y_type == "CFG Scale":
cfg = float(x_value) if self.x_type == "CFG Scale" else float(y_value)
if self.x_type == "Sampler" or self.y_type == "Sampler" or self.y_type == "Sampler & Scheduler":
sampler_name = float(x_value) if self.x_type == "Sampler" or self.x_type == "Sampler & Scheduler" else float(y_value)
if self.x_type == "Scheduler" or self.y_type == "Scheduler" or self.y_type == "Sampler & Scheduler":
scheduler = float(x_value) if self.x_type == "Scheduler" or self.x_type == "Sampler & Scheduler" else float(y_value)
if self.x_type == "Denoise" or self.y_type == "Denoise":
denoise = float(x_value) if self.x_type == "Denoise" else float(y_value)
# 模型叠加
if self.x_type == "ModelMergeBlocks" or self.y_type == "ModelMergeBlocks":
ckpt_name_1, ckpt_name_2 = plot_image_vars['models']
model1, clip1, vae1 = easyCache.load_checkpoint(ckpt_name_1)
model2, clip2, vae2 = easyCache.load_checkpoint(ckpt_name_2)
xy_values = x_value if self.x_type == "ModelMergeBlocks" else y_value
if ":" in xy_values:
xy_line = xy_values.split(':')
xy_values = xy_line[1]
xy_arrs = xy_values.split(',')
# ModelMergeBlocks
if len(xy_arrs) == 3:
input, middle, out = xy_arrs
kwargs = {
"input": input,
"middle": middle,
"out": out
}
elif len(xy_arrs) == 30:
kwargs = {}
kwargs["time_embed."] = xy_arrs[0]
kwargs["label_emb."] = xy_arrs[1]
for i in range(12):
kwargs["input_blocks.{}.".format(i)] = xy_arrs[2+i]
for i in range(3):
kwargs["middle_block.{}.".format(i)] = xy_arrs[14+i]
for i in range(12):
kwargs["output_blocks.{}.".format(i)] = xy_arrs[17+i]
kwargs["out."] = xy_arrs[29]
else:
raise Exception("ModelMergeBlocks weight length error")
default_ratio = next(iter(kwargs.values()))
m = model1.clone()
kp = model2.get_key_patches("diffusion_model.")
for k in kp:
ratio = float(default_ratio)
k_unet = k[len("diffusion_model."):]
last_arg_size = 0
for arg in kwargs:
if k_unet.startswith(arg) and last_arg_size < len(arg):
ratio = float(kwargs[arg])
last_arg_size = len(arg)
m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
vae_use = plot_image_vars['vae_use']
clip = clip2 if vae_use == 'Use Model 2' else clip1
if vae_use == 'Use Model 2':
vae = vae2
elif vae_use == 'Use Model 1':
vae = vae1
else:
(vae,) = VAELoader().load_vae(vae_use)
model = m
# 如果存在lora_stack叠加lora
optional_lora_stack = plot_image_vars['lora_stack']
if optional_lora_stack is not None and optional_lora_stack != []:
for lora in optional_lora_stack:
lora_name = lora["lora_name"]
model = model if model is not None else lora["model"]
clip = clip if clip is not None else lora["clip"]
lora_model_strength = lora["lora_model_strength"]
lora_clip_strength = lora["lora_clip_strength"]
if "lbw" in lora:
lbw = lora["lbw"]
lbw_a = lora["lbw_a"]
lbw_b = lora["lbw_b"]
cls = ALL_NODE_CLASS_MAPPINGS['LoraLoaderBlockWeight //Inspire']
model, clip, _ = cls().doit(model, clip, lora_name, lora_model_strength, lora_clip_strength, False, 0,
lbw_a, lbw_b, "", lbw)
model, clip = easyCache.load_lora(lora_name, model, clip, lora_model_strength, lora_clip_strength)
# 处理clip
clip = clip.clone()
if plot_image_vars['clip_skip'] != 0:
clip.clip_layer(plot_image_vars['clip_skip'])
# 提示词
if "Positive" in self.x_type or "Positive" in self.y_type:
if self.x_type == 'Positive Prompt S/R' or self.y_type == 'Positive Prompt S/R':
positive = x_value if self.x_type == "Positive Prompt S/R" else y_value
if plot_image_vars['a1111_prompt_style']:
if "smZ CLIPTextEncode" in ALL_NODE_CLASS_MAPPINGS:
cls = ALL_NODE_CLASS_MAPPINGS['smZ CLIPTextEncode']
steps = plot_image_vars['steps']
clip = clip if clip is not None else plot_image_vars["clip"]
positive, = cls().encode(clip, positive, "A1111", True, True, False, False, 6,
1024, 1024, 0, 0, 1024, 1024, '', '', steps)
else:
raise Exception(
f"[ERROR] To use clip text encode same as webui, you need to install 'smzNodes'")
else:
clip = clip if clip is not None else plot_image_vars["clip"]
positive, positive_pooled = advanced_encode(clip, positive,
plot_image_vars['positive_token_normalization'],
plot_image_vars[
'positive_weight_interpretation'],
w_max=1.0,
apply_to_pooled="enable")
positive = [[positive, {"pooled_output": positive_pooled}]]
if "Negative" in self.x_type or "Negative" in self.y_type:
if self.x_type == 'Negative Prompt S/R' or self.y_type == 'Negative Prompt S/R':
negative = x_value if self.x_type == "Negative Prompt S/R" else y_value
if plot_image_vars['a1111_prompt_style']:
if "smZ CLIPTextEncode" in ALL_NODE_CLASS_MAPPINGS:
cls = ALL_NODE_CLASS_MAPPINGS['smZ CLIPTextEncode']
steps = plot_image_vars['steps']
clip = clip if clip is not None else plot_image_vars["clip"]
negative, = cls().encode(clip, negative, "A1111", True, True, False, False, 6,
1024, 1024, 0, 0, 1024, 1024, '', '', steps)
else:
raise Exception(
f"[ERROR] To use clip text encode same as webui, you need to install 'smzNodes'")
else:
clip = clip if clip is not None else plot_image_vars["clip"]
negative, negative_pooled = advanced_encode(clip, negative,
plot_image_vars['negative_token_normalization'],
plot_image_vars[
'negative_weight_interpretation'],
w_max=1.0,
apply_to_pooled="enable")
negative = [[negative, {"pooled_output": negative_pooled}]]
# ControlNet
if "ControlNet" in self.x_type or "ControlNet" in self.y_type:
_pipe = {
"model": model if model is not None else plot_image_vars["model"],
"positive": positive if positive is not None else plot_image_vars["positive_cond"],
"negative": negative if negative is not None else plot_image_vars["negative_cond"],
"vae": vae if vae is not None else plot_image_vars['vae'],
"clip": clip if clip is not None else plot_image_vars['clip'],
"samples": None,
"images": None,
"loader_settings": {}
}
cnet = plot_image_vars["cnet"] if "cnet" in plot_image_vars else None
if cnet:
strength, start_percent, end_percent = x_value.split(',') if "ControlNet" in self.x_type else y_value.split(',')
strength = float(strength)
start_percent = float(start_percent)
end_percent = float(end_percent)
for index, item in enumerate(cnet):
control_net_names = item[0]
image = item[1]
for idx, control_net_name in enumerate(control_net_names):
# print(control_net_name)
_pipe, = controlnetAdvanced().controlnetApply(_pipe, image, control_net_name, None, strength, start_percent,
end_percent)
positive = _pipe['positive']
negative = _pipe['negative']
del _pipe
# 简单用法
if plot_image_vars["x_node_type"] == "loader" or plot_image_vars["y_node_type"] == "loader":
model, clip, vae = easyCache.load_checkpoint(plot_image_vars['ckpt_name'])
if plot_image_vars['lora_name'] != "None":
model, clip = easyCache.load_lora(plot_image_vars['lora_name'], model, clip,
plot_image_vars['lora_model_strength'],
plot_image_vars['lora_clip_strength'])
# Check for custom VAE
if plot_image_vars['vae_name'] not in ["Baked-VAE", "Baked VAE"]:
vae = easyCache.load_vae(plot_image_vars['vae_name'])
# CLIP skip
if not clip:
raise Exception("No CLIP found")
clip = clip.clone()
clip.clip_layer(plot_image_vars['clip_skip'])
if plot_image_vars['a1111_prompt_style']:
if "smZ CLIPTextEncode" in ALL_NODE_CLASS_MAPPINGS:
cls = ALL_NODE_CLASS_MAPPINGS['smZ CLIPTextEncode']
steps = plot_image_vars['steps']
positive, = cls().encode(clip, plot_image_vars['positive'], "A1111", True, True, False, False, 6, 1024, 1024, 0, 0, 1024, 1024, '', '', steps)
negative, = cls().encode(clip, plot_image_vars['negative'], "A1111", True, True, False, False, 6, 1024, 1024, 0, 0, 1024, 1024, '', '', steps)
else:
raise Exception(f"[ERROR] To use clip text encode same as webui, you need to install 'smzNodes'")
else:
positive, positive_pooled = advanced_encode(clip, plot_image_vars['positive'],
plot_image_vars['positive_token_normalization'],
plot_image_vars['positive_weight_interpretation'], w_max=1.0,
apply_to_pooled="enable")
positive = [[positive, {"pooled_output": positive_pooled}]]
negative, negative_pooled = advanced_encode(clip, plot_image_vars['negative'],
plot_image_vars['negative_token_normalization'],
plot_image_vars['negative_weight_interpretation'], w_max=1.0,
apply_to_pooled="enable")
negative = [[negative, {"pooled_output": negative_pooled}]]
model = model if model is not None else plot_image_vars["model"]
clip = clip if clip is not None else plot_image_vars["clip"]
vae = vae if vae is not None else plot_image_vars["vae"]
positive = positive if positive is not None else plot_image_vars["positive_cond"]
negative = negative if negative is not None else plot_image_vars["negative_cond"]
seed = seed if seed is not None else plot_image_vars["seed"]
steps = steps if steps is not None else plot_image_vars["steps"]
cfg = cfg if cfg is not None else plot_image_vars["cfg"]
sampler_name = sampler_name if sampler_name is not None else plot_image_vars["sampler_name"]
scheduler = scheduler if scheduler is not None else plot_image_vars["scheduler"]
denoise = denoise if denoise is not None else plot_image_vars["denoise"]
# Sample
samples = sampler.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, samples,
denoise=denoise, disable_noise=disable_noise, preview_latent=preview_latent,
start_step=start_step, last_step=last_step,
force_full_denoise=force_full_denoise)
# Decode images and store
latent = samples["samples"]
# Add the latent tensor to the tensors list
latents_plot.append(latent)
# Decode the image
image = vae.decode(latent).cpu()
if self.output_individuals in [True, "True"]:
easy_save = easySave(self.my_unique_id, self.prompt, self.extra_pnginfo)
easy_save.images(image, self.save_prefix, self.image_output, group_id=self.num)
# Convert the image from tensor to PIL Image and add it to the list
pil_image = easySampler.tensor2pil(image)
image_list.append(pil_image)
# Update max dimensions
self.max_width = max(self.max_width, pil_image.width)
self.max_height = max(self.max_height, pil_image.height)
# Return the touched variables
return image_list, self.max_width, self.max_height, latents_plot
# Process Functions
def validate_xy_plot(self):
if self.x_type == 'None' and self.y_type == 'None':
log_node_warn(f'easyKsampler[{self.my_unique_id}]','No Valid Plot Types - Reverting to default sampling...')
return False
else:
return True
def get_latent(self, samples):
# Extract the 'samples' tensor from the dictionary
latent_image_tensor = samples["samples"]
# Split the tensor into individual image tensors
image_tensors = torch.split(latent_image_tensor, 1, dim=0)
# Create a list of dictionaries containing the individual image tensors
latent_list = [{'samples': image} for image in image_tensors]
# Set latent only to the first latent of batch
if self.latent_id >= len(latent_list):
log_node_warn(f'easy kSampler[{self.my_unique_id}]',f'The selected latent_id ({self.latent_id}) is out of range.')
log_node_warn(f'easy kSampler[{self.my_unique_id}]', f'Automatically setting the latent_id to the last image in the list (index: {len(latent_list) - 1}).')
self.latent_id = len(latent_list) - 1
return latent_list[self.latent_id]
def get_labels_and_sample(self, plot_image_vars, latent_image, preview_latent, start_step, last_step,
force_full_denoise, disable_noise):
for x_index, x_value in enumerate(self.x_values):
plot_image_vars, x_value_label = self.define_variable(plot_image_vars, self.x_type, x_value,
x_index)
self.x_label = self.update_label(self.x_label, x_value_label, len(self.x_values))
if self.y_type != 'None':
for y_index, y_value in enumerate(self.y_values):
plot_image_vars, y_value_label = self.define_variable(plot_image_vars, self.y_type, y_value,
y_index)
self.y_label = self.update_label(self.y_label, y_value_label, len(self.y_values))
# ttNl(f'{CC.GREY}X: {x_value_label}, Y: {y_value_label}').t(
# f'Plot Values {self.num}/{self.total} ->').p()
self.image_list, self.max_width, self.max_height, self.latents_plot = self.sample_plot_image(
plot_image_vars, latent_image, preview_latent, self.latents_plot, self.image_list,
disable_noise, start_step, last_step, force_full_denoise, x_value, y_value)
self.num += 1
else:
# ttNl(f'{CC.GREY}X: {x_value_label}').t(f'Plot Values {self.num}/{self.total} ->').p()
self.image_list, self.max_width, self.max_height, self.latents_plot = self.sample_plot_image(
plot_image_vars, latent_image, preview_latent, self.latents_plot, self.image_list, disable_noise,
start_step, last_step, force_full_denoise, x_value)
self.num += 1
# Rearrange latent array to match preview image grid
self.latents_plot = self.rearrange_tensors(self.latents_plot, self.num_cols, self.num_rows)
# Concatenate the tensors along the first dimension (dim=0)
self.latents_plot = torch.cat(self.latents_plot, dim=0)
return self.latents_plot
def plot_images_and_labels(self):
# Calculate the background dimensions
bg_width, bg_height, x_offset_initial, y_offset = self.calculate_background_dimensions()
# Create the white background image
background = Image.new('RGBA', (int(bg_width), int(bg_height)), color=(255, 255, 255, 255))
output_image = []
for row_index in range(self.num_rows):
x_offset = x_offset_initial
for col_index in range(self.num_cols):
index = col_index * self.num_rows + row_index
img = self.image_list[index]
output_image.append(sampler.pil2tensor(img))
background.paste(img, (x_offset, y_offset))
# Handle X label
if row_index == 0 and self.x_type != "None":
label_bg = self.create_label(img, self.x_label[col_index], int(48 * img.width / 512))
label_y = (y_offset - label_bg.height) // 2
background.alpha_composite(label_bg, (x_offset, label_y))
# Handle Y label
if col_index == 0 and self.y_type != "None":
label_bg = self.create_label(img, self.y_label[row_index], int(48 * img.height / 512), False)
label_bg = label_bg.rotate(90, expand=True)
label_x = (x_offset - label_bg.width) // 2
label_y = y_offset + (img.height - label_bg.height) // 2
background.alpha_composite(label_bg, (label_x, label_y))
x_offset += img.width + self.grid_spacing
y_offset += img.height + self.grid_spacing
return (sampler.pil2tensor(background), output_image)
easyCache = easyLoader()
sampler = easySampler()
def check_link_to_clip(node_id, clip_id, visited=None, node=None):
"""Check if a given node links directly or indirectly to a loader node."""
if visited is None:
visited = set()
if node_id in visited:
return False
visited.add(node_id)
if "pipe" in node["inputs"]:
link_ids = node["inputs"]["pipe"]
for id in link_ids:
if id != 0 and id == str(clip_id):
return True
return False
def find_nearest_steps(clip_id, prompt):
"""Find the nearest KSampler or preSampling node that references the given id."""
for id in prompt:
node = prompt[id]
if "Sampler" in node["class_type"] or "sampler" in node["class_type"] or "Sampling" in node["class_type"]:
# Check if this KSampler node directly or indirectly references the given CLIPTextEncode node
if check_link_to_clip(id, clip_id, None, node):
steps = node["inputs"]["steps"] if "steps" in node["inputs"] else 1
return steps
return 1
def find_wildcards_seed(text, prompt):
if "__" in text:
for i in prompt:
if "wildcards" in prompt[i]['class_type'] and text == prompt[i]['inputs']['text']:
return prompt[i]['inputs']['seed_num'] if "seed_num" in prompt[i]['inputs'] else None
else:
return None
class easySave:
def __init__(self, my_unique_id=0, prompt=None, extra_pnginfo=None, number_padding=5, overwrite_existing=False,
output_dir=folder_paths.get_temp_directory()):
self.number_padding = int(number_padding) if number_padding not in [None, "None", 0] else None
self.overwrite_existing = overwrite_existing
self.my_unique_id = my_unique_id
self.prompt = prompt
self.extra_pnginfo = extra_pnginfo
self.type = 'temp'
self.output_dir = output_dir
if self.output_dir != folder_paths.get_temp_directory():
self.output_dir = self.folder_parser(self.output_dir, self.prompt, self.my_unique_id)
if not os.path.exists(self.output_dir):
self._create_directory(self.output_dir)
@staticmethod
def _create_directory(folder: str):
"""Try to create the directory and log the status."""
log_node_warn("", f"Folder {folder} does not exist. Attempting to create...")
if not os.path.exists(folder):
try:
os.makedirs(folder)
log_node_success("",f"{folder} Created Successfully")
except OSError: | log_node_error(f"Failed to create folder {folder}") | 4 | 2023-12-10 07:02:36+00:00 | 16k |
AIFSH/NativeDancer | nativedancer/third_part/detectron2/data/build.py | [
{
"identifier": "configurable",
"path": "nativedancer/third_part/detectron2/config/config.py",
"snippet": "def configurable(init_func=None, *, from_config=None):\n \"\"\"\n Decorate a function or a class's __init__ method so that it can be called\n with a :class:`CfgNode` object using a :func:`... | import itertools
import logging
import numpy as np
import operator
import pickle
import torch
import torch.utils.data as torchdata
from collections import OrderedDict, defaultdict
from typing import Any, Callable, Dict, List, Optional, Union
from tabulate import tabulate
from termcolor import colored
from ..config import configurable
from ..structures import BoxMode
from ..utils.comm import get_world_size
from ..utils.env import seed_all_rng
from ..utils.file_io import PathManager
from ..utils.logger import _log_api_usage, log_first_n
from .catalog import DatasetCatalog, MetadataCatalog
from .common import AspectRatioGroupedDataset, DatasetFromList, MapDataset, ToIterableDataset
from .dataset_mapper import DatasetMapper
from .detection_utils import check_metadata_consistency
from .samplers import (
InferenceSampler,
RandomSubsetTrainingSampler,
RepeatFactorTrainingSampler,
TrainingSampler,
) | 12,525 | table = tabulate(
data,
headers=["category", "#instances"] * (N_COLS // 2),
tablefmt="pipe",
numalign="left",
stralign="center",
)
log_first_n(
logging.INFO,
"Distribution of instances among all {} categories:\n".format(num_classes)
+ colored(table, "cyan"),
key="message",
)
def get_detection_dataset_dicts(
names,
filter_empty=True,
min_keypoints=0,
proposal_files=None,
check_consistency=True,
):
"""
Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation.
Args:
names (str or list[str]): a dataset name or a list of dataset names
filter_empty (bool): whether to filter out images without instance annotations
min_keypoints (int): filter out images with fewer keypoints than
`min_keypoints`. Set to 0 to do nothing.
proposal_files (list[str]): if given, a list of object proposal files
that match each dataset in `names`.
check_consistency (bool): whether to check if datasets have consistent metadata.
Returns:
list[dict]: a list of dicts following the standard dataset dict format.
"""
if isinstance(names, str):
names = [names]
assert len(names), names
available_datasets = DatasetCatalog.keys()
names_set = set(names)
if not names_set.issubset(available_datasets):
logger = logging.getLogger(__name__)
logger.warning(
"The following dataset names are not registered in the DatasetCatalog: "
f"{names_set - available_datasets}. "
f"Available datasets are {available_datasets}"
)
dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names]
if isinstance(dataset_dicts[0], torchdata.Dataset):
if len(dataset_dicts) > 1:
# ConcatDataset does not work for iterable style dataset.
# We could support concat for iterable as well, but it's often
# not a good idea to concat iterables anyway.
return torchdata.ConcatDataset(dataset_dicts)
return dataset_dicts[0]
for dataset_name, dicts in zip(names, dataset_dicts):
assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
if proposal_files is not None:
assert len(names) == len(proposal_files)
# load precomputed proposals from proposal files
dataset_dicts = [
load_proposals_into_dataset(dataset_i_dicts, proposal_file)
for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files)
]
dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts))
has_instances = "annotations" in dataset_dicts[0]
if filter_empty and has_instances:
dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts)
if min_keypoints > 0 and has_instances:
dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints)
if check_consistency and has_instances:
try:
class_names = MetadataCatalog.get(names[0]).thing_classes
check_metadata_consistency("thing_classes", names)
print_instances_class_histogram(dataset_dicts, class_names)
except AttributeError: # class names are not available for this dataset
pass
assert len(dataset_dicts), "No valid data found in {}.".format(",".join(names))
return dataset_dicts
def build_batch_data_loader(
dataset,
sampler,
total_batch_size,
*,
aspect_ratio_grouping=False,
num_workers=0,
collate_fn=None,
drop_last: bool = True,
**kwargs,
):
"""
Build a batched dataloader. The main differences from `torch.utils.data.DataLoader` are:
1. support aspect ratio grouping options
2. use no "batch collation", because this is common for detection training
Args:
dataset (torch.utils.data.Dataset): a pytorch map-style or iterable dataset.
sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces indices.
Must be provided iff. ``dataset`` is a map-style dataset.
total_batch_size, aspect_ratio_grouping, num_workers, collate_fn: see
:func:`build_detection_train_loader`.
drop_last (bool): if ``True``, the dataloader will drop incomplete batches.
Returns:
iterable[list]. Length of each list is the batch size of the current
GPU. Each element in the list comes from the dataset.
"""
| # Copyright (c) Facebook, Inc. and its affiliates.
"""
This file contains the default logic to build a dataloader for training or testing.
"""
__all__ = [
"build_batch_data_loader",
"build_detection_train_loader",
"build_detection_test_loader",
"get_detection_dataset_dicts",
"load_proposals_into_dataset",
"print_instances_class_histogram",
]
def filter_images_with_only_crowd_annotations(dataset_dicts):
"""
Filter out images with none annotations or only crowd annotations
(i.e., images without non-crowd annotations).
A common training-time preprocessing on COCO dataset.
Args:
dataset_dicts (list[dict]): annotations in Detectron2 Dataset format.
Returns:
list[dict]: the same format, but filtered.
"""
num_before = len(dataset_dicts)
def valid(anns):
for ann in anns:
if ann.get("iscrowd", 0) == 0:
return True
return False
dataset_dicts = [x for x in dataset_dicts if valid(x["annotations"])]
num_after = len(dataset_dicts)
logger = logging.getLogger(__name__)
logger.info(
"Removed {} images with no usable annotations. {} images left.".format(
num_before - num_after, num_after
)
)
return dataset_dicts
def filter_images_with_few_keypoints(dataset_dicts, min_keypoints_per_image):
"""
Filter out images with too few number of keypoints.
Args:
dataset_dicts (list[dict]): annotations in Detectron2 Dataset format.
Returns:
list[dict]: the same format as dataset_dicts, but filtered.
"""
num_before = len(dataset_dicts)
def visible_keypoints_in_image(dic):
# Each keypoints field has the format [x1, y1, v1, ...], where v is visibility
annotations = dic["annotations"]
return sum(
(np.array(ann["keypoints"][2::3]) > 0).sum()
for ann in annotations
if "keypoints" in ann
)
dataset_dicts = [
x for x in dataset_dicts if visible_keypoints_in_image(x) >= min_keypoints_per_image
]
num_after = len(dataset_dicts)
logger = logging.getLogger(__name__)
logger.info(
"Removed {} images with fewer than {} keypoints.".format(
num_before - num_after, min_keypoints_per_image
)
)
return dataset_dicts
def load_proposals_into_dataset(dataset_dicts, proposal_file):
"""
Load precomputed object proposals into the dataset.
The proposal file should be a pickled dict with the following keys:
- "ids": list[int] or list[str], the image ids
- "boxes": list[np.ndarray], each is an Nx4 array of boxes corresponding to the image id
- "objectness_logits": list[np.ndarray], each is an N sized array of objectness scores
corresponding to the boxes.
- "bbox_mode": the BoxMode of the boxes array. Defaults to ``BoxMode.XYXY_ABS``.
Args:
dataset_dicts (list[dict]): annotations in Detectron2 Dataset format.
proposal_file (str): file path of pre-computed proposals, in pkl format.
Returns:
list[dict]: the same format as dataset_dicts, but added proposal field.
"""
logger = logging.getLogger(__name__)
logger.info("Loading proposals from: {}".format(proposal_file))
with PathManager.open(proposal_file, "rb") as f:
proposals = pickle.load(f, encoding="latin1")
# Rename the key names in D1 proposal files
rename_keys = {"indexes": "ids", "scores": "objectness_logits"}
for key in rename_keys:
if key in proposals:
proposals[rename_keys[key]] = proposals.pop(key)
# Fetch the indexes of all proposals that are in the dataset
# Convert image_id to str since they could be int.
img_ids = set({str(record["image_id"]) for record in dataset_dicts})
id_to_index = {str(id): i for i, id in enumerate(proposals["ids"]) if str(id) in img_ids}
# Assuming default bbox_mode of precomputed proposals are 'XYXY_ABS'
bbox_mode = BoxMode(proposals["bbox_mode"]) if "bbox_mode" in proposals else BoxMode.XYXY_ABS
for record in dataset_dicts:
# Get the index of the proposal
i = id_to_index[str(record["image_id"])]
boxes = proposals["boxes"][i]
objectness_logits = proposals["objectness_logits"][i]
# Sort the proposals in descending order of the scores
inds = objectness_logits.argsort()[::-1]
record["proposal_boxes"] = boxes[inds]
record["proposal_objectness_logits"] = objectness_logits[inds]
record["proposal_bbox_mode"] = bbox_mode
return dataset_dicts
def print_instances_class_histogram(dataset_dicts, class_names):
"""
Args:
dataset_dicts (list[dict]): list of dataset dicts.
class_names (list[str]): list of class names (zero-indexed).
"""
num_classes = len(class_names)
hist_bins = np.arange(num_classes + 1)
histogram = np.zeros((num_classes,), dtype=int)
for entry in dataset_dicts:
annos = entry["annotations"]
classes = np.asarray(
[x["category_id"] for x in annos if not x.get("iscrowd", 0)], dtype=int
)
if len(classes):
assert classes.min() >= 0, f"Got an invalid category_id={classes.min()}"
assert (
classes.max() < num_classes
), f"Got an invalid category_id={classes.max()} for a dataset of {num_classes} classes"
histogram += np.histogram(classes, bins=hist_bins)[0]
N_COLS = min(6, len(class_names) * 2)
def short_name(x):
# make long class names shorter. useful for lvis
if len(x) > 13:
return x[:11] + ".."
return x
data = list(
itertools.chain(*[[short_name(class_names[i]), int(v)] for i, v in enumerate(histogram)])
)
total_num_instances = sum(data[1::2])
data.extend([None] * (N_COLS - (len(data) % N_COLS)))
if num_classes > 1:
data.extend(["total", total_num_instances])
data = itertools.zip_longest(*[data[i::N_COLS] for i in range(N_COLS)])
table = tabulate(
data,
headers=["category", "#instances"] * (N_COLS // 2),
tablefmt="pipe",
numalign="left",
stralign="center",
)
log_first_n(
logging.INFO,
"Distribution of instances among all {} categories:\n".format(num_classes)
+ colored(table, "cyan"),
key="message",
)
def get_detection_dataset_dicts(
names,
filter_empty=True,
min_keypoints=0,
proposal_files=None,
check_consistency=True,
):
"""
Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation.
Args:
names (str or list[str]): a dataset name or a list of dataset names
filter_empty (bool): whether to filter out images without instance annotations
min_keypoints (int): filter out images with fewer keypoints than
`min_keypoints`. Set to 0 to do nothing.
proposal_files (list[str]): if given, a list of object proposal files
that match each dataset in `names`.
check_consistency (bool): whether to check if datasets have consistent metadata.
Returns:
list[dict]: a list of dicts following the standard dataset dict format.
"""
if isinstance(names, str):
names = [names]
assert len(names), names
available_datasets = DatasetCatalog.keys()
names_set = set(names)
if not names_set.issubset(available_datasets):
logger = logging.getLogger(__name__)
logger.warning(
"The following dataset names are not registered in the DatasetCatalog: "
f"{names_set - available_datasets}. "
f"Available datasets are {available_datasets}"
)
dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names]
if isinstance(dataset_dicts[0], torchdata.Dataset):
if len(dataset_dicts) > 1:
# ConcatDataset does not work for iterable style dataset.
# We could support concat for iterable as well, but it's often
# not a good idea to concat iterables anyway.
return torchdata.ConcatDataset(dataset_dicts)
return dataset_dicts[0]
for dataset_name, dicts in zip(names, dataset_dicts):
assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
if proposal_files is not None:
assert len(names) == len(proposal_files)
# load precomputed proposals from proposal files
dataset_dicts = [
load_proposals_into_dataset(dataset_i_dicts, proposal_file)
for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files)
]
dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts))
has_instances = "annotations" in dataset_dicts[0]
if filter_empty and has_instances:
dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts)
if min_keypoints > 0 and has_instances:
dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints)
if check_consistency and has_instances:
try:
class_names = MetadataCatalog.get(names[0]).thing_classes
check_metadata_consistency("thing_classes", names)
print_instances_class_histogram(dataset_dicts, class_names)
except AttributeError: # class names are not available for this dataset
pass
assert len(dataset_dicts), "No valid data found in {}.".format(",".join(names))
return dataset_dicts
def build_batch_data_loader(
dataset,
sampler,
total_batch_size,
*,
aspect_ratio_grouping=False,
num_workers=0,
collate_fn=None,
drop_last: bool = True,
**kwargs,
):
"""
Build a batched dataloader. The main differences from `torch.utils.data.DataLoader` are:
1. support aspect ratio grouping options
2. use no "batch collation", because this is common for detection training
Args:
dataset (torch.utils.data.Dataset): a pytorch map-style or iterable dataset.
sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces indices.
Must be provided iff. ``dataset`` is a map-style dataset.
total_batch_size, aspect_ratio_grouping, num_workers, collate_fn: see
:func:`build_detection_train_loader`.
drop_last (bool): if ``True``, the dataloader will drop incomplete batches.
Returns:
iterable[list]. Length of each list is the batch size of the current
GPU. Each element in the list comes from the dataset.
""" | world_size = get_world_size() | 2 | 2023-12-10 20:14:00+00:00 | 16k |
ethanweber/nerfiller | nerfiller/scripts/inpaint_nerfstudio_dataset.py | [
{
"identifier": "RGBInpainter",
"path": "nerfiller/inpaint/rgb_inpainter.py",
"snippet": "class RGBInpainter:\n \"\"\"\n Module for inpainting with the stable diffusion inpainting pipeline.\n \"\"\"\n\n def __init__(\n self,\n half_precision_weights: bool = True,\n lora_... | import json
import shutil
import mediapy
import torch
import tyro
import math
from pathlib import Path
from nerfiller.inpaint.rgb_inpainter import RGBInpainter
from nerfiller.inpaint.lama_inpainter import LaMaInpainter
from nerfiller.nerf.dataset_utils import parse_nerfstudio_frame
from nerfiller.utils.image_utils import get_inpainted_image_row
from nerfiller.utils.camera_utils import rescale_intrinsics
from nerfiller.configs.inpaint import InpaintConfig, AnnotatedBaseConfigUnion
from datetime import datetime
from nerfiller.utils.diff_utils import register_extended_attention
from nerfiller.utils.mask_utils import downscale_mask | 11,842 |
# Setup the modules for guidance.
# multiview_metric = ReprojectMetric(lossfeatmult=1.0)
# feature_extractor = SuperPointExtractor(device=config.device)
# TODO: make sure feature_extractor is half precision with half_precision_weights
# Copy the original dataset besides the images, which we will inpaint.
output_folder = (
Path(str(config.nerfstudio_dataset) + "-" + "inpaint")
/ str(config.method_name)
/ datetime.now().strftime("%Y-%m-%d_%H%M%S")
)
output_folder.mkdir(parents=True)
shutil.copytree(config.nerfstudio_dataset / "images", output_folder / "original_images")
shutil.copytree(config.nerfstudio_dataset / "masks", output_folder / "original_masks")
shutil.copytree(config.nerfstudio_dataset / "depth", output_folder / "depth")
shutil.copytree(config.nerfstudio_dataset / "masks", output_folder / "masks")
shutil.copy(config.nerfstudio_dataset / "transforms.json", output_folder / "transforms.json")
(output_folder / "images").mkdir(parents=True)
(output_folder / "inpaint").mkdir(parents=True)
f = open(config.nerfstudio_dataset / "transforms.json")
transforms = json.load(f)
f.close()
num_images = len(transforms["frames"])
if config.randomize_image_order:
indices = torch.randperm(num_images)
else:
indices = torch.arange(num_images)
padded_num_images = config.chunk_size * math.ceil(num_images / config.chunk_size)
if num_images != padded_num_images:
indices = torch.cat([indices, indices[: padded_num_images - num_images]])
for i in range(0, padded_num_images - config.chunk_size + 1, config.new_size):
images = []
masks = []
depths = []
Ks = []
c2ws = []
for j in range(i, i + config.chunk_size):
if i == 0 or j >= (i + config.chunk_size - config.new_size):
# new stuff to inpaint
image, depth, mask, c2w, K = parse_nerfstudio_frame(
transforms,
config.nerfstudio_dataset,
indices[j],
depth_max=config.depth_max,
device=config.device,
)
else:
# old stuff already inpainted
image, depth, mask, c2w, K = parse_nerfstudio_frame(
transforms,
output_folder,
indices[j],
depth_max=config.depth_max,
device=config.device,
)
images.append(image)
masks.append(mask)
depths.append(depth)
Ks.append(K)
c2ws.append(c2w)
images = torch.cat(images)
masks = torch.cat(masks)
depths = torch.cat(depths)
Ks = torch.cat(Ks)
c2ws = torch.cat(c2ws)
# generator = [
# torch.Generator(device=config.device).manual_seed(int(indices[j])) for j in range(i, i + config.chunk_size)
# ]
generator = None
image = torch.nn.functional.interpolate(images, scale_factor=config.scale_factor, mode="bilinear")
depth = torch.nn.functional.interpolate(depths, scale_factor=config.scale_factor, mode="bilinear")
mask = downscale_mask(
masks,
scale_factor=config.scale_factor,
dilate_iters=config.dilate_iters,
dilate_kernel_size=config.dilate_kernel_size,
)
if config.method_name == "individual-lama":
imagein = rgb_inpainter.get_image(image=image, mask=mask)
else:
enable_gradient = config.num_guidance_steps > 0 and len(config.guidance_steps) > 0
with torch.enable_grad() if enable_gradient else torch.no_grad():
imagein = rgb_inpainter.get_image(
text_embeddings=text_embeddings,
image=image,
mask=mask,
depth=depth,
denoise_in_grid=config.denoise_in_grid,
multidiffusion_steps=config.multidiffusion_steps,
randomize_latents=config.randomize_latents,
text_guidance_scale=config.text_guidance_scale,
image_guidance_scale=config.image_guidance_scale,
num_inference_steps=config.num_inference_steps,
num_guidance_steps=config.num_guidance_steps,
classifier_guidance_scale=config.classifier_guidance_scale,
guidance_steps=config.guidance_steps,
multiview_guidance_scale=config.multiview_guidance_scale,
K=rescale_intrinsics(Ks, config.scale_factor, config.scale_factor),
c2w=c2ws,
output_folder=output_folder / "inpaint" / f"{i:06d}-{i+config.chunk_size:06d}"
if config.save_intermediates
else None,
show_multiview=False,
generator=generator,
use_decoder_approximation=config.use_decoder_approximation,
)
# TODO: use an upscaler here
imagein = torch.nn.functional.interpolate(imagein, scale_factor=1 / config.scale_factor, mode="bilinear")
imagein = torch.where(masks == 1, imagein, images)
|
def main(
config: InpaintConfig,
):
"""
Inpaint a Nerfstudio dataset where the masks == 0.
"""
if config.method_name == "individual-lama":
rgb_inpainter = LaMaInpainter(device=config.device, model_path=Path("data/models/big-lama"))
else:
# Load the inpainting module.
rgb_inpainter = RGBInpainter(
half_precision_weights=config.half_precision_weights,
lora_model_path=config.lora_model_path,
device=config.device,
vae_device=config.vae_device,
)
if config.text_guidance_scale != 0.0:
assert config.prompt != "", "You need to set an actual prompt to use this method."
# Process the text prompts.
text_embeddings = rgb_inpainter.compute_text_embeddings(config.prompt, config.negative_prompt)
if config.use_expanded_attention:
register_extended_attention(rgb_inpainter.unet)
# Setup the modules for guidance.
# multiview_metric = ReprojectMetric(lossfeatmult=1.0)
# feature_extractor = SuperPointExtractor(device=config.device)
# TODO: make sure feature_extractor is half precision with half_precision_weights
# Copy the original dataset besides the images, which we will inpaint.
output_folder = (
Path(str(config.nerfstudio_dataset) + "-" + "inpaint")
/ str(config.method_name)
/ datetime.now().strftime("%Y-%m-%d_%H%M%S")
)
output_folder.mkdir(parents=True)
shutil.copytree(config.nerfstudio_dataset / "images", output_folder / "original_images")
shutil.copytree(config.nerfstudio_dataset / "masks", output_folder / "original_masks")
shutil.copytree(config.nerfstudio_dataset / "depth", output_folder / "depth")
shutil.copytree(config.nerfstudio_dataset / "masks", output_folder / "masks")
shutil.copy(config.nerfstudio_dataset / "transforms.json", output_folder / "transforms.json")
(output_folder / "images").mkdir(parents=True)
(output_folder / "inpaint").mkdir(parents=True)
f = open(config.nerfstudio_dataset / "transforms.json")
transforms = json.load(f)
f.close()
num_images = len(transforms["frames"])
if config.randomize_image_order:
indices = torch.randperm(num_images)
else:
indices = torch.arange(num_images)
padded_num_images = config.chunk_size * math.ceil(num_images / config.chunk_size)
if num_images != padded_num_images:
indices = torch.cat([indices, indices[: padded_num_images - num_images]])
for i in range(0, padded_num_images - config.chunk_size + 1, config.new_size):
images = []
masks = []
depths = []
Ks = []
c2ws = []
for j in range(i, i + config.chunk_size):
if i == 0 or j >= (i + config.chunk_size - config.new_size):
# new stuff to inpaint
image, depth, mask, c2w, K = parse_nerfstudio_frame(
transforms,
config.nerfstudio_dataset,
indices[j],
depth_max=config.depth_max,
device=config.device,
)
else:
# old stuff already inpainted
image, depth, mask, c2w, K = parse_nerfstudio_frame(
transforms,
output_folder,
indices[j],
depth_max=config.depth_max,
device=config.device,
)
images.append(image)
masks.append(mask)
depths.append(depth)
Ks.append(K)
c2ws.append(c2w)
images = torch.cat(images)
masks = torch.cat(masks)
depths = torch.cat(depths)
Ks = torch.cat(Ks)
c2ws = torch.cat(c2ws)
# generator = [
# torch.Generator(device=config.device).manual_seed(int(indices[j])) for j in range(i, i + config.chunk_size)
# ]
generator = None
image = torch.nn.functional.interpolate(images, scale_factor=config.scale_factor, mode="bilinear")
depth = torch.nn.functional.interpolate(depths, scale_factor=config.scale_factor, mode="bilinear")
mask = downscale_mask(
masks,
scale_factor=config.scale_factor,
dilate_iters=config.dilate_iters,
dilate_kernel_size=config.dilate_kernel_size,
)
if config.method_name == "individual-lama":
imagein = rgb_inpainter.get_image(image=image, mask=mask)
else:
enable_gradient = config.num_guidance_steps > 0 and len(config.guidance_steps) > 0
with torch.enable_grad() if enable_gradient else torch.no_grad():
imagein = rgb_inpainter.get_image(
text_embeddings=text_embeddings,
image=image,
mask=mask,
depth=depth,
denoise_in_grid=config.denoise_in_grid,
multidiffusion_steps=config.multidiffusion_steps,
randomize_latents=config.randomize_latents,
text_guidance_scale=config.text_guidance_scale,
image_guidance_scale=config.image_guidance_scale,
num_inference_steps=config.num_inference_steps,
num_guidance_steps=config.num_guidance_steps,
classifier_guidance_scale=config.classifier_guidance_scale,
guidance_steps=config.guidance_steps,
multiview_guidance_scale=config.multiview_guidance_scale,
K=rescale_intrinsics(Ks, config.scale_factor, config.scale_factor),
c2w=c2ws,
output_folder=output_folder / "inpaint" / f"{i:06d}-{i+config.chunk_size:06d}"
if config.save_intermediates
else None,
show_multiview=False,
generator=generator,
use_decoder_approximation=config.use_decoder_approximation,
)
# TODO: use an upscaler here
imagein = torch.nn.functional.interpolate(imagein, scale_factor=1 / config.scale_factor, mode="bilinear")
imagein = torch.where(masks == 1, imagein, images) | imageinrow = get_inpainted_image_row(image=images, mask=masks, inpainted_image=imagein, show_original=True) | 3 | 2023-12-07 19:12:08+00:00 | 16k |
nnanhuang/Customize-it-3D | ldm/models/diffusion/ddpm.py | [
{
"identifier": "log_txt_as_img",
"path": "ldm/util.py",
"snippet": "def log_txt_as_img(wh, xc, size=10):\n # wh a tuple of (width, height)\n # xc a list of captions to plot\n b = len(xc)\n txts = list()\n for bi in range(b):\n txt = Image.new(\"RGB\", wh, color=\"white\")\n ... | import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
import itertools
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager, nullcontext
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.rank_zero import rank_zero_only
from omegaconf import ListConfig
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.modules.attention import CrossAttention | 12,358 | force_c_encode=True,
return_original_cond=True,
bs=N)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
log["inputs"] = x
log["reconstruction"] = xrec
if self.model.conditioning_key is not None:
if hasattr(self.cond_stage_model, "decode"):
xc = self.cond_stage_model.decode(c)
log["conditioning"] = xc
elif self.cond_stage_key in ["caption", "txt"]:
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2]//25)
log["conditioning"] = xc
elif self.cond_stage_key == 'class_label':
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2]//25)
log['conditioning'] = xc
elif isimage(xc):
log["conditioning"] = xc
if ismap(xc):
log["original_conditioning"] = self.to_rgb(xc)
if plot_diffusion_rows:
# get diffusion row
diffusion_row = list()
z_start = z[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(z_start)
z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
diffusion_row.append(self.decode_first_stage(z_noisy))
diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
log["diffusion_row"] = diffusion_grid
if sample:
# get denoise row
with ema_scope("Sampling"):
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
ddim_steps=ddim_steps,eta=ddim_eta)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
x_samples = self.decode_first_stage(samples)
log["samples"] = x_samples
if plot_denoise_rows:
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
log["denoise_row"] = denoise_grid
if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
self.first_stage_model, IdentityFirstStage):
# also display when quantizing x0 while sampling
with ema_scope("Plotting Quantized Denoised"):
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
ddim_steps=ddim_steps,eta=ddim_eta,
quantize_denoised=True)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
# quantize_denoised=True)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_x0_quantized"] = x_samples
if unconditional_guidance_scale > 1.0:
uc = self.get_unconditional_conditioning(N, unconditional_guidance_label, image_size=x.shape[-1])
# uc = torch.zeros_like(c)
with ema_scope("Sampling with classifier-free guidance"):
samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
ddim_steps=ddim_steps, eta=ddim_eta,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=uc,
)
x_samples_cfg = self.decode_first_stage(samples_cfg)
log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
if inpaint:
# make a simple center square
b, h, w = z.shape[0], z.shape[2], z.shape[3]
mask = torch.ones(N, h, w).to(self.device)
# zeros will be filled in
mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
mask = mask[:, None, ...]
with ema_scope("Plotting Inpaint"):
samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_inpainting"] = x_samples
log["mask"] = mask
# outpaint
mask = 1. - mask
with ema_scope("Plotting Outpaint"):
samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_outpainting"] = x_samples
if plot_progressive_rows:
with ema_scope("Plotting Progressives"):
img, progressives = self.progressive_denoising(c,
shape=(self.channels, self.image_size, self.image_size),
batch_size=N)
prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
log["progressive_row"] = prog_row
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = []
if self.unet_trainable == "attn":
print("Training only unet attention layers")
for n, m in self.model.named_modules():
| """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
make_it_fit=False,
ucg_training=None,
):
super().__init__()
assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
self.make_it_fit = make_it_fit
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
self.ucg_training = ucg_training or dict()
if self.ucg_training:
self.ucg_prng = np.random.RandomState()
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
1. - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer('posterior_variance', to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
self.register_buffer('posterior_mean_coef1', to_torch(
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
self.register_buffer('posterior_mean_coef2', to_torch(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
if self.parameterization == "eps":
lvlb_weights = self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
elif self.parameterization == "x0":
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
@torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
if self.make_it_fit:
n_params = len([name for name, _ in
itertools.chain(self.named_parameters(),
self.named_buffers())])
for name, param in tqdm(
itertools.chain(self.named_parameters(),
self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params
):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape)==len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop((batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates)
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
def get_loss(self, pred, target, mean=True):
if self.loss_type == 'l1':
loss = (target - pred).abs()
if mean:
loss = loss.mean()
elif self.loss_type == 'l2':
if mean:
loss = torch.nn.functional.mse_loss(target, pred)
else:
loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
return loss
def p_losses(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_out = self.model(x_noisy, t)
loss_dict = {}
if self.parameterization == "eps":
target = noise
elif self.parameterization == "x0":
target = x_start
else:
raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
log_prefix = 'train' if self.training else 'val'
loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
loss_simple = loss.mean() * self.l_simple_weight
loss_vlb = (self.lvlb_weights[t] * loss).mean()
loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
loss = loss_simple + self.original_elbo_weight * loss_vlb
loss_dict.update({f'{log_prefix}/loss': loss})
return loss, loss_dict
def forward(self, x, *args, **kwargs):
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
return self.p_losses(x, t, *args, **kwargs)
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = rearrange(x, 'b h w c -> b c h w')
x = x.to(memory_format=torch.contiguous_format).float()
return x
def shared_step(self, batch):
x = self.get_input(batch, self.first_stage_key)
loss, loss_dict = self(x)
return loss, loss_dict
def training_step(self, batch, batch_idx):
for k in self.ucg_training:
p = self.ucg_training[k]["p"]
val = self.ucg_training[k]["val"]
if val is None:
val = ""
for i in range(len(batch[k])):
if self.ucg_prng.choice(2, p=[1-p, p]):
batch[k][i] = val
loss, loss_dict = self.shared_step(batch)
self.log_dict(loss_dict, prog_bar=True,
logger=True, on_step=True, on_epoch=True)
self.log("global_step", self.global_step,
prog_bar=True, logger=True, on_step=True, on_epoch=False)
if self.use_scheduler:
lr = self.optimizers().param_groups[0]['lr']
self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
return loss
@torch.no_grad()
def validation_step(self, batch, batch_idx):
_, loss_dict_no_ema = self.shared_step(batch)
with self.ema_scope():
_, loss_dict_ema = self.shared_step(batch)
loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self.model)
def _get_rows_from_list(self, samples):
n_imgs_per_row = len(samples)
denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
@torch.no_grad()
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
log = dict()
x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
x = x.to(self.device)[:N]
log["inputs"] = x
# get diffusion row
diffusion_row = list()
x_start = x[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(x_start)
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
diffusion_row.append(x_noisy)
log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
log["samples"] = samples
log["denoise_row"] = self._get_rows_from_list(denoise_row)
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.model.parameters())
if self.learn_logvar:
params = params + [self.logvar]
opt = torch.optim.AdamW(params, lr=lr)
return opt
class LatentDiffusion(DDPM):
"""main class"""
def __init__(self,
first_stage_config,
cond_stage_config,
num_timesteps_cond=None,
cond_stage_key="image",
cond_stage_trainable=False,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
unet_trainable=True,
*args, **kwargs):
self.num_timesteps_cond = default(num_timesteps_cond, 1)
self.scale_by_std = scale_by_std
assert self.num_timesteps_cond <= kwargs['timesteps']
# for backwards compatibility after implementation of DiffusionWrapper
if conditioning_key is None:
conditioning_key = 'concat' if concat_mode else 'crossattn'
if cond_stage_config == '__is_unconditional__':
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.unet_trainable = unet_trainable
self.cond_stage_key = cond_stage_key
try:
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
except:
self.num_downs = 0
if not scale_by_std:
self.scale_factor = scale_factor
else:
self.register_buffer('scale_factor', torch.tensor(scale_factor))
self.instantiate_first_stage(first_stage_config)
self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward
# construct linear projection layer for concatenating image CLIP embedding and RT
self.cc_projection = nn.Linear(772, 768)
nn.init.eye_(list(self.cc_projection.parameters())[0][:768, :768])
nn.init.zeros_(list(self.cc_projection.parameters())[1])
self.cc_projection.requires_grad_(True)
self.clip_denoised = False
self.bbox_tokenizer = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys)
self.restarted_from_ckpt = True
def make_cond_schedule(self, ):
self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
self.cond_ids[:self.num_timesteps_cond] = ids
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
# only for very first batch
if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
# set rescale weight to 1./std of encodings
print("### USING STD-RESCALING ###")
x = super().get_input(batch, self.first_stage_key)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
del self.scale_factor
self.register_buffer('scale_factor', 1. / z.flatten().std())
print(f"setting self.scale_factor to {self.scale_factor}")
print("### USING STD-RESCALING ###")
def register_schedule(self,
given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
self.make_cond_schedule()
def instantiate_first_stage(self, config):
model = instantiate_from_config(config)
self.first_stage_model = model.eval()
self.first_stage_model.train = disabled_train
for param in self.first_stage_model.parameters():
param.requires_grad = False
def instantiate_cond_stage(self, config):
if not self.cond_stage_trainable:
if config == "__is_first_stage__":
print("Using first stage also as cond stage.")
self.cond_stage_model = self.first_stage_model
elif config == "__is_unconditional__":
print(f"Training {self.__class__.__name__} as an unconditional model.")
self.cond_stage_model = None
# self.be_unconditional = True
else:
model = instantiate_from_config(config)
self.cond_stage_model = model.eval()
self.cond_stage_model.train = disabled_train
for param in self.cond_stage_model.parameters():
param.requires_grad = False
else:
assert config != '__is_first_stage__'
assert config != '__is_unconditional__'
model = instantiate_from_config(config)
self.cond_stage_model = model
def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
denoise_row = []
for zd in tqdm(samples, desc=desc):
denoise_row.append(self.decode_first_stage(zd.to(self.device),
force_not_quantize=force_no_decoder_quantization))
n_imgs_per_row = len(denoise_row)
denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
def get_first_stage_encoding(self, encoder_posterior):
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
z = encoder_posterior.sample()
elif isinstance(encoder_posterior, torch.Tensor):
z = encoder_posterior
else:
raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
return self.scale_factor * z
def get_learned_conditioning(self, c):
if self.cond_stage_forward is None:
if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
c = self.cond_stage_model.encode(c)
if isinstance(c, DiagonalGaussianDistribution):
c = c.mode()
else:
c = self.cond_stage_model(c)
else:
assert hasattr(self.cond_stage_model, self.cond_stage_forward)
c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
return c
def meshgrid(self, h, w):
y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
arr = torch.cat([y, x], dim=-1)
return arr
def delta_border(self, h, w):
"""
:param h: height
:param w: width
:return: normalized distance to image border,
wtith min distance = 0 at border and max dist = 0.5 at image center
"""
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
arr = self.meshgrid(h, w) / lower_right_corner
dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
return edge_dist
def get_weighting(self, h, w, Ly, Lx, device):
weighting = self.delta_border(h, w)
weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
self.split_input_params["clip_max_weight"], )
weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
if self.split_input_params["tie_braker"]:
L_weighting = self.delta_border(Ly, Lx)
L_weighting = torch.clip(L_weighting,
self.split_input_params["clip_min_tie_weight"],
self.split_input_params["clip_max_tie_weight"])
L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
weighting = weighting * L_weighting
return weighting
def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
"""
:param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
"""
bs, nc, h, w = x.shape
# number of crops in image
Ly = (h - kernel_size[0]) // stride[0] + 1
Lx = (w - kernel_size[1]) // stride[1] + 1
if uf == 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
elif uf > 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
dilation=1, padding=0,
stride=(stride[0] * uf, stride[1] * uf))
fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
elif df > 1 and uf == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
dilation=1, padding=0,
stride=(stride[0] // df, stride[1] // df))
fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
else:
raise NotImplementedError
return fold, unfold, normalization, weighting
@torch.no_grad()
def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
cond_key=None, return_original_cond=False, bs=None, uncond=0.05):
x = super().get_input(batch, k)
T = batch['T'].to(memory_format=torch.contiguous_format).float()
if bs is not None:
x = x[:bs]
T = T[:bs].to(self.device)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
cond_key = cond_key or self.cond_stage_key
xc = super().get_input(batch, cond_key).to(self.device)
if bs is not None:
xc = xc[:bs]
cond = {}
# To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%.
random = torch.rand(x.size(0), device=x.device)
prompt_mask = rearrange(random < 2 * uncond, "n -> n 1 1")
input_mask = 1 - rearrange((random >= uncond).float() * (random < 3 * uncond).float(), "n -> n 1 1 1")
null_prompt = self.get_learned_conditioning([""])
# z.shape: [8, 4, 64, 64]; c.shape: [8, 1, 768]
# print('=========== xc shape ===========', xc.shape)
with torch.enable_grad():
clip_emb = self.get_learned_conditioning(xc).detach()
null_prompt = self.get_learned_conditioning([""]).detach()
cond["c_crossattn"] = [self.cc_projection(torch.cat([torch.where(prompt_mask, null_prompt, clip_emb), T[:, None, :]], dim=-1))]
cond["c_concat"] = [input_mask * self.encode_first_stage((xc.to(self.device))).mode().detach()]
out = [z, cond]
if return_first_stage_outputs:
xrec = self.decode_first_stage(z)
out.extend([x, xrec])
if return_original_cond:
out.append(xc)
return out
# @torch.no_grad()
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
# @torch.no_grad() # wasted two hours to find this bug... why no grad here!
def encode_first_stage(self, x):
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
df = self.split_input_params["vqf"]
self.split_input_params['original_image_size'] = x.shape[-2:]
bs, nc, h, w = x.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
z = unfold(x) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization
return decoded
else:
return self.first_stage_model.encode(x)
else:
return self.first_stage_model.encode(x)
def shared_step(self, batch, **kwargs):
x, c = self.get_input(batch, self.first_stage_key)
loss = self(x, c)
return loss
def forward(self, x, c, *args, **kwargs):
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
if self.model.conditioning_key is not None:
assert c is not None
# if self.cond_stage_trainable:
# c = self.get_learned_conditioning(c)
if self.shorten_cond_schedule: # TODO: drop this option
tc = self.cond_ids[t].to(self.device)
c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
return self.p_losses(x, c, t, *args, **kwargs)
def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
def rescale_bbox(bbox):
x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
w = min(bbox[2] / crop_coordinates[2], 1 - x0)
h = min(bbox[3] / crop_coordinates[3], 1 - y0)
return x0, y0, w, h
return [rescale_bbox(b) for b in bboxes]
def apply_model(self, x_noisy, t, cond, return_ids=False):
if isinstance(cond, dict):
# hybrid case, cond is exptected to be a dict
pass
else:
if not isinstance(cond, list):
cond = [cond]
key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
cond = {key: cond}
if hasattr(self, "split_input_params"):
assert len(cond) == 1 # todo can only deal with one conditioning atm
assert not return_ids
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
h, w = x_noisy.shape[-2:]
fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
if self.cond_stage_key in ["image", "LR_image", "segmentation",
'bbox_img'] and self.model.conditioning_key: # todo check for completeness
c_key = next(iter(cond.keys())) # get key
c = next(iter(cond.values())) # get value
assert (len(c) == 1) # todo extend to list with more than one elem
c = c[0] # get element
c = unfold(c)
c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
elif self.cond_stage_key == 'coordinates_bbox':
assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
# assuming padding of unfold is always 0 and its dilation is always 1
n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
full_img_h, full_img_w = self.split_input_params['original_image_size']
# as we are operating on latents, we need the factor from the original image size to the
# spatial latent size to properly rescale the crops for regenerating the bbox annotations
num_downs = self.first_stage_model.encoder.num_resolutions - 1
rescale_latent = 2 ** (num_downs)
# get top left postions of patches as conforming for the bbbox tokenizer, therefore we
# need to rescale the tl patch coordinates to be in between (0,1)
tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
for patch_nr in range(z.shape[-1])]
# patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
patch_limits = [(x_tl, y_tl,
rescale_latent * ks[0] / full_img_w,
rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
# patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
# tokenize crop coordinates for the bounding boxes of the respective patches
patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
# cut tknzd crop position from conditioning
assert isinstance(cond, dict), 'cond must be dict to be fed into model'
cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
adapted_cond = self.get_learned_conditioning(adapted_cond)
adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
else:
cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
# apply model by loop over crops
output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
assert not isinstance(output_list[0],
tuple) # todo cant deal with multiple model outputs check this never happens
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
x_recon = fold(o) / normalization
else:
x_recon = self.model(x_noisy, t, **cond)
if isinstance(x_recon, tuple) and not return_ids:
return x_recon[0]
else:
return x_recon
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
def _prior_bpd(self, x_start):
"""
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
"""
batch_size = x_start.shape[0]
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
return mean_flat(kl_prior) / np.log(2.0)
def p_losses(self, x_start, cond, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_output = self.apply_model(x_noisy, t, cond)
loss_dict = {}
prefix = 'train' if self.training else 'val'
if self.parameterization == "x0":
target = x_start
elif self.parameterization == "eps":
target = noise
else:
raise NotImplementedError()
loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
logvar_t = self.logvar[t].to(self.device)
loss = loss_simple / torch.exp(logvar_t) + logvar_t
# loss = loss_simple / torch.exp(self.logvar) + self.logvar
if self.learn_logvar:
loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
loss_dict.update({'logvar': self.logvar.data.mean()})
loss = self.l_simple_weight * loss.mean()
loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
loss += (self.original_elbo_weight * loss_vlb)
loss_dict.update({f'{prefix}/loss': loss})
return loss, loss_dict
def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
return_x0=False, score_corrector=None, corrector_kwargs=None):
t_in = t
model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
if score_corrector is not None:
assert self.parameterization == "eps"
model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
if return_codebook_ids:
model_out, logits = model_out
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
else:
raise NotImplementedError()
if clip_denoised:
x_recon.clamp_(-1., 1.)
if quantize_denoised:
x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
if return_codebook_ids:
return model_mean, posterior_variance, posterior_log_variance, logits
elif return_x0:
return model_mean, posterior_variance, posterior_log_variance, x_recon
else:
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
return_codebook_ids=False, quantize_denoised=False, return_x0=False,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
b, *_, device = *x.shape, x.device
outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
return_codebook_ids=return_codebook_ids,
quantize_denoised=quantize_denoised,
return_x0=return_x0,
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
if return_codebook_ids:
raise DeprecationWarning("Support dropped.")
model_mean, _, model_log_variance, logits = outputs
elif return_x0:
model_mean, _, model_log_variance, x0 = outputs
else:
model_mean, _, model_log_variance = outputs
noise = noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
if return_codebook_ids:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
if return_x0:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
else:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
timesteps = self.num_timesteps
if batch_size is not None:
b = batch_size if batch_size is not None else shape[0]
shape = [batch_size] + list(shape)
else:
b = batch_size = shape[0]
if x_T is None:
img = torch.randn(shape, device=self.device)
else:
img = x_T
intermediates = []
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
total=timesteps) if verbose else reversed(
range(0, timesteps))
if type(temperature) == float:
temperature = [temperature] * timesteps
for i in iterator:
ts = torch.full((b,), i, device=self.device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img, x0_partial = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised, return_x0=True,
temperature=temperature[i], noise_dropout=noise_dropout,
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
if mask is not None:
assert x0 is not None
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(x0_partial)
if callback: callback(i)
if img_callback: img_callback(img, i)
return img, intermediates
@torch.no_grad()
def p_sample_loop(self, cond, shape, return_intermediates=False,
x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
device = self.betas.device
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
intermediates = [img]
if timesteps is None:
timesteps = self.num_timesteps
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
range(0, timesteps))
if mask is not None:
assert x0 is not None
assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
for i in iterator:
ts = torch.full((b,), i, device=device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised)
if mask is not None:
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(img)
if callback: callback(i)
if img_callback: img_callback(img, i)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
verbose=True, timesteps=None, quantize_denoised=False,
mask=None, x0=None, shape=None,**kwargs):
if shape is None:
shape = (batch_size, self.channels, self.image_size, self.image_size)
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
return self.p_sample_loop(cond,
shape,
return_intermediates=return_intermediates, x_T=x_T,
verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
mask=mask, x0=x0)
@torch.no_grad()
def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
if ddim:
ddim_sampler = DDIMSampler(self)
shape = (self.channels, self.image_size, self.image_size)
samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,
shape, cond, verbose=False, **kwargs)
else:
samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
return_intermediates=True, **kwargs)
return samples, intermediates
@torch.no_grad()
def get_unconditional_conditioning(self, batch_size, null_label=None, image_size=512):
if null_label is not None:
xc = null_label
if isinstance(xc, ListConfig):
xc = list(xc)
if isinstance(xc, dict) or isinstance(xc, list):
c = self.get_learned_conditioning(xc)
else:
if hasattr(xc, "to"):
xc = xc.to(self.device)
c = self.get_learned_conditioning(xc)
else:
# todo: get null label from cond_stage_model
raise NotImplementedError()
c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
cond = {}
cond["c_crossattn"] = [c]
cond["c_concat"] = [torch.zeros([batch_size, 4, image_size // 8, image_size // 8]).to(self.device)]
return cond
@torch.no_grad()
def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
use_ema_scope=True,
**kwargs):
ema_scope = self.ema_scope if use_ema_scope else nullcontext
use_ddim = ddim_steps is not None
log = dict()
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=True,
return_original_cond=True,
bs=N)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
log["inputs"] = x
log["reconstruction"] = xrec
if self.model.conditioning_key is not None:
if hasattr(self.cond_stage_model, "decode"):
xc = self.cond_stage_model.decode(c)
log["conditioning"] = xc
elif self.cond_stage_key in ["caption", "txt"]:
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2]//25)
log["conditioning"] = xc
elif self.cond_stage_key == 'class_label':
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2]//25)
log['conditioning'] = xc
elif isimage(xc):
log["conditioning"] = xc
if ismap(xc):
log["original_conditioning"] = self.to_rgb(xc)
if plot_diffusion_rows:
# get diffusion row
diffusion_row = list()
z_start = z[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(z_start)
z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
diffusion_row.append(self.decode_first_stage(z_noisy))
diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
log["diffusion_row"] = diffusion_grid
if sample:
# get denoise row
with ema_scope("Sampling"):
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
ddim_steps=ddim_steps,eta=ddim_eta)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
x_samples = self.decode_first_stage(samples)
log["samples"] = x_samples
if plot_denoise_rows:
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
log["denoise_row"] = denoise_grid
if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
self.first_stage_model, IdentityFirstStage):
# also display when quantizing x0 while sampling
with ema_scope("Plotting Quantized Denoised"):
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
ddim_steps=ddim_steps,eta=ddim_eta,
quantize_denoised=True)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
# quantize_denoised=True)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_x0_quantized"] = x_samples
if unconditional_guidance_scale > 1.0:
uc = self.get_unconditional_conditioning(N, unconditional_guidance_label, image_size=x.shape[-1])
# uc = torch.zeros_like(c)
with ema_scope("Sampling with classifier-free guidance"):
samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
ddim_steps=ddim_steps, eta=ddim_eta,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=uc,
)
x_samples_cfg = self.decode_first_stage(samples_cfg)
log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
if inpaint:
# make a simple center square
b, h, w = z.shape[0], z.shape[2], z.shape[3]
mask = torch.ones(N, h, w).to(self.device)
# zeros will be filled in
mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
mask = mask[:, None, ...]
with ema_scope("Plotting Inpaint"):
samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_inpainting"] = x_samples
log["mask"] = mask
# outpaint
mask = 1. - mask
with ema_scope("Plotting Outpaint"):
samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_outpainting"] = x_samples
if plot_progressive_rows:
with ema_scope("Plotting Progressives"):
img, progressives = self.progressive_denoising(c,
shape=(self.channels, self.image_size, self.image_size),
batch_size=N)
prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
log["progressive_row"] = prog_row
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = []
if self.unet_trainable == "attn":
print("Training only unet attention layers")
for n, m in self.model.named_modules(): | if isinstance(m, CrossAttention) and n.endswith('attn2'): | 18 | 2023-12-14 11:03:35+00:00 | 16k |
mkang315/ASF-YOLO | segment/predict.py | [
{
"identifier": "DetectMultiBackend",
"path": "models/common.py",
"snippet": "class DetectMultiBackend(nn.Module):\n # YOLOv5 MultiBackend class for python inference on various backends\n def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=Tr... | import argparse
import os
import platform
import sys
import time
import torch
from pathlib import Path
from models.common import DetectMultiBackend
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
increment_path, non_max_suppression, print_args, scale_boxes, scale_segments,
strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.segment.general import masks2segments, process_mask, process_mask_native
from utils.torch_utils import select_device, smart_inference_mode | 14,309 | # Run inference
model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup
seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
for path, im, im0s, vid_cap, s in dataset:
with dt[0]:
im = torch.from_numpy(im).to(model.device)
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
# Inference
with dt[1]:
act = time.time()
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred, proto = model(im, augment=augment, visualize=visualize)[:2]
print('time.time():',time.time()-act)
# NMS
with dt[2]:
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=32)
# Second-stage classifier (optional)
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
# Process predictions
for i, det in enumerate(pred): # per image
seen += 1
if webcam: # batch_size >= 1
p, im0, frame = path[i], im0s[i].copy(), dataset.count
s += f'{i}: '
else:
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
save_path = str(save_dir / p.name) # im.jpg
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
s += '%gx%g ' % im.shape[2:] # print string
imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det):
if retina_masks:
# scale bbox first the crop masks
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size
masks = process_mask_native(proto[i], det[:, 6:], det[:, :4], im0.shape[:2]) # HWC
else:
masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True) # HWC
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size
# Segments
if save_txt:
segments = reversed(masks2segments(masks))
segments = [
scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True)
for x in segments]
# Print results
for c in det[:, 5].unique():
n = (det[:, 5] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
# Mask plotting
plot_img = torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() / 255. \
if retina_masks else im[i]
annotator.masks(masks, colors=[colors(x, True) for x in det[:, 5]], im_gpu=plot_img)
# Write results
for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
if save_txt: # Write to file
segj = segments[j].reshape(-1) # (n,2) to (n*2)
line = (cls, *segj, conf) if save_conf else (cls, *segj) # label format
with open(f'{txt_path}.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or save_crop or view_img: # Add bbox to image
c = int(cls) # integer class
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
annotator.box_label(xyxy, label, color=colors(c, True))
# annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
if save_crop:
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
# Stream results
im0 = annotator.result()
if view_img:
if platform.system() == 'Linux' and p not in windows:
windows.append(p)
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
cv2.imshow(str(p), im0)
if cv2.waitKey(1) == ord('q'): # 1 millisecond
exit()
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if vid_path[i] != save_path: # new video
vid_path[i] = save_path
if isinstance(vid_writer[i], cv2.VideoWriter):
vid_writer[i].release() # release previous video writer
if vid_cap: # video
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer[i].write(im0)
# Print time (inference-only)
LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
# Print results
t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
| # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run YOLOv5 segmentation inference on images, videos, directories, streams, etc.
Usage - sources:
$ python segment/predict.py --weights yolov5s-seg.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
Usage - formats:
$ python segment/predict.py --weights yolov5s-seg.pt # PyTorch
yolov5s-seg.torchscript # TorchScript
yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s-seg_openvino_model # OpenVINO
yolov5s-seg.engine # TensorRT
yolov5s-seg.mlmodel # CoreML (macOS-only)
yolov5s-seg_saved_model # TensorFlow SavedModel
yolov5s-seg.pb # TensorFlow GraphDef
yolov5s-seg.tflite # TensorFlow Lite
yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU
yolov5s-seg_paddle_model # PaddlePaddle
"""
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
@smart_inference_mode()
def run(
weights=ROOT / 'yolov5s-seg.pt', # model.pt path(s)
source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam)
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
imgsz=(640, 640), # inference size (height, width)
conf_thres=0.25, # confidence threshold
iou_thres=0.45, # NMS IOU threshold
max_det=1000, # maximum detections per image
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
view_img=False, # show results
save_txt=False, # save results to *.txt
save_conf=False, # save confidences in --save-txt labels
save_crop=False, # save cropped prediction boxes
nosave=False, # do not save images/videos
classes=None, # filter by class: --class 0, or --class 0 2 3
agnostic_nms=False, # class-agnostic NMS
augment=False, # augmented inference
visualize=False, # visualize features
update=False, # update all models
project=ROOT / 'runs/predict-seg', # save results to project/name
name='exp', # save results to project/name
exist_ok=False, # existing project/name ok, do not increment
line_thickness=3, # bounding box thickness (pixels)
hide_labels=False, # hide labels
hide_conf=False, # hide confidences
half=False, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
vid_stride=1, # video frame-rate stride
retina_masks=False,
):
source = str(source)
save_img = not nosave and not source.endswith('.txt') # save inference images
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
screenshot = source.lower().startswith('screen')
if is_url and is_file:
source = check_file(source) # download
# Directories
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Load model
device = select_device(device)
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
stride, names, pt = model.stride, model.names, model.pt
imgsz = check_img_size(imgsz, s=stride) # check image size
# Dataloader
bs = 1 # batch_size
if webcam:
view_img = check_imshow(warn=True)
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
bs = len(dataset)
elif screenshot:
dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
vid_path, vid_writer = [None] * bs, [None] * bs
# Run inference
model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup
seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
for path, im, im0s, vid_cap, s in dataset:
with dt[0]:
im = torch.from_numpy(im).to(model.device)
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
# Inference
with dt[1]:
act = time.time()
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred, proto = model(im, augment=augment, visualize=visualize)[:2]
print('time.time():',time.time()-act)
# NMS
with dt[2]:
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=32)
# Second-stage classifier (optional)
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
# Process predictions
for i, det in enumerate(pred): # per image
seen += 1
if webcam: # batch_size >= 1
p, im0, frame = path[i], im0s[i].copy(), dataset.count
s += f'{i}: '
else:
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
save_path = str(save_dir / p.name) # im.jpg
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
s += '%gx%g ' % im.shape[2:] # print string
imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det):
if retina_masks:
# scale bbox first the crop masks
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size
masks = process_mask_native(proto[i], det[:, 6:], det[:, :4], im0.shape[:2]) # HWC
else:
masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True) # HWC
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size
# Segments
if save_txt:
segments = reversed(masks2segments(masks))
segments = [
scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True)
for x in segments]
# Print results
for c in det[:, 5].unique():
n = (det[:, 5] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
# Mask plotting
plot_img = torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() / 255. \
if retina_masks else im[i]
annotator.masks(masks, colors=[colors(x, True) for x in det[:, 5]], im_gpu=plot_img)
# Write results
for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
if save_txt: # Write to file
segj = segments[j].reshape(-1) # (n,2) to (n*2)
line = (cls, *segj, conf) if save_conf else (cls, *segj) # label format
with open(f'{txt_path}.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or save_crop or view_img: # Add bbox to image
c = int(cls) # integer class
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
annotator.box_label(xyxy, label, color=colors(c, True))
# annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
if save_crop:
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
# Stream results
im0 = annotator.result()
if view_img:
if platform.system() == 'Linux' and p not in windows:
windows.append(p)
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
cv2.imshow(str(p), im0)
if cv2.waitKey(1) == ord('q'): # 1 millisecond
exit()
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if vid_path[i] != save_path: # new video
vid_path[i] = save_path
if isinstance(vid_writer[i], cv2.VideoWriter):
vid_writer[i].release() # release previous video writer
if vid_cap: # video
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer[i].write(im0)
# Print time (inference-only)
LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
# Print results
t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' | LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") | 6 | 2023-12-10 14:18:29+00:00 | 16k |
youngskkim/CRN | models/camera_radar_net_det.py | [
{
"identifier": "BaseBEVDepth",
"path": "models/base_bev_depth.py",
"snippet": "class BaseBEVDepth(nn.Module):\n \"\"\"Source code of `BEVDepth`, `https://arxiv.org/abs/2112.11790`.\n\n Args:\n backbone_conf (dict): Config of backbone.\n head_conf (dict): Config of head.\n \"\"\"\... | import mmcv
from models.base_bev_depth import BaseBEVDepth
from layers.backbones.rvt_lss_fpn import RVTLSSFPN
from layers.backbones.pts_backbone import PtsBackbone
from layers.fuser.multimodal_feature_aggregation import MFAFuser
from layers.heads.bev_depth_head_det import BEVDepthHead | 11,825 |
logger = mmcv.utils.get_logger('mmdet')
logger.setLevel('WARNING')
__all__ = ['CameraRadarNetDet']
class CameraRadarNetDet(BaseBEVDepth):
"""Source code of `CRN`, `https://arxiv.org/abs/2304.00670`.
Args:
backbone_img_conf (dict): Config of image backbone.
backbone_pts_conf (dict): Config of point backbone.
fuser_conf (dict): Config of BEV feature fuser.
head_conf (dict): Config of head.
"""
def __init__(self, backbone_img_conf, backbone_pts_conf, fuser_conf, head_conf):
super(BaseBEVDepth, self).__init__()
self.backbone_img = RVTLSSFPN(**backbone_img_conf)
self.backbone_pts = PtsBackbone(**backbone_pts_conf)
|
logger = mmcv.utils.get_logger('mmdet')
logger.setLevel('WARNING')
__all__ = ['CameraRadarNetDet']
class CameraRadarNetDet(BaseBEVDepth):
"""Source code of `CRN`, `https://arxiv.org/abs/2304.00670`.
Args:
backbone_img_conf (dict): Config of image backbone.
backbone_pts_conf (dict): Config of point backbone.
fuser_conf (dict): Config of BEV feature fuser.
head_conf (dict): Config of head.
"""
def __init__(self, backbone_img_conf, backbone_pts_conf, fuser_conf, head_conf):
super(BaseBEVDepth, self).__init__()
self.backbone_img = RVTLSSFPN(**backbone_img_conf)
self.backbone_pts = PtsBackbone(**backbone_pts_conf) | self.fuser = MFAFuser(**fuser_conf) | 3 | 2023-12-06 14:57:49+00:00 | 16k |
LIU-Yuxin/SyncMVD | src/pipeline.py | [
{
"identifier": "UVProjection",
"path": "src/renderer/project.py",
"snippet": "class UVProjection():\n\tdef __init__(self, texture_size=96, render_size=64, sampling_mode=\"nearest\", channels=3, device=None):\n\t\tself.channels = channels\n\t\tself.device = device or torch.device(\"cpu\")\n\t\tself.ligh... | import os
import numpy as np
import math
import random
import torch
import select
import sys
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from PIL import Image
from IPython.display import display
from torch import functional as F
from torch import nn
from torchvision.transforms import Compose, Resize, GaussianBlur, InterpolationMode
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from diffusers import DDPMScheduler, DDIMScheduler, UniPCMultistepScheduler
from diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
BaseOutput,
randn_tensor,
numpy_to_pil,
pt_to_pil,
# make_image_grid,
is_accelerate_available,
is_accelerate_version,
is_compiled_module,
logging,
randn_tensor,
replace_example_docstring
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.models.attention_processor import Attention, AttentionProcessor
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from .renderer.project import UVProjection as UVP
from .syncmvd.attention import SamplewiseAttnProcessor2_0, replace_attention_processors
from .syncmvd.prompt import *
from .syncmvd.step import step_tex
from .utils import * | 12,093 |
for prompt_tag, prompt_embeds in prompt_embeds_groups.items():
if prompt_tag == "positive" or not guess_mode:
# controlnet(s) inference
control_model_input = latent_model_input
controlnet_prompt_embeds = prompt_embeds
if isinstance(controlnet_keep[i], list):
cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
else:
controlnet_cond_scale = controlnet_conditioning_scale
if isinstance(controlnet_cond_scale, list):
controlnet_cond_scale = controlnet_cond_scale[0]
cond_scale = controlnet_cond_scale * controlnet_keep[i]
# Split into micro-batches according to group meta info
# Ignore this feature for now
down_block_res_samples_list = []
mid_block_res_sample_list = []
model_input_batches = [torch.index_select(control_model_input, dim=0, index=torch.tensor(meta[0], device=self._execution_device)) for meta in self.group_metas]
prompt_embeds_batches = [torch.index_select(controlnet_prompt_embeds, dim=0, index=torch.tensor(meta[0], device=self._execution_device)) for meta in self.group_metas]
conditioning_images_batches = [torch.index_select(conditioning_images, dim=0, index=torch.tensor(meta[0], device=self._execution_device)) for meta in self.group_metas]
for model_input_batch ,prompt_embeds_batch, conditioning_images_batch \
in zip (model_input_batches, prompt_embeds_batches, conditioning_images_batches):
down_block_res_samples, mid_block_res_sample = self.controlnet(
model_input_batch,
t,
encoder_hidden_states=prompt_embeds_batch,
controlnet_cond=conditioning_images_batch,
conditioning_scale=cond_scale,
guess_mode=guess_mode,
return_dict=False,
)
down_block_res_samples_list.append(down_block_res_samples)
mid_block_res_sample_list.append(mid_block_res_sample)
''' For the ith element of down_block_res_samples, concat the ith element of all mini-batch result '''
model_input_batches = prompt_embeds_batches = conditioning_images_batches = None
if guess_mode:
for dbres in down_block_res_samples_list:
dbres_sizes = []
for res in dbres:
dbres_sizes.append(res.shape)
dbres_sizes_list.append(dbres_sizes)
for mbres in mid_block_res_sample_list:
mbres_size_list.append(mbres.shape)
else:
# Infered ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# add 0 to the unconditional batch to keep it unchanged.
# We copy the tensor shapes from a conditional batch
down_block_res_samples_list = []
mid_block_res_sample_list = []
for dbres_sizes in dbres_sizes_list:
down_block_res_samples_list.append([torch.zeros(shape, device=self._execution_device, dtype=latents.dtype) for shape in dbres_sizes])
for mbres in mbres_size_list:
mid_block_res_sample_list.append(torch.zeros(mbres, device=self._execution_device, dtype=latents.dtype))
dbres_sizes_list = []
mbres_size_list = []
'''
predict the noise residual, split into mini-batches
Downblock res samples has n samples, we split each sample into m batches
and re group them into m lists of n mini batch samples.
'''
noise_pred_list = []
model_input_batches = [torch.index_select(latent_model_input, dim=0, index=torch.tensor(meta[0], device=self._execution_device)) for meta in self.group_metas]
prompt_embeds_batches = [torch.index_select(prompt_embeds, dim=0, index=torch.tensor(meta[0], device=self._execution_device)) for meta in self.group_metas]
for model_input_batch, prompt_embeds_batch, down_block_res_samples_batch, mid_block_res_sample_batch, meta \
in zip(model_input_batches, prompt_embeds_batches, down_block_res_samples_list, mid_block_res_sample_list, self.group_metas):
if t > num_timesteps * (1- ref_attention_end):
replace_attention_processors(self.unet, SamplewiseAttnProcessor2_0, attention_mask=meta[2], ref_attention_mask=meta[3], ref_weight=1)
else:
replace_attention_processors(self.unet, SamplewiseAttnProcessor2_0, attention_mask=meta[2], ref_attention_mask=meta[3], ref_weight=0)
noise_pred = self.unet(
model_input_batch,
t,
encoder_hidden_states=prompt_embeds_batch,
cross_attention_kwargs=cross_attention_kwargs,
down_block_additional_residuals=down_block_res_samples_batch,
mid_block_additional_residual=mid_block_res_sample_batch,
return_dict=False,
)[0]
noise_pred_list.append(noise_pred)
noise_pred_list = [torch.index_select(noise_pred, dim=0, index=torch.tensor(meta[1], device=self._execution_device)) for noise_pred, meta in zip(noise_pred_list, self.group_metas)]
noise_pred = torch.cat(noise_pred_list, dim=0)
down_block_res_samples_list = None
mid_block_res_sample_list = None
noise_pred_list = None
model_input_batches = prompt_embeds_batches = down_block_res_samples_batches = mid_block_res_sample_batches = None
result_groups[prompt_tag] = noise_pred
positive_noise_pred = result_groups["positive"]
# perform guidance
if do_classifier_free_guidance:
noise_pred = result_groups["negative"] + guidance_scale * (positive_noise_pred - result_groups["negative"])
if do_classifier_free_guidance and guidance_rescale > 0.0:
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
self.uvp.to(self._execution_device)
# compute the previous noisy sample x_t -> x_t-1
# Multi-View step or individual step
if t > (1-multiview_diffusion_end)*num_timesteps:
|
if torch.cuda.is_available():
device = torch.device("cuda:0")
torch.cuda.set_device(device)
else:
device = torch.device("cpu")
# Background colors
color_constants = {"black": [-1, -1, -1], "white": [1, 1, 1], "maroon": [0, -1, -1],
"red": [1, -1, -1], "olive": [0, 0, -1], "yellow": [1, 1, -1],
"green": [-1, 0, -1], "lime": [-1 ,1, -1], "teal": [-1, 0, 0],
"aqua": [-1, 1, 1], "navy": [-1, -1, 0], "blue": [-1, -1, 1],
"purple": [0, -1 , 0], "fuchsia": [1, -1, 1]}
color_names = list(color_constants.keys())
# Used to generate depth or normal conditioning images
@torch.no_grad()
def get_conditioning_images(uvp, output_size, render_size=512, blur_filter=5, cond_type="normal"):
verts, normals, depths, cos_maps, texels, fragments = uvp.render_geometry(image_size=render_size)
masks = normals[...,3][:,None,...]
masks = Resize((output_size//8,)*2, antialias=True)(masks)
normals_transforms = Compose([
Resize((output_size,)*2, interpolation=InterpolationMode.BILINEAR, antialias=True),
GaussianBlur(blur_filter, blur_filter//3+1)]
)
if cond_type == "normal":
view_normals = uvp.decode_view_normal(normals).permute(0,3,1,2) *2 - 1
conditional_images = normals_transforms(view_normals)
# Some problem here, depth controlnet don't work when depth is normalized
# But it do generate using the unnormalized form as below
elif cond_type == "depth":
view_depths = uvp.decode_normalized_depth(depths).permute(0,3,1,2)
conditional_images = normals_transforms(view_depths)
return conditional_images, masks
# Revert time 0 background to time t to composite with time t foreground
@torch.no_grad()
def composite_rendered_view(scheduler, backgrounds, foregrounds, masks, t):
composited_images = []
for i, (background, foreground, mask) in enumerate(zip(backgrounds, foregrounds, masks)):
if t > 0:
alphas_cumprod = scheduler.alphas_cumprod[t]
noise = torch.normal(0, 1, background.shape, device=background.device)
background = (1-alphas_cumprod) * noise + alphas_cumprod * background
composited = foreground * mask + background * (1-mask)
composited_images.append(composited)
composited_tensor = torch.stack(composited_images)
return composited_tensor
# Split into micro-batches to use less memory in each unet prediction
# But need more investigation on reducing memory usage
# Assume it has no possitive effect and use a large "max_batch_size" to skip splitting
def split_groups(attention_mask, max_batch_size, ref_view=[]):
group_sets = []
group = set()
ref_group = set()
idx = 0
while idx < len(attention_mask):
new_group = group | set([idx])
new_ref_group = (ref_group | set(attention_mask[idx] + ref_view)) - new_group
if len(new_group) + len(new_ref_group) <= max_batch_size:
group = new_group
ref_group = new_ref_group
idx += 1
else:
assert len(group) != 0, "Cannot fit into a group"
group_sets.append((group, ref_group))
group = set()
ref_group = set()
if len(group)>0:
group_sets.append((group, ref_group))
group_metas = []
for group, ref_group in group_sets:
in_mask = sorted(list(group | ref_group))
out_mask = []
group_attention_masks = []
for idx in in_mask:
if idx in group:
out_mask.append(in_mask.index(idx))
group_attention_masks.append([in_mask.index(idxx) for idxx in attention_mask[idx] if idxx in in_mask])
ref_attention_mask = [in_mask.index(idx) for idx in ref_view]
group_metas.append([in_mask, out_mask, group_attention_masks, ref_attention_mask])
return group_metas
'''
MultiView-Diffusion Stable-Diffusion Pipeline
Modified from a Diffusers StableDiffusionControlNetPipeline
Just mimic the pipeline structure but did not follow any API convention
'''
class StableSyncMVDPipeline(StableDiffusionControlNetPipeline):
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel]],
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = False,
):
super().__init__(
vae, text_encoder, tokenizer, unet,
controlnet, scheduler, safety_checker,
feature_extractor, requires_safety_checker
)
self.scheduler = DDPMScheduler.from_config(self.scheduler.config)
self.model_cpu_offload_seq = "vae->text_encoder->unet->vae"
self.enable_model_cpu_offload()
self.enable_vae_slicing()
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
def initialize_pipeline(
self,
mesh_path=None,
mesh_transform=None,
mesh_autouv=None,
camera_azims=None,
camera_centers=None,
top_cameras=True,
ref_views=[],
latent_size=None,
render_rgb_size=None,
texture_size=None,
texture_rgb_size=None,
max_batch_size=24,
logging_config=None,
):
# Make output dir
output_dir = logging_config["output_dir"]
self.result_dir = f"{output_dir}/results"
self.intermediate_dir = f"{output_dir}/intermediate"
dirs = [output_dir, self.result_dir, self.intermediate_dir]
for dir_ in dirs:
if not os.path.isdir(dir_):
os.mkdir(dir_)
# Define the cameras for rendering
self.camera_poses = []
self.attention_mask=[]
self.centers = camera_centers
cam_count = len(camera_azims)
front_view_diff = 360
back_view_diff = 360
front_view_idx = 0
back_view_idx = 0
for i, azim in enumerate(camera_azims):
if azim < 0:
azim += 360
self.camera_poses.append((0, azim))
self.attention_mask.append([(cam_count+i-1)%cam_count, i, (i+1)%cam_count])
if abs(azim) < front_view_diff:
front_view_idx = i
front_view_diff = abs(azim)
if abs(azim - 180) < back_view_diff:
back_view_idx = i
back_view_diff = abs(azim - 180)
# Add two additional cameras for painting the top surfaces
if top_cameras:
self.camera_poses.append((30, 0))
self.camera_poses.append((30, 180))
self.attention_mask.append([front_view_idx, cam_count])
self.attention_mask.append([back_view_idx, cam_count+1])
# Reference view for attention (all views attend the the views in this list)
# A forward view will be used if not specified
if len(ref_views) == 0:
ref_views = [front_view_idx]
# Calculate in-group attention mask
self.group_metas = split_groups(self.attention_mask, max_batch_size, ref_views)
# Set up pytorch3D for projection between screen space and UV space
# uvp is for latent and uvp_rgb for rgb color
self.uvp = UVP(texture_size=texture_size, render_size=latent_size, sampling_mode="nearest", channels=4, device=self._execution_device)
if mesh_path.lower().endswith(".obj"):
self.uvp.load_mesh(mesh_path, scale_factor=mesh_transform["scale"] or 1, autouv=mesh_autouv)
elif mesh_path.lower().endswith(".glb"):
self.uvp.load_glb_mesh(mesh_path, scale_factor=mesh_transform["scale"] or 1, autouv=mesh_autouv)
else:
assert False, "The mesh file format is not supported. Use .obj or .glb."
self.uvp.set_cameras_and_render_settings(self.camera_poses, centers=camera_centers, camera_distance=4.0)
self.uvp_rgb = UVP(texture_size=texture_rgb_size, render_size=render_rgb_size, sampling_mode="nearest", channels=3, device=self._execution_device)
self.uvp_rgb.mesh = self.uvp.mesh.clone()
self.uvp_rgb.set_cameras_and_render_settings(self.camera_poses, centers=camera_centers, camera_distance=4.0)
_,_,_,cos_maps,_, _ = self.uvp_rgb.render_geometry()
self.uvp_rgb.calculate_cos_angle_weights(cos_maps, fill=False)
# Save some VRAM
del _, cos_maps
self.uvp.to("cpu")
self.uvp_rgb.to("cpu")
color_images = torch.FloatTensor([color_constants[name] for name in color_names]).reshape(-1,3,1,1).to(dtype=self.text_encoder.dtype, device=self._execution_device)
color_images = torch.ones(
(1,1,latent_size*8, latent_size*8),
device=self._execution_device,
dtype=self.text_encoder.dtype
) * color_images
color_images *= ((0.5*color_images)+0.5)
color_latents = encode_latents(self.vae, color_images)
self.color_latents = {color[0]:color[1] for color in zip(color_names, [latent for latent in color_latents])}
self.vae = self.vae.to("cpu")
print("Done Initialization")
'''
Modified from a StableDiffusion ControlNet pipeline
Multi ControlNet not supported yet
'''
@torch.no_grad()
def __call__(
self,
prompt: str = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: str = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
return_dict: bool = False,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
max_batch_size=6,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
controlnet_guess_mode: bool = False,
controlnet_conditioning_scale: Union[float, List[float]] = 0.7,
controlnet_conditioning_end_scale: Union[float, List[float]] = 0.9,
control_guidance_start: Union[float, List[float]] = 0.0,
control_guidance_end: Union[float, List[float]] = 0.99,
guidance_rescale: float = 0.0,
mesh_path: str = None,
mesh_transform: dict = None,
mesh_autouv = False,
camera_azims=None,
camera_centers=None,
top_cameras=True,
texture_size = 1536,
render_rgb_size=1024,
texture_rgb_size = 1024,
multiview_diffusion_end=0.8,
shuffle_background_change=0.4,
shuffle_background_end=0.99, #0.4
use_directional_prompt=True,
ref_attention_end=0.2,
logging_config=None,
cond_type="depth",
):
# Setup pipeline settings
self.initialize_pipeline(
mesh_path=mesh_path,
mesh_transform=mesh_transform,
mesh_autouv=mesh_autouv,
camera_azims=camera_azims,
camera_centers=camera_centers,
top_cameras=top_cameras,
ref_views=[],
latent_size=height//8,
render_rgb_size=render_rgb_size,
texture_size=texture_size,
texture_rgb_size=texture_rgb_size,
max_batch_size=max_batch_size,
logging_config=logging_config
)
num_timesteps = self.scheduler.config.num_train_timesteps
initial_controlnet_conditioning_scale = controlnet_conditioning_scale
log_interval = logging_config.get("log_interval", 10)
view_fast_preview = logging_config.get("view_fast_preview", True)
tex_fast_preview = logging_config.get("tex_fast_preview", True)
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
# align format for control guidance
if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
control_guidance_start = len(control_guidance_end) * [control_guidance_start]
elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
control_guidance_end = len(control_guidance_start) * [control_guidance_end]
elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
# mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
mult = 1
control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [
control_guidance_end
]
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
torch.zeros((1,3,height,width), device=self._execution_device),
callback_steps,
negative_prompt,
None,
None,
controlnet_conditioning_scale,
control_guidance_start,
control_guidance_end,
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, list):
assert len(prompt) == 1 and len(negative_prompt) == 1, "Only implemented for 1 (negative) prompt"
assert num_images_per_prompt == 1, "Only implemented for 1 image per-prompt"
batch_size = len(self.uvp.cameras)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
# controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
global_pool_conditions = (
controlnet.config.global_pool_conditions
if isinstance(controlnet, ControlNetModel)
else controlnet.nets[0].config.global_pool_conditions
)
guess_mode = controlnet_guess_mode or global_pool_conditions
# 3. Encode input prompt
prompt, negative_prompt = prepare_directional_prompt(prompt, negative_prompt)
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=None,
negative_prompt_embeds=None,
lora_scale=text_encoder_lora_scale,
)
negative_prompt_embeds, prompt_embeds = torch.chunk(prompt_embeds, 2)
prompt_embed_dict = dict(zip(direction_names, [emb for emb in prompt_embeds]))
negative_prompt_embed_dict = dict(zip(direction_names, [emb for emb in negative_prompt_embeds]))
# (4. Prepare image) This pipeline use internal conditional images from Pytorch3D
self.uvp.to(self._execution_device)
conditioning_images, masks = get_conditioning_images(self.uvp, height, cond_type=cond_type)
conditioning_images = conditioning_images.type(prompt_embeds.dtype)
cond = (conditioning_images/2+0.5).permute(0,2,3,1).cpu().numpy()
cond = np.concatenate([img for img in cond], axis=1)
numpy_to_pil(cond)[0].save(f"{self.intermediate_dir}/cond.jpg")
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 6. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
None,
)
latent_tex = self.uvp.set_noise_texture()
noise_views = self.uvp.render_textured_views()
foregrounds = [view[:-1] for view in noise_views]
masks = [view[-1:] for view in noise_views]
composited_tensor = composite_rendered_view(self.scheduler, latents, foregrounds, masks, timesteps[0]+1)
latents = composited_tensor.type(latents.dtype)
self.uvp.to("cpu")
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7.1 Create tensor stating which controlnets to keep
controlnet_keep = []
for i in range(len(timesteps)):
keeps = [
1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
for s, e in zip(control_guidance_start, control_guidance_end)
]
controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
intermediate_results = []
background_colors = [random.choice(list(color_constants.keys())) for i in range(len(self.camera_poses))]
dbres_sizes_list = []
mbres_size_list = []
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# mix prompt embeds according to azim angle
positive_prompt_embeds = [azim_prompt(prompt_embed_dict, pose) for pose in self.camera_poses]
positive_prompt_embeds = torch.stack(positive_prompt_embeds, axis=0)
negative_prompt_embeds = [azim_neg_prompt(negative_prompt_embed_dict, pose) for pose in self.camera_poses]
negative_prompt_embeds = torch.stack(negative_prompt_embeds, axis=0)
# expand the latents if we are doing classifier free guidance
latent_model_input = self.scheduler.scale_model_input(latents, t)
'''
Use groups to manage prompt and results
Make sure negative and positive prompt does not perform attention together
'''
prompt_embeds_groups = {"positive": positive_prompt_embeds}
result_groups = {}
if do_classifier_free_guidance:
prompt_embeds_groups["negative"] = negative_prompt_embeds
for prompt_tag, prompt_embeds in prompt_embeds_groups.items():
if prompt_tag == "positive" or not guess_mode:
# controlnet(s) inference
control_model_input = latent_model_input
controlnet_prompt_embeds = prompt_embeds
if isinstance(controlnet_keep[i], list):
cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
else:
controlnet_cond_scale = controlnet_conditioning_scale
if isinstance(controlnet_cond_scale, list):
controlnet_cond_scale = controlnet_cond_scale[0]
cond_scale = controlnet_cond_scale * controlnet_keep[i]
# Split into micro-batches according to group meta info
# Ignore this feature for now
down_block_res_samples_list = []
mid_block_res_sample_list = []
model_input_batches = [torch.index_select(control_model_input, dim=0, index=torch.tensor(meta[0], device=self._execution_device)) for meta in self.group_metas]
prompt_embeds_batches = [torch.index_select(controlnet_prompt_embeds, dim=0, index=torch.tensor(meta[0], device=self._execution_device)) for meta in self.group_metas]
conditioning_images_batches = [torch.index_select(conditioning_images, dim=0, index=torch.tensor(meta[0], device=self._execution_device)) for meta in self.group_metas]
for model_input_batch ,prompt_embeds_batch, conditioning_images_batch \
in zip (model_input_batches, prompt_embeds_batches, conditioning_images_batches):
down_block_res_samples, mid_block_res_sample = self.controlnet(
model_input_batch,
t,
encoder_hidden_states=prompt_embeds_batch,
controlnet_cond=conditioning_images_batch,
conditioning_scale=cond_scale,
guess_mode=guess_mode,
return_dict=False,
)
down_block_res_samples_list.append(down_block_res_samples)
mid_block_res_sample_list.append(mid_block_res_sample)
''' For the ith element of down_block_res_samples, concat the ith element of all mini-batch result '''
model_input_batches = prompt_embeds_batches = conditioning_images_batches = None
if guess_mode:
for dbres in down_block_res_samples_list:
dbres_sizes = []
for res in dbres:
dbres_sizes.append(res.shape)
dbres_sizes_list.append(dbres_sizes)
for mbres in mid_block_res_sample_list:
mbres_size_list.append(mbres.shape)
else:
# Infered ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# add 0 to the unconditional batch to keep it unchanged.
# We copy the tensor shapes from a conditional batch
down_block_res_samples_list = []
mid_block_res_sample_list = []
for dbres_sizes in dbres_sizes_list:
down_block_res_samples_list.append([torch.zeros(shape, device=self._execution_device, dtype=latents.dtype) for shape in dbres_sizes])
for mbres in mbres_size_list:
mid_block_res_sample_list.append(torch.zeros(mbres, device=self._execution_device, dtype=latents.dtype))
dbres_sizes_list = []
mbres_size_list = []
'''
predict the noise residual, split into mini-batches
Downblock res samples has n samples, we split each sample into m batches
and re group them into m lists of n mini batch samples.
'''
noise_pred_list = []
model_input_batches = [torch.index_select(latent_model_input, dim=0, index=torch.tensor(meta[0], device=self._execution_device)) for meta in self.group_metas]
prompt_embeds_batches = [torch.index_select(prompt_embeds, dim=0, index=torch.tensor(meta[0], device=self._execution_device)) for meta in self.group_metas]
for model_input_batch, prompt_embeds_batch, down_block_res_samples_batch, mid_block_res_sample_batch, meta \
in zip(model_input_batches, prompt_embeds_batches, down_block_res_samples_list, mid_block_res_sample_list, self.group_metas):
if t > num_timesteps * (1- ref_attention_end):
replace_attention_processors(self.unet, SamplewiseAttnProcessor2_0, attention_mask=meta[2], ref_attention_mask=meta[3], ref_weight=1)
else:
replace_attention_processors(self.unet, SamplewiseAttnProcessor2_0, attention_mask=meta[2], ref_attention_mask=meta[3], ref_weight=0)
noise_pred = self.unet(
model_input_batch,
t,
encoder_hidden_states=prompt_embeds_batch,
cross_attention_kwargs=cross_attention_kwargs,
down_block_additional_residuals=down_block_res_samples_batch,
mid_block_additional_residual=mid_block_res_sample_batch,
return_dict=False,
)[0]
noise_pred_list.append(noise_pred)
noise_pred_list = [torch.index_select(noise_pred, dim=0, index=torch.tensor(meta[1], device=self._execution_device)) for noise_pred, meta in zip(noise_pred_list, self.group_metas)]
noise_pred = torch.cat(noise_pred_list, dim=0)
down_block_res_samples_list = None
mid_block_res_sample_list = None
noise_pred_list = None
model_input_batches = prompt_embeds_batches = down_block_res_samples_batches = mid_block_res_sample_batches = None
result_groups[prompt_tag] = noise_pred
positive_noise_pred = result_groups["positive"]
# perform guidance
if do_classifier_free_guidance:
noise_pred = result_groups["negative"] + guidance_scale * (positive_noise_pred - result_groups["negative"])
if do_classifier_free_guidance and guidance_rescale > 0.0:
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
self.uvp.to(self._execution_device)
# compute the previous noisy sample x_t -> x_t-1
# Multi-View step or individual step
if t > (1-multiview_diffusion_end)*num_timesteps: | step_results = step_tex( | 3 | 2023-12-09 03:27:58+00:00 | 16k |
SqueezeBits/owlite | owlite/backend/onnx/export.py | [
{
"identifier": "OwLiteStatus",
"path": "owlite/enums/owlite_status.py",
"snippet": "class OwLiteStatus(Enum):\n \"\"\"The enum for specifying model status about compression with `GraphModule.meta`\n\n Attributes\n NOT_COMPRESSED: The model is symbolic traced, but before inserting quantizer... | import io
import os
import tempfile
import onnx
import onnxsim
import onnxsim.model_checking
import torch
import torch.onnx.errors
from collections.abc import Collection, Mapping, Sequence
from typing import Any, Optional, Union
from onnx import ModelProto
from onnx.shape_inference import infer_shapes, infer_shapes_path
from onnxsim.onnxsim_cpp2py_export import simplify_path
from torch.fx.graph_module import GraphModule
from ...enums import OwLiteStatus
from ...logger import log
from ...nn import FakeQuantizer
from ..fx.transforms import clip_narrow_range_weights, fold_zp_to_bias, fuse_bn
from ..utils import (
get_most_common_device,
get_most_common_floating_point_type,
map_signature,
move_tensors_to,
nodestr,
)
from .dynamize import DynamicDimensions, dynamize
from .model_checking import compare
from .transforms import apply_onnx_transforms | 12,508 | Defaults to `torch._C._onnx.OperatorExportTypes.ONNX`.
* `OperatorExportTypes.ONNX`: Export all ops as regular ONNX ops (in the default opset domain).
* `OperatorExportTypes.ONNX_FALLTHROUGH`: Try to convert all ops
to standard ONNX ops in the default opset domain. If unable to do so
(e.g. because support has not been added to convert a particular torch op to ONNX),
fall back to exporting the op into a custom opset domain without conversion. Applies
to `custom ops <https://pytorch.org/tutorials/advanced/torch_script_custom_ops.html>`_
as well as ATen ops. For the exported model to be usable, the runtime must support
these non-standard ops.
* `OperatorExportTypes.ONNX_ATEN`: All ATen ops (in the TorchScript namespace "aten")
are exported as ATen ops (in opset domain "org.pytorch.aten").
`ATen <https://pytorch.org/cppdocs/#aten>`_ is PyTorch's built-in tensor library, so
this instructs the runtime to use PyTorch's implementation of these ops.
.. warning::
Models exported this way are probably runnable only by Caffe2.
This may be useful if the numeric differences in implementations of operators are
causing large differences in behavior between PyTorch and Caffe2 (which is more
common on untrained models).
* `OperatorExportTypes.ONNX_ATEN_FALLBACK`: Try to export each ATen op
(in the TorchScript namespace "aten") as a regular ONNX op. If we are unable to do so
(e.g. because support has not been added to convert a particular torch op to ONNX),
fall back to exporting an ATen op. See documentation on OperatorExportTypes.ONNX_ATEN for
context.
For example::
graph(%0 : Float):
%3 : int = prim::Constant[value=0]()
# conversion unsupported
%4 : Float = aten::triu(%0, %3)
# conversion supported
%5 : Float = aten::mul(%4, %0)
return (%5)
Assuming `aten::triu` is not supported in ONNX, this will be exported as::
graph(%0 : Float):
%1 : Long() = onnx::Constant[value={0}]()
# not converted
%2 : Float = aten::ATen[operator="triu"](%0, %1)
# converted
%3 : Float = onnx::Mul(%2, %0)
return (%3)
If PyTorch was built with Caffe2 (i.e. with `BUILD_CAFFE2=1`), then
Caffe2-specific behavior will be enabled, including special support
for ops are produced by the modules described in
`Quantization <https://pytorch.org/docs/stable/quantization.html>`_.
.. warning::
Models exported this way are probably runnable only by Caffe2.
opset_version (int, optional): The version of the default (ai.onnx) opset
<https://github.com/onnx/onnx/blob/master/docs/Operators.md> to target. Must be >= 7 and <= 18.
Defaults to 17.
do_constant_folding (bool, optional): Apply the constant-folding optimization.
Constant-folding will replace some of the ops that have all constant inputs
with pre-computed constant nodes. Defaults to True.
keep_initializers_as_inputs (Optional[bool], optional): If True, all the initializers
(typically corresponding to parameters) in the exported graph will also be added
as inputs to the graph. If False, then initializers are not added as inputs to the
graph, and only the non-parameter inputs are added as inputs. This may allow for
better optimizations (e.g. constant folding) by backends/runtimes. Defaults to None.
custom_opsets (Optional[Mapping[str, int]], optional): A dict with schema:
* KEY (str): opset domain name
* VALUE (int): opset version
If a custom opset is referenced by ``model`` but not mentioned in this dictionary,
the opset version is set to 1. Only custom opset domain name and version should be
indicated through this argument. Defaults to None.
export_modules_as_functions (Union[bool, Collection[type[torch.nn.Module]]], optional): Flag to enable
exporting all ``nn.Module`` forward calls as local functions in ONNX. Or a set to indicate the
particular types of modules to export as local functions in ONNX.
This feature requires ``opset_version`` >= 15, otherwise the export will fail. This is because
``opset_version`` < 15 implies IR version < 8, which means no local function support.
Module variables will be exported as function attributes. There are two categories of function
attributes. Defaults to False.
use_fast_export (bool, optional): If True, export process will be done in memory. If `module` with total
parameter size larger than 2GB, this flag will be automatically set to `False`. If False, temporary
export process will be done using temporary files. Defaults to True.
apply_transforms (bool, optional): If True, ONNX transforms defined by SqueezeBits.inc will be applied for
model optimization. If False, ONNX transformations will be skipped. However, turning this flag to `False`
is experimental and might yield unexpected behavior. Defaults to True.
simplify (bool, optional): If True, onnx-simplifier will be run. If False, onnx-simplifier will be skipped.
Defaults to True.
check_n (int, optional): Only available when `simplify=True`. The number of times to run check for the
simplified ONNX proto after onnx-simplifier. Defaults to 1.
skip_fuse_bn (bool, optional): Only available when `simplify=True`. Whether to skip batchnorm-fusion.
Defaults to False.
skipped_optimizers (Optional[list[str]], optional): Only available when `simplify=True`. The list of
onnx-simplifier passes to skip. Defaults to None.
See https://github.com/onnx/optimizer/tree/master/onnxoptimizer/passes for available passes.
dynamic_dimensions (Optional[DynamicDimensions], optional): Dynamic dimensions setting configured by
`configure_dynamic_dimensions`. Defaults to None.
Raises:
TypeError: If `f` is not a string.
ValueError: If the quantizer has invalid condition.
`torch.onnx.errors.CheckerError`: If the ONNX checker detects an invalid ONNX graph.
`torch.onnx.errors.UnsupportedOperatorError`: If the ONNX graph cannot be exported because it
uses an operator that is not supported by the exporter.
`torch.onnx.errors.OnnxExporterError`: Other errors that can occur during export.
All errors are subclasses of :class:`errors.OnnxExporterError`.
"""
if not isinstance(f, str):
raise TypeError("owlite.onnx.export requires the argument `f` to be a string.")
if isinstance(module, GraphModule):
if module.meta["owlite_status"] == OwLiteStatus.COMPRESSED:
log.warning(
"This module has not yet been calibrated. "
"The onnx that comes out of this module may have unexpected results in accuracy and latency."
)
clip_narrow_range_weights(module)
# Batch Norm Fusing
| """An alternative for torch.onnx.export with extra optimizations"""
# pylint: disable=protected-access
# pylint: disable=no-name-in-module
# Large models (e.g. SwinTransformer) requires
# more than 50 (default) onnxsim iterations
os.environ["ONNXSIM_FIXED_POINT_ITERS"] = "100"
# pylint: disable=too-many-arguments, too-many-locals, invalid-name
def export(
module: torch.nn.Module,
args: Union[tuple[Any, ...], torch.Tensor],
f: str,
export_params: bool = True,
verbose: bool = False,
training: torch._C._onnx.TrainingMode = torch._C._onnx.TrainingMode.EVAL,
input_names: Optional[Sequence[str]] = None,
output_names: Optional[Sequence[str]] = None,
operator_export_type: torch._C._onnx.OperatorExportTypes = torch._C._onnx.OperatorExportTypes.ONNX,
opset_version: int = 17,
do_constant_folding: bool = True,
keep_initializers_as_inputs: Optional[bool] = None,
custom_opsets: Optional[Mapping[str, int]] = None,
export_modules_as_functions: Union[bool, Collection[type[torch.nn.Module]]] = False,
use_fast_export: bool = True,
apply_transforms: bool = True,
simplify: bool = True,
check_n: int = 1,
skip_fuse_bn: bool = False,
skipped_optimizers: Optional[list[str]] = None,
dynamic_dimensions: Optional[DynamicDimensions] = None,
) -> None:
r"""Exports a model into ONNX format.
Args:
module (torch.nn.Module): The model to be exported.
args (Union[tuple[Any, ...], torch.Tensor]): Argument of a `module`.
args can be structured either as:
1. ONLY A TUPLE OF ARGUMENTS::
args = (x, y, z)
The tuple should contain model inputs such that `module(*args)` is a valid
invocation of the model. Any non-Tensor arguments will be hard-coded into the
exported model; any Tensor arguments will become inputs of the exported model,
in the order they occur in the tuple.
2. A TENSOR::
args = torch.Tensor([1])
This is equivalent to a 1-ary tuple of that Tensor.
3. A TUPLE OF ARGUMENTS ENDING WITH A DICTIONARY OF NAMED ARGUMENTS::
args = (
x,
{
"y": input_y,
"z": input_z
}
)
All but the last element of the tuple will be passed as non-keyword arguments,
and named arguments will be set from the last element. If a named argument is
not present in the dictionary, it is assigned the default value, or None if a
default value is not provided.
.. note::
If a dictionary is the last element of the args tuple, it will be
interpreted as containing named arguments. In order to pass a dict as the
last non-keyword arg, provide an empty dict as the last element of the args
tuple. For example, instead of::
export(
module,
(
x,
# WRONG: will be interpreted as named arguments
{y: z}
),
"test.onnx.pb"
)
Write::
export(
module,
(
x,
{y: z},
{}
),
"test.onnx.pb"
)
f (str): A string containing a file name. A binary protocol buffer will be written to this file.
export_params (bool, optional): If True, all parameters will
be exported. Set this to False if you want to export an untrained model.
In this case, the exported model will first take all of its parameters
as arguments, with the ordering as specified by `module.state_dict().values()`. Defaults to True.
verbose (bool, optional): If True, prints a description of the
model being exported to stdout. In addition, the final ONNX graph will include the
field `doc_string` from the exported model which mentions the source code locations
for `module`. If True, ONNX exporter logging will be turned on. Defaults to False.
training (torch._C._onnx.TrainingMode, optional): Defaults to torch._C._onnx.TrainingMode.EVAL.
* `TrainingMode.EVAL`: export the model in inference mode.
* `TrainingMode.PRESERVE`: export the model in inference mode if model.training is
False and in training mode if model.training is True.
* `TrainingMode.TRAINING`: export the model in training mode. Disables optimizations
which might interfere with training.
input_names (Optional[Sequence[str]], optional): Names to assign to the input nodes of the graph, in order.
Names of `module.forward` arguments will be used when None is given. Defaults to None.
output_names (Optional[Sequence[str]], optional): Names to assign to the output nodes of the graph, in order.
Defaults to None.
operator_export_type (torch._C._onnx.OperatorExportTypes, optional):
Defaults to `torch._C._onnx.OperatorExportTypes.ONNX`.
* `OperatorExportTypes.ONNX`: Export all ops as regular ONNX ops (in the default opset domain).
* `OperatorExportTypes.ONNX_FALLTHROUGH`: Try to convert all ops
to standard ONNX ops in the default opset domain. If unable to do so
(e.g. because support has not been added to convert a particular torch op to ONNX),
fall back to exporting the op into a custom opset domain without conversion. Applies
to `custom ops <https://pytorch.org/tutorials/advanced/torch_script_custom_ops.html>`_
as well as ATen ops. For the exported model to be usable, the runtime must support
these non-standard ops.
* `OperatorExportTypes.ONNX_ATEN`: All ATen ops (in the TorchScript namespace "aten")
are exported as ATen ops (in opset domain "org.pytorch.aten").
`ATen <https://pytorch.org/cppdocs/#aten>`_ is PyTorch's built-in tensor library, so
this instructs the runtime to use PyTorch's implementation of these ops.
.. warning::
Models exported this way are probably runnable only by Caffe2.
This may be useful if the numeric differences in implementations of operators are
causing large differences in behavior between PyTorch and Caffe2 (which is more
common on untrained models).
* `OperatorExportTypes.ONNX_ATEN_FALLBACK`: Try to export each ATen op
(in the TorchScript namespace "aten") as a regular ONNX op. If we are unable to do so
(e.g. because support has not been added to convert a particular torch op to ONNX),
fall back to exporting an ATen op. See documentation on OperatorExportTypes.ONNX_ATEN for
context.
For example::
graph(%0 : Float):
%3 : int = prim::Constant[value=0]()
# conversion unsupported
%4 : Float = aten::triu(%0, %3)
# conversion supported
%5 : Float = aten::mul(%4, %0)
return (%5)
Assuming `aten::triu` is not supported in ONNX, this will be exported as::
graph(%0 : Float):
%1 : Long() = onnx::Constant[value={0}]()
# not converted
%2 : Float = aten::ATen[operator="triu"](%0, %1)
# converted
%3 : Float = onnx::Mul(%2, %0)
return (%3)
If PyTorch was built with Caffe2 (i.e. with `BUILD_CAFFE2=1`), then
Caffe2-specific behavior will be enabled, including special support
for ops are produced by the modules described in
`Quantization <https://pytorch.org/docs/stable/quantization.html>`_.
.. warning::
Models exported this way are probably runnable only by Caffe2.
opset_version (int, optional): The version of the default (ai.onnx) opset
<https://github.com/onnx/onnx/blob/master/docs/Operators.md> to target. Must be >= 7 and <= 18.
Defaults to 17.
do_constant_folding (bool, optional): Apply the constant-folding optimization.
Constant-folding will replace some of the ops that have all constant inputs
with pre-computed constant nodes. Defaults to True.
keep_initializers_as_inputs (Optional[bool], optional): If True, all the initializers
(typically corresponding to parameters) in the exported graph will also be added
as inputs to the graph. If False, then initializers are not added as inputs to the
graph, and only the non-parameter inputs are added as inputs. This may allow for
better optimizations (e.g. constant folding) by backends/runtimes. Defaults to None.
custom_opsets (Optional[Mapping[str, int]], optional): A dict with schema:
* KEY (str): opset domain name
* VALUE (int): opset version
If a custom opset is referenced by ``model`` but not mentioned in this dictionary,
the opset version is set to 1. Only custom opset domain name and version should be
indicated through this argument. Defaults to None.
export_modules_as_functions (Union[bool, Collection[type[torch.nn.Module]]], optional): Flag to enable
exporting all ``nn.Module`` forward calls as local functions in ONNX. Or a set to indicate the
particular types of modules to export as local functions in ONNX.
This feature requires ``opset_version`` >= 15, otherwise the export will fail. This is because
``opset_version`` < 15 implies IR version < 8, which means no local function support.
Module variables will be exported as function attributes. There are two categories of function
attributes. Defaults to False.
use_fast_export (bool, optional): If True, export process will be done in memory. If `module` with total
parameter size larger than 2GB, this flag will be automatically set to `False`. If False, temporary
export process will be done using temporary files. Defaults to True.
apply_transforms (bool, optional): If True, ONNX transforms defined by SqueezeBits.inc will be applied for
model optimization. If False, ONNX transformations will be skipped. However, turning this flag to `False`
is experimental and might yield unexpected behavior. Defaults to True.
simplify (bool, optional): If True, onnx-simplifier will be run. If False, onnx-simplifier will be skipped.
Defaults to True.
check_n (int, optional): Only available when `simplify=True`. The number of times to run check for the
simplified ONNX proto after onnx-simplifier. Defaults to 1.
skip_fuse_bn (bool, optional): Only available when `simplify=True`. Whether to skip batchnorm-fusion.
Defaults to False.
skipped_optimizers (Optional[list[str]], optional): Only available when `simplify=True`. The list of
onnx-simplifier passes to skip. Defaults to None.
See https://github.com/onnx/optimizer/tree/master/onnxoptimizer/passes for available passes.
dynamic_dimensions (Optional[DynamicDimensions], optional): Dynamic dimensions setting configured by
`configure_dynamic_dimensions`. Defaults to None.
Raises:
TypeError: If `f` is not a string.
ValueError: If the quantizer has invalid condition.
`torch.onnx.errors.CheckerError`: If the ONNX checker detects an invalid ONNX graph.
`torch.onnx.errors.UnsupportedOperatorError`: If the ONNX graph cannot be exported because it
uses an operator that is not supported by the exporter.
`torch.onnx.errors.OnnxExporterError`: Other errors that can occur during export.
All errors are subclasses of :class:`errors.OnnxExporterError`.
"""
if not isinstance(f, str):
raise TypeError("owlite.onnx.export requires the argument `f` to be a string.")
if isinstance(module, GraphModule):
if module.meta["owlite_status"] == OwLiteStatus.COMPRESSED:
log.warning(
"This module has not yet been calibrated. "
"The onnx that comes out of this module may have unexpected results in accuracy and latency."
)
clip_narrow_range_weights(module)
# Batch Norm Fusing | fuse_bn(module) | 5 | 2023-12-08 06:41:50+00:00 | 16k |
qitan/devops-backend-lite | apps/ucenter/views.py | [
{
"identifier": "FEISHU_SYNC_USER_JOB_CACHE_KEY",
"path": "common/variables.py",
"snippet": "FEISHU_SYNC_USER_JOB_CACHE_KEY = 'celery_job:feishu_user_sync'"
},
{
"identifier": "Menu",
"path": "dbapp/models.py",
"snippet": ""
},
{
"identifier": "CustomModelViewSet",
"path": "c... | import hashlib
import django_filters
import datetime
import time
import shortuuid
import json
import logging
from django.core.cache import cache
from rest_framework import viewsets, status
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework.decorators import action
from rest_framework import pagination
from rest_framework_simplejwt.views import TokenObtainPairView, TokenRefreshView
from rest_framework_simplejwt.exceptions import TokenError, InvalidToken
from rest_framework_simplejwt.authentication import JWTAuthentication
from rest_framework_simplejwt.tokens import RefreshToken, Token, OutstandingToken
from rest_framework.filters import SearchFilter, OrderingFilter
from django_q.tasks import async_task, result
from django.contrib.auth.models import update_last_login
from django.db.models import Q
from django.contrib.auth import logout
from common.variables import FEISHU_SYNC_USER_JOB_CACHE_KEY
from dbapp.models import Menu, Permission, Role, Organization, UserProfile, AuditLog, SystemConfig, DataDict
from ucenter.serializers import MenuSerializers, MenuListSerializers, PermissionListSerializers, PermissionSerializers, \
RoleListSerializers, \
RoleSerializers, OrganizationSerializers, \
UserProfileListSerializers, UserProfileSerializers, UserProfileDetailSerializers, AuditLogSerializers, \
AuditLogActivitySerializers, SystemConfigSerializers, \
SystemConfigListSerializers, DataDictSerializers
from common.extends.viewsets import CustomModelViewSet, CustomModelParentViewSet
from common.extends.permissions import RbacPermission
from common.extends.JwtAuth import CustomInvalidToken, TokenObtainPairSerializer, TokenRefreshSerializer
from common.extends.handler import log_audit
from common.extends.filters import AuditLogFilter, CustomSearchFilter
from common.utils.JenkinsAPI import GlueJenkins
from common.get_ip import user_ip
from common.ext_fun import ThirdPartyUser, set_redis_data, get_redis_data, timeline_generate, time_period, \
node_filter
from qtasks.tasks import test_notify
from django.conf import settings
from django.contrib.auth import login, REDIRECT_FIELD_NAME
from django.views.decorators.csrf import csrf_exempt, csrf_protect
from django.views.decorators.cache import never_cache | 10,818 | def perform_update(self, serializer):
serializer.save()
cache.delete(f"datadict:{serializer.data['key']}:0")
cache.delete(f"datadict:{serializer.data['key']}:1")
@action(methods=['GET'], url_path='user', detail=False)
def get_user(self, request):
"""
获取用户列表
### 传递参数
force: 0|1
force为1时强制刷新
"""
_force = request.query_params.get('force', None)
position = request.query_params.get('position', None)
_key = str(
f'project:users:{self.request.user.id}-{self.request.query_params}')
try:
data = cache.get(_key)
except BaseException as e:
cache.delete(_key)
data = None
if not data or _force:
if position:
users = UserProfile.objects.exclude(
username='thirdparty').filter(position=position)
else:
users = UserProfile.objects.exclude(username='thirdparty')
data = [{'id': i.id, 'first_name': i.first_name, 'username': i.username, 'name': i.name, 'title': i.title,
'position': i.position} for i in users]
cache.set(_key, data, timeout=60 * 60 * 24)
return Response({'code': 20000, 'data': data})
@action(methods=['GET'], url_path='extra', detail=False)
def get_by_key(self, request):
"""
通过指定key名获取
参数: key
"""
key_name = request.query_params.get('key', None)
instance = self.queryset.get(key=key_name)
serializer = self.get_serializer(instance)
data = {'data': serializer.data, 'code': 20000, 'status': 'success'}
return Response(data)
class AuditLogViewSet(CustomModelViewSet):
"""
审计日志视图
### 审计日志权限
{'get': ('audit_list', '查看审计日志')}
"""
perms_map = (
{'*': ('admin', '管理员')},
{'get': ('audit_list', '查看审计日志')}
)
queryset = AuditLog.objects.all()
serializer_class = AuditLogSerializers
filter_backends = (django_filters.rest_framework.DjangoFilterBackend,
CustomSearchFilter, OrderingFilter)
filter_class = AuditLogFilter
filter_fields = ('user', 'type', 'action', 'action_ip', 'operator')
search_fields = ('user', 'type', 'action', 'action_ip', 'content')
def create(self, request, *args, **kwargs):
pass
def update(self, request, *args, **kwargs):
pass
def destroy(self, request, *args, **kwargs):
pass
class MenuViewSet(CustomModelParentViewSet):
"""
菜单视图
### 菜单权限
{'*': ('menu_all', '菜单管理')},
{'get': ('menu_list', '查看菜单')},
{'post': ('menu_create', '创建菜单')},
{'put': ('menu_edit', '编辑菜单')},
{'patch': ('menu_edit', '编辑菜单')},
{'delete': ('menu_delete', '删除菜单')}
"""
perms_map = (
{'*': ('admin', '管理员')},
{'*': ('menu_all', '菜单管理')},
{'get': ('menu_list', '查看菜单')},
{'post': ('menu_create', '创建菜单')},
{'put': ('menu_edit', '编辑菜单')},
{'patch': ('menu_edit', '编辑菜单')},
{'delete': ('menu_delete', '删除菜单')}
)
queryset = Menu.objects.all()
serializer_class = MenuSerializers
def get_serializer_class(self):
if self.action in ['list', 'retrieve']:
return MenuListSerializers
return MenuSerializers
class PermissionViewSet(CustomModelParentViewSet):
"""
权限视图
### 查看权限列表的权限
{'*': ('perm_all', '权限管理')},
{'get': ('perm_list', '查看权限')},
"""
perms_map = (
{'*': ('admin', '管理员')},
{'*': ('perm_all', '权限管理')},
{'get': ('perm_list', '查看权限')}
)
| #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author : Charles Lai
@Contact : qqing_lai@hotmail.com
@Time : 2020/9/15 下午4:08
@FileName: views.py
@Blog ：https://imaojia.com
"""
logger = logging.getLogger('drf')
DEFAULT_SESSION_TIMEOUT = None
class DataDictViewSet(CustomModelParentViewSet):
"""
数据字典视图
### 数据字典权限
{'*': ('data_all', '数据字典管理')},
{'get': ('data_list', '查看数据字典')},
{'post': ('data_create', '创建数据字典')},
{'put': ('data_edit', '编辑数据字典')},
{'patch': ('data_edit', '编辑数据字典')},
{'delete': ('data_delete', '删除数据字典')}
"""
perms_map = (
{'*': ('admin', '管理员')},
{'*': ('data_all', '数据字典管理')},
{'get': ('data_list', '查看数据字典')},
{'post': ('data_create', '创建数据字典')},
{'put': ('data_edit', '编辑数据字典')},
{'patch': ('data_edit', '编辑数据字典')},
{'delete': ('data_delete', '删除数据字典')}
)
queryset = DataDict.objects.all()
serializer_class = DataDictSerializers
filter_backends = (
django_filters.rest_framework.DjangoFilterBackend, SearchFilter, OrderingFilter)
filter_fields = ('key', 'value')
search_fields = ('key', 'value')
def perform_update(self, serializer):
serializer.save()
cache.delete(f"datadict:{serializer.data['key']}:0")
cache.delete(f"datadict:{serializer.data['key']}:1")
@action(methods=['GET'], url_path='user', detail=False)
def get_user(self, request):
"""
获取用户列表
### 传递参数
force: 0|1
force为1时强制刷新
"""
_force = request.query_params.get('force', None)
position = request.query_params.get('position', None)
_key = str(
f'project:users:{self.request.user.id}-{self.request.query_params}')
try:
data = cache.get(_key)
except BaseException as e:
cache.delete(_key)
data = None
if not data or _force:
if position:
users = UserProfile.objects.exclude(
username='thirdparty').filter(position=position)
else:
users = UserProfile.objects.exclude(username='thirdparty')
data = [{'id': i.id, 'first_name': i.first_name, 'username': i.username, 'name': i.name, 'title': i.title,
'position': i.position} for i in users]
cache.set(_key, data, timeout=60 * 60 * 24)
return Response({'code': 20000, 'data': data})
@action(methods=['GET'], url_path='extra', detail=False)
def get_by_key(self, request):
"""
通过指定key名获取
参数: key
"""
key_name = request.query_params.get('key', None)
instance = self.queryset.get(key=key_name)
serializer = self.get_serializer(instance)
data = {'data': serializer.data, 'code': 20000, 'status': 'success'}
return Response(data)
class AuditLogViewSet(CustomModelViewSet):
"""
审计日志视图
### 审计日志权限
{'get': ('audit_list', '查看审计日志')}
"""
perms_map = (
{'*': ('admin', '管理员')},
{'get': ('audit_list', '查看审计日志')}
)
queryset = AuditLog.objects.all()
serializer_class = AuditLogSerializers
filter_backends = (django_filters.rest_framework.DjangoFilterBackend,
CustomSearchFilter, OrderingFilter)
filter_class = AuditLogFilter
filter_fields = ('user', 'type', 'action', 'action_ip', 'operator')
search_fields = ('user', 'type', 'action', 'action_ip', 'content')
def create(self, request, *args, **kwargs):
pass
def update(self, request, *args, **kwargs):
pass
def destroy(self, request, *args, **kwargs):
pass
class MenuViewSet(CustomModelParentViewSet):
"""
菜单视图
### 菜单权限
{'*': ('menu_all', '菜单管理')},
{'get': ('menu_list', '查看菜单')},
{'post': ('menu_create', '创建菜单')},
{'put': ('menu_edit', '编辑菜单')},
{'patch': ('menu_edit', '编辑菜单')},
{'delete': ('menu_delete', '删除菜单')}
"""
perms_map = (
{'*': ('admin', '管理员')},
{'*': ('menu_all', '菜单管理')},
{'get': ('menu_list', '查看菜单')},
{'post': ('menu_create', '创建菜单')},
{'put': ('menu_edit', '编辑菜单')},
{'patch': ('menu_edit', '编辑菜单')},
{'delete': ('menu_delete', '删除菜单')}
)
queryset = Menu.objects.all()
serializer_class = MenuSerializers
def get_serializer_class(self):
if self.action in ['list', 'retrieve']:
return MenuListSerializers
return MenuSerializers
class PermissionViewSet(CustomModelParentViewSet):
"""
权限视图
### 查看权限列表的权限
{'*': ('perm_all', '权限管理')},
{'get': ('perm_list', '查看权限')},
"""
perms_map = (
{'*': ('admin', '管理员')},
{'*': ('perm_all', '权限管理')},
{'get': ('perm_list', '查看权限')}
) | queryset = Permission.objects.all() | 1 | 2023-12-13 03:09:32+00:00 | 16k |
liujin112/PortraitDiffusion | app.py | [
{
"identifier": "AttentionBase",
"path": "utils/masactrl_utils.py",
"snippet": "class AttentionBase:\n def __init__(self):\n self.cur_step = 0\n self.num_att_layers = -1\n self.cur_att_layer = 0\n\n def after_step(self):\n pass\n\n def __call__(self, q, k, v, sim, at... | import os
import torch
import random
import numpy as np
import gradio as gr
import torch.nn.functional as F
from glob import glob
from datetime import datetime
from diffusers import StableDiffusionPipeline
from diffusers import DDIMScheduler, LCMScheduler
from PIL import Image,ImageDraw
from utils.masactrl_utils import (AttentionBase,
regiter_attention_editor_diffusers)
from utils.free_lunch_utils import register_upblock2d,register_crossattn_upblock2d,register_free_upblock2d, register_free_crossattn_upblock2d
from utils.style_attn_control import MaskPromptedStyleAttentionControl
from utils.pipeline import MasaCtrlPipeline
from torchvision.utils import save_image
from segment_anything import sam_model_registry, SamPredictor | 12,178 | return None
else:
base_model = self.personalized_model_list[base_model_dropdown]
mid_model = StableDiffusionPipeline.from_single_file(base_model)
self.pipeline.vae = mid_model.vae
self.pipeline.unet = mid_model.unet
self.pipeline.text_encoder = mid_model.text_encoder
self.pipeline.to(self.device)
self.personal_model_loaded = base_model_dropdown.split('.')[0]
print(f'load {base_model_dropdown} model success!')
return gr.Dropdown()
def update_lora_model(self, lora_model_dropdown,lora_alpha_slider):
if self.pipeline is None:
gr.Info(f"Please select a pretrained model path.")
return None
else:
if lora_model_dropdown == "none":
self.pipeline.unfuse_lora()
self.pipeline.unload_lora_weights()
self.lora_loaded = None
print("Restore lora.")
else:
lora_model_path = self.lora_model_list[lora_model_dropdown]
self.pipeline.load_lora_weights(lora_model_path)
self.pipeline.fuse_lora(lora_alpha_slider)
self.lora_loaded = lora_model_dropdown.split('.')[0]
print(f'load {lora_model_dropdown} LoRA Model Success!')
return gr.Dropdown()
def load_lcm_lora(self, lora_alpha_slider=1.0):
# set scheduler
self.pipeline = MasaCtrlPipeline.from_pretrained(self.stable_diffusion_list[0]).to(self.device)
self.pipeline.scheduler = LCMScheduler.from_config(self.pipeline.scheduler.config)
# load LCM-LoRA
self.pipeline.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
self.pipeline.fuse_lora(lora_alpha_slider)
self.lcm_lora_loaded = True
print(f'load LCM-LoRA model success!')
def generate(self, source, style, source_mask, style_mask,
start_step, start_layer, Style_attn_step,
Method, Style_Guidance, ddim_steps, scale, seed, de_bug,
target_prompt, negative_prompt_textbox,
inter_latents,
freeu, b1, b2, s1, s2,
width_slider,height_slider,
):
os.makedirs(self.savedir, exist_ok=True)
os.makedirs(self.savedir_sample, exist_ok=True)
os.makedirs(self.savedir_mask, exist_ok=True)
model = self.pipeline
if seed != -1 and seed != "": torch.manual_seed(int(seed))
else: torch.seed()
seed = torch.initial_seed()
sample_count = len(os.listdir(self.savedir_sample))
os.makedirs(os.path.join(self.savedir_mask, f"results_{sample_count}"), exist_ok=True)
# ref_prompt = [source_prompt, target_prompt]
# prompts = ref_prompt+['']
ref_prompt = [target_prompt, target_prompt]
prompts = ref_prompt+[target_prompt]
source_image,style_image,source_mask,style_mask = load_mask_images(source,style,source_mask,style_mask,self.device,width_slider,height_slider,out_dir=os.path.join(self.savedir_mask, f"results_{sample_count}"))
# global START_CODE, LATENTS_LIST
with torch.no_grad():
#import pdb;pdb.set_trace()
#prev_source
if self.start_code is None and self.latents_list is None:
content_style = torch.cat([style_image, source_image], dim=0)
editor = AttentionBase()
regiter_attention_editor_diffusers(model, editor)
st_code, latents_list = model.invert(content_style,
ref_prompt,
guidance_scale=scale,
num_inference_steps=ddim_steps,
return_intermediates=True)
start_code = torch.cat([st_code, st_code[1:]], dim=0)
self.start_code = start_code
self.latents_list = latents_list
else:
start_code = self.start_code
latents_list = self.latents_list
print('------------------------------------------ Use previous latents ------------------------------------------ ')
#["Without mask", "Only masked region", "Seperate Background Foreground"]
if Method == "Without mask":
style_mask = None
source_mask = None
only_masked_region = False
elif Method == "Only masked region":
assert style_mask is not None and source_mask is not None
only_masked_region = True
else:
assert style_mask is not None and source_mask is not None
only_masked_region = False
controller = MaskPromptedStyleAttentionControl(start_step, start_layer,
style_attn_step=Style_attn_step,
style_guidance=Style_Guidance,
style_mask=style_mask,
source_mask=source_mask,
only_masked_region=only_masked_region,
guidance=scale,
de_bug=de_bug,
)
if freeu:
# model.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)
print(f'++++++++++++++++++ Run with FreeU {b1}_{b2}_{s1}_{s2} ++++++++++++++++')
if Method != "Without mask":
register_free_upblock2d(model, b1=b1, b2=b2, s1=s1, s2=s1,source_mask=source_mask)
|
css = """
.toolbutton {
margin-buttom: 0em 0em 0em 0em;
max-width: 2.5em;
min-width: 2.5em !important;
height: 2.5em;
}
"""
class GlobalText:
def __init__(self):
# config dirs
self.basedir = os.getcwd()
self.stable_diffusion_dir = os.path.join(self.basedir, "models", "StableDiffusion")
self.personalized_model_dir = './models/Stable-diffusion'
self.lora_model_dir = './models/Lora'
self.savedir = os.path.join(self.basedir, "samples", datetime.now().strftime("Gradio-%Y-%m-%dT%H-%M-%S"))
self.savedir_sample = os.path.join(self.savedir, "sample")
self.savedir_mask = os.path.join(self.savedir, "mask")
self.stable_diffusion_list = ["runwayml/stable-diffusion-v1-5",
"latent-consistency/lcm-lora-sdv1-5"]
self.personalized_model_list = []
self.lora_model_list = []
# config models
self.tokenizer = None
self.text_encoder = None
self.vae = None
self.unet = None
self.pipeline = None
self.lora_loaded = None
self.lcm_lora_loaded = False
self.personal_model_loaded = None
self.sam_predictor = None
self.lora_model_state_dict = {}
self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# self.refresh_stable_diffusion()
self.refresh_personalized_model()
self.reset_start_code()
def load_base_pipeline(self, model_path):
print(f'loading {model_path} model')
scheduler = DDIMScheduler.from_pretrained(model_path,subfolder="scheduler")
self.pipeline = MasaCtrlPipeline.from_pretrained(model_path,
scheduler=scheduler).to(self.device)
def refresh_stable_diffusion(self):
self.load_base_pipeline(self.stable_diffusion_list[0])
self.lora_loaded = None
self.personal_model_loaded = None
self.lcm_lora_loaded = False
return self.stable_diffusion_list[0]
def refresh_personalized_model(self):
personalized_model_list = glob(os.path.join(self.personalized_model_dir, "**/*.safetensors"), recursive=True)
self.personalized_model_list = {os.path.basename(file): file for file in personalized_model_list}
lora_model_list = glob(os.path.join(self.lora_model_dir, "**/*.safetensors"), recursive=True)
self.lora_model_list = {os.path.basename(file): file for file in lora_model_list}
def update_stable_diffusion(self, stable_diffusion_dropdown):
if stable_diffusion_dropdown == 'latent-consistency/lcm-lora-sdv1-5':
self.load_lcm_lora()
else:
self.load_base_pipeline(stable_diffusion_dropdown)
self.lora_loaded = None
self.personal_model_loaded = None
return gr.Dropdown()
def update_base_model(self, base_model_dropdown):
if self.pipeline is None:
gr.Info(f"Please select a pretrained model path.")
return None
else:
base_model = self.personalized_model_list[base_model_dropdown]
mid_model = StableDiffusionPipeline.from_single_file(base_model)
self.pipeline.vae = mid_model.vae
self.pipeline.unet = mid_model.unet
self.pipeline.text_encoder = mid_model.text_encoder
self.pipeline.to(self.device)
self.personal_model_loaded = base_model_dropdown.split('.')[0]
print(f'load {base_model_dropdown} model success!')
return gr.Dropdown()
def update_lora_model(self, lora_model_dropdown,lora_alpha_slider):
if self.pipeline is None:
gr.Info(f"Please select a pretrained model path.")
return None
else:
if lora_model_dropdown == "none":
self.pipeline.unfuse_lora()
self.pipeline.unload_lora_weights()
self.lora_loaded = None
print("Restore lora.")
else:
lora_model_path = self.lora_model_list[lora_model_dropdown]
self.pipeline.load_lora_weights(lora_model_path)
self.pipeline.fuse_lora(lora_alpha_slider)
self.lora_loaded = lora_model_dropdown.split('.')[0]
print(f'load {lora_model_dropdown} LoRA Model Success!')
return gr.Dropdown()
def load_lcm_lora(self, lora_alpha_slider=1.0):
# set scheduler
self.pipeline = MasaCtrlPipeline.from_pretrained(self.stable_diffusion_list[0]).to(self.device)
self.pipeline.scheduler = LCMScheduler.from_config(self.pipeline.scheduler.config)
# load LCM-LoRA
self.pipeline.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
self.pipeline.fuse_lora(lora_alpha_slider)
self.lcm_lora_loaded = True
print(f'load LCM-LoRA model success!')
def generate(self, source, style, source_mask, style_mask,
start_step, start_layer, Style_attn_step,
Method, Style_Guidance, ddim_steps, scale, seed, de_bug,
target_prompt, negative_prompt_textbox,
inter_latents,
freeu, b1, b2, s1, s2,
width_slider,height_slider,
):
os.makedirs(self.savedir, exist_ok=True)
os.makedirs(self.savedir_sample, exist_ok=True)
os.makedirs(self.savedir_mask, exist_ok=True)
model = self.pipeline
if seed != -1 and seed != "": torch.manual_seed(int(seed))
else: torch.seed()
seed = torch.initial_seed()
sample_count = len(os.listdir(self.savedir_sample))
os.makedirs(os.path.join(self.savedir_mask, f"results_{sample_count}"), exist_ok=True)
# ref_prompt = [source_prompt, target_prompt]
# prompts = ref_prompt+['']
ref_prompt = [target_prompt, target_prompt]
prompts = ref_prompt+[target_prompt]
source_image,style_image,source_mask,style_mask = load_mask_images(source,style,source_mask,style_mask,self.device,width_slider,height_slider,out_dir=os.path.join(self.savedir_mask, f"results_{sample_count}"))
# global START_CODE, LATENTS_LIST
with torch.no_grad():
#import pdb;pdb.set_trace()
#prev_source
if self.start_code is None and self.latents_list is None:
content_style = torch.cat([style_image, source_image], dim=0)
editor = AttentionBase()
regiter_attention_editor_diffusers(model, editor)
st_code, latents_list = model.invert(content_style,
ref_prompt,
guidance_scale=scale,
num_inference_steps=ddim_steps,
return_intermediates=True)
start_code = torch.cat([st_code, st_code[1:]], dim=0)
self.start_code = start_code
self.latents_list = latents_list
else:
start_code = self.start_code
latents_list = self.latents_list
print('------------------------------------------ Use previous latents ------------------------------------------ ')
#["Without mask", "Only masked region", "Seperate Background Foreground"]
if Method == "Without mask":
style_mask = None
source_mask = None
only_masked_region = False
elif Method == "Only masked region":
assert style_mask is not None and source_mask is not None
only_masked_region = True
else:
assert style_mask is not None and source_mask is not None
only_masked_region = False
controller = MaskPromptedStyleAttentionControl(start_step, start_layer,
style_attn_step=Style_attn_step,
style_guidance=Style_Guidance,
style_mask=style_mask,
source_mask=source_mask,
only_masked_region=only_masked_region,
guidance=scale,
de_bug=de_bug,
)
if freeu:
# model.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)
print(f'++++++++++++++++++ Run with FreeU {b1}_{b2}_{s1}_{s2} ++++++++++++++++')
if Method != "Without mask":
register_free_upblock2d(model, b1=b1, b2=b2, s1=s1, s2=s1,source_mask=source_mask) | register_free_crossattn_upblock2d(model, b1=b1, b2=b2, s1=s1, s2=s1,source_mask=source_mask) | 5 | 2023-12-06 01:18:39+00:00 | 16k |
AsuradaYuci/TF-CLIP | datasets/make_dataloader_clipreid.py | [
{
"identifier": "VideoDataset",
"path": "datasets/video_loader_xh.py",
"snippet": "class VideoDataset(Dataset):\n \"\"\"Video Person ReID Dataset.\n Note batch data has shape (batch, seq_len, channel, height, width).\n \"\"\"\n sample_methods = ['evenly', 'random', 'dense']\n\n def __init... | import torch
import utils.spatial_transforms as ST
import utils.temporal_transforms as TT
import utils.transforms as T
import utils.seqtransforms as SeqT
from torch.utils.data import DataLoader
from datasets.video_loader_xh import VideoDataset
from datasets.samplers import RandomIdentitySampler, RandomIdentitySamplerForSeq, RandomIdentitySamplerWYQ
from datasets.seqpreprocessor import SeqTrainPreprocessor, SeqTestPreprocessor
from datasets.set.mars import Mars
from datasets.set.ilidsvidsequence import iLIDSVIDSEQUENCE
from datasets.set.lsvid import LSVID | 11,830 |
# from torchvision.transforms import InterpolationMode
# import torchvision.transforms as T
__factory = {
'mars': Mars,
'ilidsvidsequence': iLIDSVIDSEQUENCE,
|
# from torchvision.transforms import InterpolationMode
# import torchvision.transforms as T
__factory = {
'mars': Mars,
'ilidsvidsequence': iLIDSVIDSEQUENCE, | 'lsvid': LSVID | 8 | 2023-12-11 04:03:46+00:00 | 16k |
MarilynKeller/aitviewer-skel | aitviewer/renderables/rigid_bodies.py | [
{
"identifier": "Arrows",
"path": "aitviewer/renderables/arrows.py",
"snippet": "class Arrows(Node):\n \"\"\"\n Draw an arrow.\n \"\"\"\n\n def __init__(\n self,\n origins,\n tips,\n r_base=0.01,\n r_head=0.02,\n p=0.1,\n color=(0.0, 0.0, 0.5,... | import numpy as np
from aitviewer.renderables.arrows import Arrows
from aitviewer.renderables.spheres import Spheres
from aitviewer.scene.node import Node
from aitviewer.utils.utils import (
compute_union_of_bounds,
compute_union_of_current_bounds,
) | 11,651 | # Copyright (C) 2023 ETH Zurich, Manuel Kaufmann, Velko Vechev, Dario Mylonopoulos
class RigidBodies(Node):
"""
A sequence of N 3D positions and orientations in space.
"""
def __init__(
self,
rb_pos,
rb_ori,
radius=0.02,
length=0.2,
radius_cylinder=None,
color=(0.0, 1.0, 0.5, 1.0),
icon="\u0086",
**kwargs,
):
"""
Initializer.
:param rb_pos: A np array of shape (F, N, 3) containing N rigid-body centers over F time steps.
:param rb_ori: A np array of shape (F, N, 3, 3) containing N rigid-body orientations over F time steps.
:param radius: Radius of the sphere at the origin of the rigid body.
:param length: Length of arrows representing the orientation of the rigid body.
:param radius_cylinder: Radius of the cylinder representing the orientation, default is length / 50
:param color: Color of the rigid body centers (4-tuple).
"""
self._rb_pos = rb_pos[np.newaxis] if rb_pos.ndim == 2 else rb_pos
self._rb_ori = rb_ori[np.newaxis] if rb_ori.ndim == 3 else rb_ori
super(RigidBodies, self).__init__(n_frames=self.rb_pos.shape[0], color=color, icon=icon, **kwargs)
self.radius = radius
self.length = length
self.spheres = Spheres(rb_pos, radius=radius, color=color, is_selectable=False)
self._add_node(self.spheres, show_in_hierarchy=False)
self.coords = []
r_base = radius_cylinder or length / 50
r_head = r_base * 2
c = [0.0, 0.0, 0.0, 1.0]
for i in range(3):
line = self.rb_ori[..., :, i]
line = line / np.linalg.norm(line, axis=-1, keepdims=True) * length
color = c.copy()
color[i] = 1.0
axs = Arrows(
self.rb_pos,
self.rb_pos + line,
r_base=r_base,
r_head=r_head,
color=tuple(color),
is_selectable=False,
)
self._add_node(axs, show_in_hierarchy=False)
self.coords.append(axs)
@Node.color.setter
def color(self, color):
self.material.color = color
self.spheres.color = color
@property
def current_rb_pos(self):
idx = self.current_frame_id if self.rb_pos.shape[0] > 1 else 0
return self.rb_pos[idx]
@current_rb_pos.setter
def current_rb_pos(self, pos):
idx = self.current_frame_id if self.rb_pos.shape[0] > 1 else 0
self.rb_pos[idx] = pos
@property
def current_rb_ori(self):
idx = self.current_frame_id if self.rb_ori.shape[0] > 1 else 0
return self.rb_ori[idx]
@current_rb_ori.setter
def current_rb_ori(self, ori):
idx = self.current_frame_id if self.rb_ori.shape[0] > 1 else 0
self.rb_ori[idx] = ori
@property
def rb_pos(self):
return self._rb_pos
@rb_pos.setter
def rb_pos(self, rb_pos):
self._rb_pos = rb_pos if len(rb_pos.shape) == 3 else rb_pos[np.newaxis]
self.n_frames = self._rb_pos.shape[0]
@property
def rb_ori(self):
return self._rb_ori
@rb_ori.setter
def rb_ori(self, rb_ori):
self._rb_ori = rb_ori if len(rb_ori.shape) == 4 else rb_ori[np.newaxis]
self.n_frames = self._rb_ori.shape[0]
@property
def bounds(self):
| # Copyright (C) 2023 ETH Zurich, Manuel Kaufmann, Velko Vechev, Dario Mylonopoulos
class RigidBodies(Node):
"""
A sequence of N 3D positions and orientations in space.
"""
def __init__(
self,
rb_pos,
rb_ori,
radius=0.02,
length=0.2,
radius_cylinder=None,
color=(0.0, 1.0, 0.5, 1.0),
icon="\u0086",
**kwargs,
):
"""
Initializer.
:param rb_pos: A np array of shape (F, N, 3) containing N rigid-body centers over F time steps.
:param rb_ori: A np array of shape (F, N, 3, 3) containing N rigid-body orientations over F time steps.
:param radius: Radius of the sphere at the origin of the rigid body.
:param length: Length of arrows representing the orientation of the rigid body.
:param radius_cylinder: Radius of the cylinder representing the orientation, default is length / 50
:param color: Color of the rigid body centers (4-tuple).
"""
self._rb_pos = rb_pos[np.newaxis] if rb_pos.ndim == 2 else rb_pos
self._rb_ori = rb_ori[np.newaxis] if rb_ori.ndim == 3 else rb_ori
super(RigidBodies, self).__init__(n_frames=self.rb_pos.shape[0], color=color, icon=icon, **kwargs)
self.radius = radius
self.length = length
self.spheres = Spheres(rb_pos, radius=radius, color=color, is_selectable=False)
self._add_node(self.spheres, show_in_hierarchy=False)
self.coords = []
r_base = radius_cylinder or length / 50
r_head = r_base * 2
c = [0.0, 0.0, 0.0, 1.0]
for i in range(3):
line = self.rb_ori[..., :, i]
line = line / np.linalg.norm(line, axis=-1, keepdims=True) * length
color = c.copy()
color[i] = 1.0
axs = Arrows(
self.rb_pos,
self.rb_pos + line,
r_base=r_base,
r_head=r_head,
color=tuple(color),
is_selectable=False,
)
self._add_node(axs, show_in_hierarchy=False)
self.coords.append(axs)
@Node.color.setter
def color(self, color):
self.material.color = color
self.spheres.color = color
@property
def current_rb_pos(self):
idx = self.current_frame_id if self.rb_pos.shape[0] > 1 else 0
return self.rb_pos[idx]
@current_rb_pos.setter
def current_rb_pos(self, pos):
idx = self.current_frame_id if self.rb_pos.shape[0] > 1 else 0
self.rb_pos[idx] = pos
@property
def current_rb_ori(self):
idx = self.current_frame_id if self.rb_ori.shape[0] > 1 else 0
return self.rb_ori[idx]
@current_rb_ori.setter
def current_rb_ori(self, ori):
idx = self.current_frame_id if self.rb_ori.shape[0] > 1 else 0
self.rb_ori[idx] = ori
@property
def rb_pos(self):
return self._rb_pos
@rb_pos.setter
def rb_pos(self, rb_pos):
self._rb_pos = rb_pos if len(rb_pos.shape) == 3 else rb_pos[np.newaxis]
self.n_frames = self._rb_pos.shape[0]
@property
def rb_ori(self):
return self._rb_ori
@rb_ori.setter
def rb_ori(self, rb_ori):
self._rb_ori = rb_ori if len(rb_ori.shape) == 4 else rb_ori[np.newaxis]
self.n_frames = self._rb_ori.shape[0]
@property
def bounds(self): | return compute_union_of_bounds(self.coords) | 3 | 2023-12-07 16:13:50+00:00 | 16k |
nexB/dejacode | dejacode/urls.py | [
{
"identifier": "ComponentViewSet",
"path": "component_catalog/api.py",
"snippet": "class ComponentViewSet(CreateRetrieveUpdateListViewSet):\n queryset = Component.objects.all()\n serializer_class = ComponentSerializer\n filterset_class = ComponentFilterSet\n lookup_field = \"uuid\"\n sea... | from django.conf import settings
from django.conf.urls import include
from django.contrib import admin
from django.contrib.auth import views as auth_views
from django.contrib.auth.decorators import login_required
from django.template.loader import render_to_string
from django.urls import path
from django.views.defaults import page_not_found
from django.views.generic import RedirectView
from django.views.generic import TemplateView
from notifications.views import mark_all_as_read
from rest_framework.documentation import include_docs_urls
from rest_framework.routers import DefaultRouter
from component_catalog.api import ComponentViewSet
from component_catalog.api import PackageViewSet
from component_catalog.api import SubcomponentViewSet
from component_catalog.views import send_scan_notification
from dje import two_factor
from dje.admin import dejacode_site
from dje.api import ExternalReferenceViewSet
from dje.forms import DejaCodeAuthenticationForm
from dje.registration import DejaCodeActivationView
from dje.registration import DejaCodeRegistrationForm
from dje.views import AccountProfileView
from dje.views import AllNotificationsList
from dje.views import DataspaceAwareAutocompleteLookup
from dje.views import DataspaceAwareRelatedLookup
from dje.views import GlobalSearchListView
from dje.views import IntegrationsStatusView
from dje.views import UnreadNotificationsList
from dje.views import home_view
from dje.views import index_dispatch
from dje.views import urn_resolve_view
from license_library.api import LicenseAnnotationViewSet
from license_library.api import LicenseViewSet
from organization.api import OwnerViewSet
from policy.api import UsagePolicyViewSet
from product_portfolio.api import CodebaseResourceViewSet
from product_portfolio.api import ProductComponentViewSet
from product_portfolio.api import ProductPackageViewSet
from product_portfolio.api import ProductViewSet
from reporting.api import ReportViewSet
from workflow.api import RequestTemplateViewSet
from workflow.api import RequestViewSet
from django_registration.backends.activation.views import RegistrationView | 11,751 | #
# Copyright (c) nexB Inc. and others. All rights reserved.
# DejaCode is a trademark of nexB Inc.
# SPDX-License-Identifier: AGPL-3.0-only
# See https://github.com/nexB/dejacode for support or download.
# See https://aboutcode.org for more information about AboutCode FOSS projects.
#
# Replace the default admin site with the DejaCode one.
admin.site = dejacode_site
# Restframework API
api_router = DefaultRouter()
api_router.register("owners", OwnerViewSet)
api_router.register("licenses", LicenseViewSet)
api_router.register("license_annotations", LicenseAnnotationViewSet)
api_router.register("components", ComponentViewSet)
api_router.register("subcomponents", SubcomponentViewSet)
api_router.register("packages", PackageViewSet)
api_router.register("products", ProductViewSet)
api_router.register("product_components", ProductComponentViewSet)
api_router.register("product_packages", ProductPackageViewSet)
api_router.register("codebase_resources", CodebaseResourceViewSet)
api_router.register("request_templates", RequestTemplateViewSet)
api_router.register("requests", RequestViewSet)
| #
# Copyright (c) nexB Inc. and others. All rights reserved.
# DejaCode is a trademark of nexB Inc.
# SPDX-License-Identifier: AGPL-3.0-only
# See https://github.com/nexB/dejacode for support or download.
# See https://aboutcode.org for more information about AboutCode FOSS projects.
#
# Replace the default admin site with the DejaCode one.
admin.site = dejacode_site
# Restframework API
api_router = DefaultRouter()
api_router.register("owners", OwnerViewSet)
api_router.register("licenses", LicenseViewSet)
api_router.register("license_annotations", LicenseAnnotationViewSet)
api_router.register("components", ComponentViewSet)
api_router.register("subcomponents", SubcomponentViewSet)
api_router.register("packages", PackageViewSet)
api_router.register("products", ProductViewSet)
api_router.register("product_components", ProductComponentViewSet)
api_router.register("product_packages", ProductPackageViewSet)
api_router.register("codebase_resources", CodebaseResourceViewSet)
api_router.register("request_templates", RequestTemplateViewSet)
api_router.register("requests", RequestViewSet) | api_router.register("reports", ReportViewSet) | 28 | 2023-12-07 16:57:42+00:00 | 16k |
wusize/CLIM | src/open_clip/factory.py | [
{
"identifier": "OPENAI_DATASET_MEAN",
"path": "src/open_clip/constants.py",
"snippet": "OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)"
},
{
"identifier": "OPENAI_DATASET_STD",
"path": "src/open_clip/constants.py",
"snippet": "OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.2... | import json
import logging
import os
import pathlib
import re
import torch
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, Optional, Tuple, Union
from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
from .model import CLIP, CustomTextCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\
resize_pos_embed, get_cast_dtype
from .coca_model import CoCa
from .loss import ClipLoss, DistillClipLoss, CoCaLoss
from .openai import load_openai_model
from .pretrained import is_pretrained_cfg, get_pretrained_cfg, \
download_pretrained, list_pretrained_tags_by_model, download_pretrained_from_hf
from .transform import image_transform, AugmentationCfg, det_image_transform
from .tokenizer import HFTokenizer, tokenize
from open_clip import eva_clip
from open_clip import eva_clip | 13,347 |
def load_checkpoint(model, checkpoint_path, strict=True):
state_dict = load_state_dict(checkpoint_path)
# detect old format and make compatible with new format
if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'):
state_dict = convert_to_custom_text_state_dict(state_dict)
resize_pos_embed(state_dict, model)
incompatible_keys = model.load_state_dict(state_dict, strict=strict)
return incompatible_keys
def create_model(
model_name: str,
pretrained: Optional[str] = None,
precision: str = 'fp32',
device: Union[str, torch.device] = 'cpu',
jit: bool = False,
force_quick_gelu: bool = False,
force_custom_text: bool = False,
force_patch_dropout: Optional[float] = None,
force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
pretrained_image: bool = False,
pretrained_hf: bool = True,
cache_dir: Optional[str] = None,
output_dict: Optional[bool] = None,
require_pretrained: bool = False,
):
has_hf_hub_prefix = model_name.startswith(HF_HUB_PREFIX)
if has_hf_hub_prefix:
model_id = model_name[len(HF_HUB_PREFIX):]
checkpoint_path = download_pretrained_from_hf(model_id, cache_dir=cache_dir)
config_path = download_pretrained_from_hf(model_id, filename='open_clip_config.json', cache_dir=cache_dir)
with open(config_path, 'r', encoding='utf-8') as f:
config = json.load(f)
pretrained_cfg = config['preprocess_cfg']
model_cfg = config['model_cfg']
else:
model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names
checkpoint_path = None
pretrained_cfg = {}
model_cfg = None
if isinstance(device, str):
device = torch.device(device)
if pretrained == 'eva':
return eva_clip.create_model(model_name=model_name,
pretrained=cache_dir, force_custom_clip=True,
precision=precision,
device=device,)
if pretrained and pretrained.lower() == 'openai':
logging.info(f'Loading pretrained {model_name} from OpenAI.')
model = load_openai_model(
model_name,
precision=precision,
device=device,
jit=jit,
cache_dir=cache_dir,
)
# to always output dict even if it is clip
if output_dict and hasattr(model, "output_dict"):
model.output_dict = True
else:
model_cfg = model_cfg or get_model_config(model_name)
if model_cfg is not None:
logging.info(f'Loaded {model_name} model config.')
else:
logging.error(f'Model config for {model_name} not found; available models {list_models()}.')
raise RuntimeError(f'Model config for {model_name} not found.')
if force_quick_gelu:
# override for use of QuickGELU on non-OpenAI transformer models
model_cfg["quick_gelu"] = True
if force_patch_dropout is not None:
# override the default patch dropout value
model_cfg["vision_cfg"]["patch_dropout"] = force_patch_dropout
if force_image_size is not None:
# override model config's image size
model_cfg["vision_cfg"]["image_size"] = force_image_size
if pretrained_image:
if 'timm_model_name' in model_cfg.get('vision_cfg', {}):
# pretrained weight loading for timm models set via vision_cfg
model_cfg['vision_cfg']['timm_model_pretrained'] = True
else:
assert False, 'pretrained image towers currently only supported for timm models'
cast_dtype = get_cast_dtype(precision)
is_hf_model = 'hf_model_name' in model_cfg.get('text_cfg', {})
custom_text = model_cfg.pop('custom_text', False) or force_custom_text or is_hf_model
if custom_text:
if is_hf_model:
model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf
if "coca" in model_name:
model = CoCa(**model_cfg, cast_dtype=cast_dtype)
else:
model = CustomTextCLIP(**model_cfg, cast_dtype=cast_dtype)
else:
model = CLIP(**model_cfg, cast_dtype=cast_dtype)
pretrained_loaded = False
if pretrained:
checkpoint_path = ''
pretrained_cfg = get_pretrained_cfg(model_name, pretrained)
if pretrained_cfg:
checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir)
elif os.path.exists(pretrained):
checkpoint_path = pretrained
if checkpoint_path:
print(f'Loading pretrained {model_name} weights ({pretrained}).', flush=True)
logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')
load_checkpoint(model, checkpoint_path)
else:
error_str = (
f'Pretrained weights ({pretrained}) not found for model {model_name}.'
|
HF_HUB_PREFIX = 'hf-hub:'
_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
_MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs
def _natural_key(string_):
return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
def _rescan_model_configs():
global _MODEL_CONFIGS
config_ext = ('.json',)
config_files = []
for config_path in _MODEL_CONFIG_PATHS:
if config_path.is_file() and config_path.suffix in config_ext:
config_files.append(config_path)
elif config_path.is_dir():
for ext in config_ext:
config_files.extend(config_path.glob(f'*{ext}'))
for cf in config_files:
with open(cf, 'r') as f:
model_cfg = json.load(f)
if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')):
_MODEL_CONFIGS[cf.stem] = model_cfg
_MODEL_CONFIGS = {k: v for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))}
_rescan_model_configs() # initial populate of model config registry
def list_models():
""" enumerate available model architectures based on config files """
return list(_MODEL_CONFIGS.keys())
def add_model_config(path):
""" add model config path or file and update registry """
if not isinstance(path, Path):
path = Path(path)
_MODEL_CONFIG_PATHS.append(path)
_rescan_model_configs()
def get_model_config(model_name):
if model_name in _MODEL_CONFIGS:
return deepcopy(_MODEL_CONFIGS[model_name])
else:
return None
def get_tokenizer(model_name):
if 'EVA' in model_name:
return eva_clip.get_tokenizer(model_name)
if model_name.startswith(HF_HUB_PREFIX):
tokenizer = HFTokenizer(model_name[len(HF_HUB_PREFIX):])
else:
config = get_model_config(model_name)
tokenizer = HFTokenizer(
config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else tokenize
return tokenizer
def load_state_dict(checkpoint_path: str, map_location='cpu'):
checkpoint = torch.load(checkpoint_path, map_location=map_location)
if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
else:
state_dict = checkpoint
if next(iter(state_dict.items()))[0].startswith('module'):
state_dict = {k[7:]: v for k, v in state_dict.items()}
return state_dict
def load_checkpoint(model, checkpoint_path, strict=True):
state_dict = load_state_dict(checkpoint_path)
# detect old format and make compatible with new format
if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'):
state_dict = convert_to_custom_text_state_dict(state_dict)
resize_pos_embed(state_dict, model)
incompatible_keys = model.load_state_dict(state_dict, strict=strict)
return incompatible_keys
def create_model(
model_name: str,
pretrained: Optional[str] = None,
precision: str = 'fp32',
device: Union[str, torch.device] = 'cpu',
jit: bool = False,
force_quick_gelu: bool = False,
force_custom_text: bool = False,
force_patch_dropout: Optional[float] = None,
force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
pretrained_image: bool = False,
pretrained_hf: bool = True,
cache_dir: Optional[str] = None,
output_dict: Optional[bool] = None,
require_pretrained: bool = False,
):
has_hf_hub_prefix = model_name.startswith(HF_HUB_PREFIX)
if has_hf_hub_prefix:
model_id = model_name[len(HF_HUB_PREFIX):]
checkpoint_path = download_pretrained_from_hf(model_id, cache_dir=cache_dir)
config_path = download_pretrained_from_hf(model_id, filename='open_clip_config.json', cache_dir=cache_dir)
with open(config_path, 'r', encoding='utf-8') as f:
config = json.load(f)
pretrained_cfg = config['preprocess_cfg']
model_cfg = config['model_cfg']
else:
model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names
checkpoint_path = None
pretrained_cfg = {}
model_cfg = None
if isinstance(device, str):
device = torch.device(device)
if pretrained == 'eva':
return eva_clip.create_model(model_name=model_name,
pretrained=cache_dir, force_custom_clip=True,
precision=precision,
device=device,)
if pretrained and pretrained.lower() == 'openai':
logging.info(f'Loading pretrained {model_name} from OpenAI.')
model = load_openai_model(
model_name,
precision=precision,
device=device,
jit=jit,
cache_dir=cache_dir,
)
# to always output dict even if it is clip
if output_dict and hasattr(model, "output_dict"):
model.output_dict = True
else:
model_cfg = model_cfg or get_model_config(model_name)
if model_cfg is not None:
logging.info(f'Loaded {model_name} model config.')
else:
logging.error(f'Model config for {model_name} not found; available models {list_models()}.')
raise RuntimeError(f'Model config for {model_name} not found.')
if force_quick_gelu:
# override for use of QuickGELU on non-OpenAI transformer models
model_cfg["quick_gelu"] = True
if force_patch_dropout is not None:
# override the default patch dropout value
model_cfg["vision_cfg"]["patch_dropout"] = force_patch_dropout
if force_image_size is not None:
# override model config's image size
model_cfg["vision_cfg"]["image_size"] = force_image_size
if pretrained_image:
if 'timm_model_name' in model_cfg.get('vision_cfg', {}):
# pretrained weight loading for timm models set via vision_cfg
model_cfg['vision_cfg']['timm_model_pretrained'] = True
else:
assert False, 'pretrained image towers currently only supported for timm models'
cast_dtype = get_cast_dtype(precision)
is_hf_model = 'hf_model_name' in model_cfg.get('text_cfg', {})
custom_text = model_cfg.pop('custom_text', False) or force_custom_text or is_hf_model
if custom_text:
if is_hf_model:
model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf
if "coca" in model_name:
model = CoCa(**model_cfg, cast_dtype=cast_dtype)
else:
model = CustomTextCLIP(**model_cfg, cast_dtype=cast_dtype)
else:
model = CLIP(**model_cfg, cast_dtype=cast_dtype)
pretrained_loaded = False
if pretrained:
checkpoint_path = ''
pretrained_cfg = get_pretrained_cfg(model_name, pretrained)
if pretrained_cfg:
checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir)
elif os.path.exists(pretrained):
checkpoint_path = pretrained
if checkpoint_path:
print(f'Loading pretrained {model_name} weights ({pretrained}).', flush=True)
logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')
load_checkpoint(model, checkpoint_path)
else:
error_str = (
f'Pretrained weights ({pretrained}) not found for model {model_name}.' | f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.') | 16 | 2023-12-09 05:43:08+00:00 | 16k |
moonshot-admin/moonshot | third-party/tqdm-4.66.1/tqdm/auto.py | [
{
"identifier": "TqdmExperimentalWarning",
"path": "third-party/tqdm-4.66.1/tqdm/std.py",
"snippet": "class TqdmExperimentalWarning(TqdmWarning, FutureWarning):\n \"\"\"beta feature, unstable API and behaviour\"\"\"\n pass"
},
{
"identifier": "tqdm",
"path": "third-party/tqdm-4.66.1/tq... | import warnings
from .std import TqdmExperimentalWarning
from .autonotebook import tqdm as notebook_tqdm
from .asyncio import tqdm as asyncio_tqdm
from .std import tqdm as std_tqdm | 12,677 | """
Enables multiple commonly used features.
Method resolution order:
- `tqdm.autonotebook` without import warnings
- `tqdm.asyncio`
- `tqdm.std` base class
Usage:
>>> from tqdm.auto import trange, tqdm
>>> for i in trange(10):
... ...
"""
with warnings.catch_warnings():
| """
Enables multiple commonly used features.
Method resolution order:
- `tqdm.autonotebook` without import warnings
- `tqdm.asyncio`
- `tqdm.std` base class
Usage:
>>> from tqdm.auto import trange, tqdm
>>> for i in trange(10):
... ...
"""
with warnings.catch_warnings(): | warnings.simplefilter("ignore", category=TqdmExperimentalWarning) | 0 | 2023-12-14 07:43:03+00:00 | 16k |
LkPrtctrd/BSL-V53 | Heart/Logic/LogicLaserMessageFactory.py | [
{
"identifier": "ClientHelloMessage",
"path": "Heart/Packets/Client/Authentification/ClientHelloMessage.py",
"snippet": "class ClientHelloMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields)... | from Heart.Packets.Client.Authentification.ClientHelloMessage import ClientHelloMessage
from Heart.Packets.Client.Authentification.LoginMessage import LoginMessage
from Heart.Packets.Client.Battle.AskForBattleEndMessage import AskForBattleEndMessage
from Heart.Packets.Client.Home.ChangeAvatarNameMessage import ChangeAvatarNameMessage
from Heart.Packets.Client.Home.EndClientTurnMessage import EndClientTurnMessage
from Heart.Packets.Client.Home.GoHomeFromOfflinePractiseMessage import GoHomeFromOfflinePractiseMessage
from Heart.Packets.Client.Home.GoHomeMessage import GoHomeMessage
from Heart.Packets.Client.Home.GetPlayerProfileMessage import GetPlayerProfileMessage
from Heart.Packets.Client.Home.AskForAllianceDataMessage import AskForAllianceDataMessage
from Heart.Packets.Client.Socket.KeepAliveMessage import KeepAliveMessage
from Heart.Packets.Server.Authentification.LoginFailedMessage import LoginFailedMessage
from Heart.Packets.Server.Authentification.LoginOkMessage import LoginOkMessage
from Heart.Packets.Server.Authentification.OutOfSyncMessage import OutOfSyncMessage
from Heart.Packets.Server.Authentification.ServerHelloMessage import ServerHelloMessage
from Heart.Packets.Server.Battle.BattleEndMessage import BattleEndMessage
from Heart.Packets.Server.Home.AvailableServerCommandMessage import AvailableServerCommandMessage
from Heart.Packets.Server.Home.LobbyInfoMessage import LobbyInfoMessage
from Heart.Packets.Server.Home.OwnHomeDataMessage import OwnHomeDataMessage
from Heart.Packets.Server.Socket.KeepAliveServerMessage import KeepAliveServerMessage
from Heart.Packets.Server.Home.PlayerProfileMessage import PlayerProfileMessage
from Heart.Packets.Server.Home.MyAllianceMessage import MyAllianceMessage
from Heart.Packets.Server.Home.AllianceDataMessage import AllianceDataMessage | 13,938 |
class LogicLaserMessageFactory:
messagesList = {
10055: 'AskPlayerJWTokenMessage',
10099: 'ClientCryptoErrorMessage',
|
class LogicLaserMessageFactory:
messagesList = {
10055: 'AskPlayerJWTokenMessage',
10099: 'ClientCryptoErrorMessage', | 10100: ClientHelloMessage, | 0 | 2023-12-14 18:57:56+00:00 | 16k |
pan-x-c/EE-LLM | megatron/core/models/gpt/gpt_layer_specs.py | [
{
"identifier": "get_bias_dropout_add",
"path": "megatron/core/fusions/fused_bias_dropout.py",
"snippet": "def get_bias_dropout_add(training, fused):\n if fused:\n # jit scripting for a nn.module (with dropout) is not\n # triggering the fusion kernel. For now, we use two\n # diff... | from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
from megatron.core.fusions.fused_layer_norm import FusedLayerNorm
from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear
from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules
from megatron.core.transformer.custom_layers.transformer_engine import (
TEDotProductAttention,
TELayerNormColumnParallelLinear,
TERowParallelLinear,
)
from megatron.core.transformer.dot_product_attention import DotProductAttention
from megatron.core.transformer.enums import AttnMaskType
from megatron.core.transformer.mlp import MLP, MLPSubmodules
from megatron.core.transformer.spec_utils import ModuleSpec
from megatron.core.transformer.switch_mlp import SwitchMLP
from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules | 12,884 |
# Use this spec to use lower level Transformer Engine modules (required for fp8 training)
gpt_layer_with_transformer_engine_spec = ModuleSpec(
module=TransformerLayer,
submodules=TransformerLayerSubmodules(
self_attention=ModuleSpec(
module=SelfAttention,
|
# Use this spec to use lower level Transformer Engine modules (required for fp8 training)
gpt_layer_with_transformer_engine_spec = ModuleSpec(
module=TransformerLayer,
submodules=TransformerLayerSubmodules(
self_attention=ModuleSpec(
module=SelfAttention, | params={"attn_mask_type": AttnMaskType.causal}, | 10 | 2023-12-07 08:29:38+00:00 | 16k |
tommy-xq/SA2VP | vit_train_sa2vp.py | [
{
"identifier": "create_optimizer",
"path": "optim_factory.py",
"snippet": "def create_optimizer(args, model, get_num_layer=None, get_layer_scale=None, filter_bias_and_bn=True, skip_list=None):\n opt_lower = args.opt.lower()\n weight_decay = args.weight_decay\n if weight_decay and filter_bias_a... | import argparse
import datetime
import numpy as np
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import json
import os
import utils
import random
import deepspeed
from pathlib import Path
from time import sleep
from timm.data.mixup import Mixup
from timm.models import create_model
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils import ModelEma
from optim_factory import create_optimizer, get_parameter_groups, LayerDecayValueAssigner
from datasets import build_dataset
from datasets import build_beit_pretraining_dataset, build_beit_pretraining_dataset_val
from engine_for_train import train_one_epoch, evaluate # engine for sa2vp
from utils import NativeScalerWithGradNormCount as NativeScaler
from scipy import interpolate
from timm.models.layers import trunc_normal_
from functools import partial
from vpt_main.src.models.vit_backbones.vit_tinypara import VisionTransformer # choose model to train
from deepspeed import DeepSpeedConfig
| 11,119 | dataset_train, sampler=sampler_train,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=True,
)
if dataset_val is not None:
data_loader_val = torch.utils.data.DataLoader(
dataset_val, sampler=sampler_val,
batch_size=int(4*args.batch_size),
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=False
)
else:
data_loader_val = None
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
print("Mixup is activated!")
mixup_fn = Mixup(
mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
label_smoothing=args.smoothing, num_classes=args.nb_classes)
model = Dual_model(args)
n_parameters = sum(p.numel() for p in model.vit_base.parameters() if p.requires_grad)
frozen_parameters = sum(p.numel() for p in model.vit_base.parameters() if not p.requires_grad)
total_parameters = sum(p.numel() for p in model.vit_base.parameters())
print('------------------------------')
# print to show parameters are frozen or learnable
for name, param in model.named_parameters():
print(name, param.requires_grad)
print('------------------------------')
model.to(device)
model_ema = None
if args.model_ema:
# Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
model_ema = ModelEma(
model,
decay=args.model_ema_decay,
device='cpu' if args.model_ema_force_cpu else '',
resume='')
print("Using EMA with decay = %.8f" % args.model_ema_decay)
model_without_ddp = model
# print("Model = %s" % str(model_without_ddp))
total_batch_size = args.batch_size * args.update_freq * utils.get_world_size()
num_training_steps_per_epoch = len(dataset_train) // total_batch_size
print("LR = %.8f" % args.lr)
print("Batch size = %d" % total_batch_size)
print("Update frequent = %d" % args.update_freq)
print("Number of training examples = %d" % len(dataset_train))
print("Number of training training per epoch = %d" % num_training_steps_per_epoch)
assigner = None
if assigner is not None:
print("Assigned values = %s" % str(assigner.values))
# skip_weight_decay_list = model.no_weight_decay()
skip_weight_decay_list = None
if args.enable_deepspeed:
loss_scaler = None
optimizer_params = get_parameter_groups(
model, args.weight_decay, skip_weight_decay_list,
assigner.get_layer_id if assigner is not None else None,
assigner.get_scale if assigner is not None else None)
model, optimizer, _, _ = ds_init(
args=args, model=model, model_parameters=optimizer_params, dist_init_required=not args.distributed,
)
print("model.gradient_accumulation_steps() = %d" % model.gradient_accumulation_steps())
assert model.gradient_accumulation_steps() == args.update_freq
else:
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
model_without_ddp = model.module
optimizer = create_optimizer(
args, model_without_ddp, skip_list=skip_weight_decay_list,
get_num_layer=assigner.get_layer_id if assigner is not None else None,
get_layer_scale=assigner.get_scale if assigner is not None else None)
loss_scaler = NativeScaler()
print("Use step level LR scheduler!")
lr_schedule_values = utils.cosine_scheduler(
args.lr, args.min_lr, args.epochs, num_training_steps_per_epoch,
warmup_epochs=args.warmup_epochs, warmup_steps=args.warmup_steps,
)
if args.weight_decay_end is None:
args.weight_decay_end = args.weight_decay
wd_schedule_values = utils.cosine_scheduler(
args.weight_decay, args.weight_decay_end, args.epochs, num_training_steps_per_epoch)
print("Max WD = %.7f, Min WD = %.7f" % (max(wd_schedule_values), min(wd_schedule_values)))
if mixup_fn is not None:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif args.smoothing > 0.:
criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
else:
criterion = torch.nn.CrossEntropyLoss()
print("criterion = %s" % str(criterion))
utils.auto_load_model(
args=args, model=model, model_without_ddp=model_without_ddp,
optimizer=optimizer, loss_scaler=loss_scaler, model_ema=model_ema)
if args.eval:
| # --------------------------------------------------------
# SA2VP: Spatially Aligned-and-Adapted Visual Prompt code
# reference:
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Based on timm
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# --------------------------------------------------------'
#os.environ['CUDA_DEVICE_ORDER']='PCI_BUS_ID' # can be removed and set in bash
#os.environ['CUDA_VISIBLE_DEVICES']='0' # can be removed and set in bash
class Dual_model(nn.Module):
def __init__(self, args):
super(Dual_model, self).__init__()
self.vit_base = VisionTransformer("sup_vitb16_imagenet21k", 224, num_classes=-1, vis=False) # choose ViT as backbone
self.vit_base.load_from(np.load(os.path.join("./backbone_ckpt", "imagenet21k_ViT-B_16.npz"))) # where to save pre-trained model ./backbone_ckpt
# show parameters is frozen or learnable
for k, p in self.vit_base.named_parameters():
name_list = k.split('.')
# print(name_list)
if name_list[2] == 'ppt' or name_list[2] == 'cross_conv' or name_list[2] == 'deep_ppt' or name_list[2] == 'encoder_norm_2' or name_list[2] == 'deep_proj' or name_list[2] == 'ppt_proj':
p.requires_grad = True
elif name_list[2] == 'cross_attn':
if name_list[4] == 'attn' or name_list[4] == 'attention_norm':
p.requires_grad = False
else:
p.requires_grad = True
elif name_list[2] == 'layer':
if name_list[4] == 'down_proj' or name_list[4] == 'up_proj' or name_list[4] == 'before_norm':
p.requires_grad = True
else:
p.requires_grad = False
else:
p.requires_grad = False
self.class_head = nn.Linear(768, args.nb_classes, bias=True)
trunc_normal_(self.class_head.weight, std=0.02)
def forward(self, x):
x, p = self.vit_base(x) # B*768
return self.class_head(x), self.class_head(p)
def get_args():
parser = argparse.ArgumentParser('SA2VP script for image classification', add_help=False)
parser.add_argument('--batch_size', default=64, type=int)
parser.add_argument('--epochs', default=30, type=int)
parser.add_argument('--update_freq', default=1, type=int)
parser.add_argument('--save_ckpt_freq', default=20, type=int)
# Model parameters
parser.add_argument('--model', default='beit_base_patch16_224', type=str, metavar='MODEL',
help='Name of model to train')
parser.add_argument('--rel_pos_bias', action='store_true')
parser.add_argument('--disable_rel_pos_bias', action='store_false', dest='rel_pos_bias')
parser.set_defaults(rel_pos_bias=False)
parser.add_argument('--abs_pos_emb', action='store_true')
parser.set_defaults(abs_pos_emb=True)
parser.add_argument('--layer_scale_init_value', default=0.1, type=float,
help="0.1 for base, 1e-5 for large. set 0 to disable layer scale")
parser.add_argument('--input_size', default=224, type=int,
help='images input size')
parser.add_argument('--second_input_size', default=112, type=int,
help='images input size for discrete vae')
parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
help='Dropout rate (default: 0.)')
parser.add_argument('--attn_drop_rate', type=float, default=0.0, metavar='PCT',
help='Attention dropout rate (default: 0.)')
parser.add_argument('--drop_path', type=float, default=0.1, metavar='PCT',
help='Drop path rate (default: 0.1)')
parser.add_argument('--disable_eval_during_finetuning', action='store_true', default=False)
parser.add_argument('--model_ema', action='store_true', default=False)
parser.add_argument('--model_ema_decay', type=float, default=0.9999, help='')
parser.add_argument('--model_ema_force_cpu', action='store_true', default=False, help='')
# Optimizer parameters
parser.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER',
help='Optimizer (default: "adamw"')
parser.add_argument('--opt_eps', default=1e-8, type=float, metavar='EPSILON',
help='Optimizer Epsilon (default: 1e-8)')
parser.add_argument('--opt_betas', default=None, type=float, nargs='+', metavar='BETA',
help='Optimizer Betas (default: None, use opt default)')
parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM',
help='Clip gradient norm (default: None, no clipping)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--weight_decay', type=float, default=0.05,
help='weight decay (default: 0.05)')
parser.add_argument('--weight_decay_end', type=float, default=None, help="""Final value of the
weight decay. We use a cosine schedule for WD and using a larger decay by
the end of training improves performance for ViTs.""")
parser.add_argument('--lr', type=float, default=5e-4, metavar='LR',
help='learning rate (default: 5e-4)')
parser.add_argument('--layer_decay', type=float, default=0.9)
parser.add_argument('--warmup_lr', type=float, default=1e-6, metavar='LR',
help='warmup learning rate (default: 1e-6)')
parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR',
help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
parser.add_argument('--warmup_epochs', type=int, default=5, metavar='N',
help='epochs to warmup LR, if scheduler supports')
parser.add_argument('--warmup_steps', type=int, default=-1, metavar='N',
help='num of steps to warmup LR, will overload warmup_epochs if set > 0')
# Augmentation parameters
parser.add_argument('--color_jitter', type=float, default=0.4, metavar='PCT',
help='Color jitter factor (default: 0.4)')
parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME',
help='Use AutoAugment policy. "v0" or "original". " + "(default: rand-m9-mstd0.5-inc1)'),
parser.add_argument('--smoothing', type=float, default=0,
help='Label smoothing (default: 0)')
parser.add_argument('--train_interpolation', type=str, default='bicubic',
help='Training interpolation (random, bilinear, bicubic default: "bicubic")')
parser.add_argument('--second_interpolation', type=str, default='lanczos',
help='Interpolation for discrete vae (random, bilinear, bicubic default: "lanczos")')
# Evaluation parameters
parser.add_argument('--crop_pct', type=float, default=None)
# * Random Erase params
parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
help='Random erase prob (default: 0.25)')
parser.add_argument('--remode', type=str, default='pixel',
help='Random erase mode (default: "pixel")')
parser.add_argument('--recount', type=int, default=1,
help='Random erase count (default: 1)')
parser.add_argument('--resplit', action='store_true', default=False,
help='Do not random erase first (clean) augmentation split')
# * Mixup params
parser.add_argument('--mixup', type=float, default=0,
help='mixup alpha, mixup enabled if > 0.')
parser.add_argument('--cutmix', type=float, default=0,
help='cutmix alpha, cutmix enabled if > 0.')
parser.add_argument('--cutmix_minmax', type=float, nargs='+', default=None,
help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
parser.add_argument('--mixup_prob', type=float, default=1.0,
help='Probability of performing mixup or cutmix when either/both is enabled')
parser.add_argument('--mixup_switch_prob', type=float, default=0.5,
help='Probability of switching to cutmix when both mixup and cutmix enabled')
parser.add_argument('--mixup_mode', type=str, default='batch',
help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
# * Finetuning params
parser.add_argument('--finetune', default='',
help='finetune from checkpoint')
parser.add_argument('--model_key', default='model|module', type=str)
parser.add_argument('--model_prefix', default='', type=str)
parser.add_argument('--init_scale', default=0.001, type=float)
parser.add_argument('--use_mean_pooling', action='store_true')
parser.set_defaults(use_mean_pooling=True)
parser.add_argument('--use_cls', action='store_false', dest='use_mean_pooling')
parser.add_argument('--disable_weight_decay_on_rel_pos_bias', action='store_true', default=False)
# Dataset parameters
parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', type=str,
help='dataset path')
parser.add_argument('--my_mode', default='train_val', type=str,
help='my mode to train or test')
parser.add_argument('--eval_data_path', default=None, type=str,
help='dataset path for evaluation')
parser.add_argument('--nb_classes', default=0, type=int,
help='number of the classification types')
parser.add_argument('--imagenet_default_mean_and_std', default=False, action='store_true')
parser.add_argument('--data_set', default='CUB', choices=['CIFAR', 'IMNET', 'image_folder', 'CUB', 'DOG', 'FLOWER', 'CAR', 'BIRD', 'CAL101', 'DMLAB','EUROSAT','PATCH_CAMELYON','Resisc45','Retinopathy','CLEVR_COUNT','CIFAR100','FOOD101','SVHN','DTD','FLOWER_S','PET','SVHN_S','SUN','CLEVR_DISTANCE','KITTI_DISTANCE','DS_LOC','DS_ORI','SN_AZI','SN_ELE', 'DTD_DAM', 'GTSRB_DAM', 'FOOD_DAM', 'CIFAR10_DAM', 'CIFAR100_DAM', 'SVHN_DAM'],
type=str, help='ImageNet dataset path')
parser.add_argument('--output_dir', default='',
help='path where to save, empty for no saving')
parser.add_argument('--log_dir', default=None,
help='path where to tensorboard log')
parser.add_argument('--device', default='cuda',
help='device to use for training / testing')
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--resume', default='',
help='resume from checkpoint')
parser.add_argument('--auto_resume', action='store_true')
parser.add_argument('--no_auto_resume', action='store_false', dest='auto_resume')
parser.set_defaults(auto_resume=True)
parser.add_argument('--save_ckpt', action='store_true')
parser.add_argument('--no_save_ckpt', action='store_false', dest='save_ckpt')
parser.set_defaults(save_ckpt=True)
parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
help='start epoch')
parser.add_argument('--eval', action='store_true',
help='Perform evaluation only')
parser.add_argument('--dist_eval', action='store_true', default=False,
help='Enabling distributed evaluation')
parser.add_argument('--num_workers', default=10, type=int)
parser.add_argument('--pin_mem', action='store_true',
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem')
parser.set_defaults(pin_mem=True)
# distributed training parameters
parser.add_argument('--world_size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--local_rank', default=-1, type=int)
parser.add_argument('--dist_on_itp', action='store_true')
parser.add_argument('--dist_url', default='env://',
help='url used to set up distributed training')
parser.add_argument('--enable_deepspeed', action='store_true', default=False)
known_args, _ = parser.parse_known_args()
if known_args.enable_deepspeed:
try:
parser = deepspeed.add_config_arguments(parser)
ds_init = deepspeed.initialize
except:
print("Please 'pip install deepspeed==0.4.0'")
exit(0)
else:
ds_init = None
return parser.parse_args(), ds_init
def main(args, ds_init):
utils.init_distributed_mode(args)
if ds_init is not None:
utils.create_ds_config(args)
print(args)
device = torch.device(args.device)
seed = 42
torch.manual_seed(seed)
np.random.seed(seed)
cudnn.benchmark = True
dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
if args.disable_eval_during_finetuning:
dataset_val = None
else:
dataset_val, _ = build_dataset(is_train=False, args=args)
print("Calculation of training examples = %d" % len(dataset_train))
print("Calculation of other examples = %d" % len(dataset_val))
if True: # args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
sampler_train = torch.utils.data.DistributedSampler(
dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
)
print("Sampler_train = %s" % str(sampler_train))
if args.dist_eval:
if len(dataset_val) % num_tasks != 0:
print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
'This will slightly alter validation results as extra duplicate entries are added to achieve '
'equal num of samples per-process.')
sampler_val = torch.utils.data.DistributedSampler(
dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=False)
else:
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
if global_rank == 0 and args.log_dir is not None:
os.makedirs(args.log_dir, exist_ok=True)
log_writer = utils.TensorboardLogger(log_dir=args.log_dir)
else:
log_writer = None
data_loader_train = torch.utils.data.DataLoader(
dataset_train, sampler=sampler_train,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=True,
)
if dataset_val is not None:
data_loader_val = torch.utils.data.DataLoader(
dataset_val, sampler=sampler_val,
batch_size=int(4*args.batch_size),
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=False
)
else:
data_loader_val = None
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
print("Mixup is activated!")
mixup_fn = Mixup(
mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
label_smoothing=args.smoothing, num_classes=args.nb_classes)
model = Dual_model(args)
n_parameters = sum(p.numel() for p in model.vit_base.parameters() if p.requires_grad)
frozen_parameters = sum(p.numel() for p in model.vit_base.parameters() if not p.requires_grad)
total_parameters = sum(p.numel() for p in model.vit_base.parameters())
print('------------------------------')
# print to show parameters are frozen or learnable
for name, param in model.named_parameters():
print(name, param.requires_grad)
print('------------------------------')
model.to(device)
model_ema = None
if args.model_ema:
# Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
model_ema = ModelEma(
model,
decay=args.model_ema_decay,
device='cpu' if args.model_ema_force_cpu else '',
resume='')
print("Using EMA with decay = %.8f" % args.model_ema_decay)
model_without_ddp = model
# print("Model = %s" % str(model_without_ddp))
total_batch_size = args.batch_size * args.update_freq * utils.get_world_size()
num_training_steps_per_epoch = len(dataset_train) // total_batch_size
print("LR = %.8f" % args.lr)
print("Batch size = %d" % total_batch_size)
print("Update frequent = %d" % args.update_freq)
print("Number of training examples = %d" % len(dataset_train))
print("Number of training training per epoch = %d" % num_training_steps_per_epoch)
assigner = None
if assigner is not None:
print("Assigned values = %s" % str(assigner.values))
# skip_weight_decay_list = model.no_weight_decay()
skip_weight_decay_list = None
if args.enable_deepspeed:
loss_scaler = None
optimizer_params = get_parameter_groups(
model, args.weight_decay, skip_weight_decay_list,
assigner.get_layer_id if assigner is not None else None,
assigner.get_scale if assigner is not None else None)
model, optimizer, _, _ = ds_init(
args=args, model=model, model_parameters=optimizer_params, dist_init_required=not args.distributed,
)
print("model.gradient_accumulation_steps() = %d" % model.gradient_accumulation_steps())
assert model.gradient_accumulation_steps() == args.update_freq
else:
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
model_without_ddp = model.module
optimizer = create_optimizer(
args, model_without_ddp, skip_list=skip_weight_decay_list,
get_num_layer=assigner.get_layer_id if assigner is not None else None,
get_layer_scale=assigner.get_scale if assigner is not None else None)
loss_scaler = NativeScaler()
print("Use step level LR scheduler!")
lr_schedule_values = utils.cosine_scheduler(
args.lr, args.min_lr, args.epochs, num_training_steps_per_epoch,
warmup_epochs=args.warmup_epochs, warmup_steps=args.warmup_steps,
)
if args.weight_decay_end is None:
args.weight_decay_end = args.weight_decay
wd_schedule_values = utils.cosine_scheduler(
args.weight_decay, args.weight_decay_end, args.epochs, num_training_steps_per_epoch)
print("Max WD = %.7f, Min WD = %.7f" % (max(wd_schedule_values), min(wd_schedule_values)))
if mixup_fn is not None:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif args.smoothing > 0.:
criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
else:
criterion = torch.nn.CrossEntropyLoss()
print("criterion = %s" % str(criterion))
utils.auto_load_model(
args=args, model=model, model_without_ddp=model_without_ddp,
optimizer=optimizer, loss_scaler=loss_scaler, model_ema=model_ema)
if args.eval:
| test_stats = evaluate(data_loader_val, model, device)
| 7 | 2023-12-12 13:19:17+00:00 | 16k |
lumina-test/lumina | lumina/e2e_test/test_gbn.py | [
{
"identifier": "get_qp_info_list",
"path": "lumina/analyzer/main.py",
"snippet": "def get_qp_info_list(switch_msg_snapshot):\n \"\"\" Get the list of QP info from the switch message snapshot\n\n Args:\n switch_msg_snapshot (str): The path to the switch message snapshot\n\n Returns:\n ... | import argparse, os, math, glob, logging, time
import lumina.analyzer.checker.integrity_check as integrity_check
import lumina.analyzer.checker.host_check as host_check
import lumina.analyzer.checker.gbn_check as gbn_check
import lumina.analyzer.checker.read_gbn_check as read_gbn_check
import lumina.orchestrator.host as host
import lumina.orchestrator.switch as switch
from lumina.analyzer.main import get_qp_info_list
from lumina.orchestrator.main import Orchestrator
from lumina.analyzer.counter.switch_counter import SwitchCounter
from lumina.analyzer.counter.host_counter import MLNXHostCounter, IntelHostCounter
from lumina.analyzer.pcap_processor.pcap_process import get_packet_list
from lumina.analyzer.measurer.latency_measure import LatencyMeasure
from lumina.utils.config_loggers import config_stream_handler, config_file_handler
from lumina.analyzer.packet_parser.roce_packet import TRIGGER_OOS, TRIGGER_TIMEOUT | 13,821 | RESULT_FILENAME = "result.log"
## Max # of retries for each experiment iteration
MAX_NB_EXP_RETRIES = 3
def setup_root_logger(orchestrator):
""" Setup the root logger for the test
Args:
orchestrator (Orchestrator object): Orchestrator object that contains all the configurations
Returns:
N/A
"""
root_logger = logging.getLogger()
root_logger.handlers.clear()
config_stream_handler(root_logger)
config_file_handler(logger=root_logger,
log_file=os.path.join(orchestrator.result_path, LOG_FILENAME),
no_format=False)
def run_traffic(orchestrator):
""" Run the traffic and collect the results
Args:
orchestrator (Orchestrator object): Orchestrator object that contains all the configurations
Returns:
bool: True if the experiment is successful, False otherwise
"""
orchestrator.rm_old_files()
if orchestrator.sync_and_compile() == False:
logging.error("Failed to sync and compile the code")
sys.exit(-1)
logging.info("Sync and compile completed")
if orchestrator.generate_switch_config_file() == False:
logging.error("Failed to generate switch configuration file")
sys.exit(-1)
num_repeats = orchestrator.get_num_repeats()
for i in range(num_repeats):
logging.info("=" * 100)
nb_retry = 0
iter_result = False
while nb_retry < MAX_NB_EXP_RETRIES:
if orchestrator.run_experiment() == False:
logging.error("Iteration %d: Failed to complete experiment" % i)
logging.error("Iteration %d: Rerun experiment (retry: %d)" % i, nb_retry)
nb_retry += 1
orchestrator.clean_up()
time.sleep(5)
continue
logging.info("Iteration %d: Completed experiment" % i)
try:
orchestrator.clean_up()
orchestrator.fetch_results(i)
logging.info("Iteration %d: Fetch experiment results" % i)
orchestrator.merge_traces(i)
logging.info("Iteration %d: Merge the pcap files" % i)
except:
logging.error("Iteration %d: Result collection failed" % (i))
logging.error("Iteration %d: Rerun experiment (retry: %d)" % (i, nb_retry))
nb_retry += 1
time.sleep(5)
continue
if orchestrator.check_integrity(i) == False:
logging.error("Iteration %d: Integrity check failed" % (i))
logging.error("Iteration %d: Rerun experiment (retry: %d)" % (i, nb_retry))
nb_retry += 1
time.sleep(5)
continue
iter_result = True
break
if iter_result is False:
logging.error("Iteration %d: Still failed after %d retries" % (i, nb_retry))
return False
return True
def analyze_retrans_latency(pkt, latency_measurement, is_read, logger):
""" Analyze the retransmission latency breakdown for an undelivered packet
Args:
pkt (Packet object): The undelivered packet
latency_measurement (LatencyMeasure object): A LatencyMeasure object that can compute latency breakdown
is_read (bool): If we use RDMA READ in this experiment
logger (logging.Logger): A logger object
Returns:
N/A
"""
# All the undelivered packets should be retransmitted in our test cases
if latency_measurement.get_retransmit_pkt(pkt) == None:
logger.error("\t\t No retransmit packet found for this packet")
logger.error("\t\t It is possible that this undelivered packet is a redundant transmission")
return
retrans_latency = latency_measurement.get_retransmit_latency(pkt)
if is_read == True:
# For RDMA READ, we should always find a NACK READ request that triggers retransmission
nack = latency_measurement.get_nack(pkt)
if nack is not None:
trigger = nack.get_trigger()
if trigger == TRIGGER_OOS:
next_delivered_pkt_delay = latency_measurement.get_qp_next_delivered_pkt_latency(pkt)
nack_gen_latency = latency_measurement.get_nack_gen_latency(pkt)
nack_resp_latency = latency_measurement.get_nack_resp_latency(pkt)
logger.info("\t\t Out of sequence (OOS) triggered retransmission")
logger.info("\t\t Retransmission latency: %fus" % (retrans_latency * 1e6))
logger.info('\t\t Next delivered packet delay: %fus' % (next_delivered_pkt_delay * 1e6))
logger.info("\t\t NACK READ request generation latency: %fus" % (nack_gen_latency * 1e6))
logger.info('\t\t NACK READ request response latency: %fus' % (nack_resp_latency * 1e6))
|
## All logs will be logged into file LOG_FILENAME
LOG_FILENAME = "test_gbn.log"
## Results (checkers and measurements) will also be dumped into file RESULT_FILENAME
RESULT_FILENAME = "result.log"
## Max # of retries for each experiment iteration
MAX_NB_EXP_RETRIES = 3
def setup_root_logger(orchestrator):
""" Setup the root logger for the test
Args:
orchestrator (Orchestrator object): Orchestrator object that contains all the configurations
Returns:
N/A
"""
root_logger = logging.getLogger()
root_logger.handlers.clear()
config_stream_handler(root_logger)
config_file_handler(logger=root_logger,
log_file=os.path.join(orchestrator.result_path, LOG_FILENAME),
no_format=False)
def run_traffic(orchestrator):
""" Run the traffic and collect the results
Args:
orchestrator (Orchestrator object): Orchestrator object that contains all the configurations
Returns:
bool: True if the experiment is successful, False otherwise
"""
orchestrator.rm_old_files()
if orchestrator.sync_and_compile() == False:
logging.error("Failed to sync and compile the code")
sys.exit(-1)
logging.info("Sync and compile completed")
if orchestrator.generate_switch_config_file() == False:
logging.error("Failed to generate switch configuration file")
sys.exit(-1)
num_repeats = orchestrator.get_num_repeats()
for i in range(num_repeats):
logging.info("=" * 100)
nb_retry = 0
iter_result = False
while nb_retry < MAX_NB_EXP_RETRIES:
if orchestrator.run_experiment() == False:
logging.error("Iteration %d: Failed to complete experiment" % i)
logging.error("Iteration %d: Rerun experiment (retry: %d)" % i, nb_retry)
nb_retry += 1
orchestrator.clean_up()
time.sleep(5)
continue
logging.info("Iteration %d: Completed experiment" % i)
try:
orchestrator.clean_up()
orchestrator.fetch_results(i)
logging.info("Iteration %d: Fetch experiment results" % i)
orchestrator.merge_traces(i)
logging.info("Iteration %d: Merge the pcap files" % i)
except:
logging.error("Iteration %d: Result collection failed" % (i))
logging.error("Iteration %d: Rerun experiment (retry: %d)" % (i, nb_retry))
nb_retry += 1
time.sleep(5)
continue
if orchestrator.check_integrity(i) == False:
logging.error("Iteration %d: Integrity check failed" % (i))
logging.error("Iteration %d: Rerun experiment (retry: %d)" % (i, nb_retry))
nb_retry += 1
time.sleep(5)
continue
iter_result = True
break
if iter_result is False:
logging.error("Iteration %d: Still failed after %d retries" % (i, nb_retry))
return False
return True
def analyze_retrans_latency(pkt, latency_measurement, is_read, logger):
""" Analyze the retransmission latency breakdown for an undelivered packet
Args:
pkt (Packet object): The undelivered packet
latency_measurement (LatencyMeasure object): A LatencyMeasure object that can compute latency breakdown
is_read (bool): If we use RDMA READ in this experiment
logger (logging.Logger): A logger object
Returns:
N/A
"""
# All the undelivered packets should be retransmitted in our test cases
if latency_measurement.get_retransmit_pkt(pkt) == None:
logger.error("\t\t No retransmit packet found for this packet")
logger.error("\t\t It is possible that this undelivered packet is a redundant transmission")
return
retrans_latency = latency_measurement.get_retransmit_latency(pkt)
if is_read == True:
# For RDMA READ, we should always find a NACK READ request that triggers retransmission
nack = latency_measurement.get_nack(pkt)
if nack is not None:
trigger = nack.get_trigger()
if trigger == TRIGGER_OOS:
next_delivered_pkt_delay = latency_measurement.get_qp_next_delivered_pkt_latency(pkt)
nack_gen_latency = latency_measurement.get_nack_gen_latency(pkt)
nack_resp_latency = latency_measurement.get_nack_resp_latency(pkt)
logger.info("\t\t Out of sequence (OOS) triggered retransmission")
logger.info("\t\t Retransmission latency: %fus" % (retrans_latency * 1e6))
logger.info('\t\t Next delivered packet delay: %fus' % (next_delivered_pkt_delay * 1e6))
logger.info("\t\t NACK READ request generation latency: %fus" % (nack_gen_latency * 1e6))
logger.info('\t\t NACK READ request response latency: %fus' % (nack_resp_latency * 1e6))
| elif trigger == TRIGGER_TIMEOUT: | 10 | 2023-12-09 08:21:14+00:00 | 16k |
boweniac/autogan | autogan/agents/tool_agent_search.py | [
{
"identifier": "CodeExecution",
"path": "autogan/tools/code_execution_tool.py",
"snippet": "class CodeExecution:\n def __init__(self, work_dir: Optional[str] = None):\n \"\"\"A class for code execution\n 用于代码执行的类\n\n Supports python, bash, shell, powershell code\n 支持 pyth... | import re
from collections import defaultdict
from typing import Optional, Dict
from autogan.tools.code_execution_tool import CodeExecution
from autogan.tools.wolfram_alpha_tool import WolframAlphaAPIWrapper
from autogan.oai.count_tokens_utils import count_text_tokens
from autogan.agents.universal_agent import UniversalAgent
from autogan.utils.compressed_text_utils import compressed_text_universal
from autogan.tools.web_search_tool import WebSearch | 11,974 |
class ToolAgentSearch(UniversalAgent):
def __init__(
self,
search_config: Dict,
agent_config: Optional[Dict] = None,
retry_times: Optional[int] = 10,
name: Optional[str] = "WebSearchExp",
duty: Optional[str] = 'Not only can I search for information on the internet, '
'but I can also answer questions using the Wolfram engine.',
work_flow: Optional[str] = """I hope you are an internet search expert. When you receive a search request, you have the following two tools to choose from:
1. web: You can search for information on the internet. When using it, please enclose the search keywords in your output with the ```web\n ``` symbol, for example:
```web
Your search keywords
```
2. wolfram: You can use the Wolfram engine to help you calculate or query data related to Mathematics, finance, unit conversion, data analysis, science, geography, history, culture, movies, music, etc.
When using it, please enclose the English question that Wolfram can understand in your output with the ```wolfram\n ``` symbol, for example:
```wolfram
one wolfram query
```
Note: When you decide to use a tool, please do not @ anyone.""",
# duty: Optional[str] = '我不但可以从网络上搜索资料，还可以通过 wolfram 引擎来回答问题。',
# work_flow: Optional[str] = """我希望你是一个网络搜索专家，当你收到搜索请求时，你有一下两种工具可供选择：
#
# 1. web: 可以在网络上查找资料。使用时请在你的输出内容中，将搜索关键词用```web\n ``` 符号封装，例如：
# ```web
# Your search keywords
# ```
#
# 2.wolfram: 可以使用wolfram引擎，帮你计算或查询数学、金融、单位转换、数据分析、科学、地理、历史、文化、电影、音乐等相关数据。使用时请在你的输出内容中，将 wolfram 可以理解的英文问题用```wolfram\n ``` 符号封装，例如：
# ```wolfram
# one wolfram query
# ```
#
# 注意：当你决定使用工具时，请不要@任何人""",
):
"""WebSearchExpert
1.Receive the user's question and convert it into search keywords.
2.Call the Google Search API to obtain a result and extract the webpage content.
3.If no content related to the user's question is extracted,
call the Google Search API again to obtain the next result.
4.Repeat operations 2 and 3 until reaching retry_times.
Within the same task session domain, if the search keywords are the same,
the offset of the search results will accumulate and move backwards.
:param agent_config: The agent configuration includes:
agent 配置包括：
- main_model: The LLM configuration of the agent's main body.
agent 主体的 LLM 配置。
- summary_model: The LLM configuration used for compressing context and generating text summaries.
用于压缩上下文以及生成文本摘要的 LLM 配置。
- request_interval_time: The interval time of LLM requests.
LLM 请求间隔时间。
- request_timeout:The timeout of LLM requests.
LLM 请求超时时间。
- max_retries: The maximum number of retries for LLM requests.
LLM 请求最大重试次数。
:param search_config: JSON format of email_config {"cx": "", "key": ""}
:param retry_times: Represent the maximum number of attempts for each search, the default is 10.
:param name: The agent name should be unique in the organizational structure.
:param duty: Used to explain one's job responsibilities to other agents.
:param work_flow: Defines the workflow of the agent.
定义 agent 的工作流程。
"""
super().__init__(
name,
agent_config=agent_config,
duty=duty,
work_flow=work_flow,
use_tool="join"
)
self._web_search = WebSearch(search_config["google_search"]) if "google_search" in search_config else None
self._wolfram_alpha = WolframAlphaAPIWrapper(
search_config["wolfram_alpha"]) if "wolfram_alpha" in search_config else None
self._conversation_search_index = defaultdict(int)
self._retry_times = retry_times
def tool_function(self, task_id: str, param: Optional[str] = None,
tokens: Optional[int] = None) -> tuple[str, int]:
lang, code = CodeExecution.extract_code(param)
if lang == "web" and code:
if self._web_search:
return self._web_function(task_id, code)
else:
return "Please add the Google Custom Search JSON API configuration.", 0
elif lang == "wolfram" and code:
if self._wolfram_alpha:
return self._wolfram_alpha_function(code)
else:
return "Please add the WolframAlphaAPI configuration.", 0
else:
return """Please make a choice between web and wolfram, and use the ``` symbol for encapsulation, for example:
```wolfram
one wolfram query
```""", 18
def _web_function(self, task_id: str, param: str) -> tuple[str, int]:
loop = self._retry_times
for i in range(loop):
# Accumulate the search offset of the same task and the same keyword.
self._conversation_search_index[task_id] += 1
start = self._conversation_search_index[task_id]
# Get webpage content.
detail = self._web_search.get_search_detail(param, start, self.name, "search", self.response_func)
if detail:
# Extract content related to the user's question from the webpage content.
|
class ToolAgentSearch(UniversalAgent):
def __init__(
self,
search_config: Dict,
agent_config: Optional[Dict] = None,
retry_times: Optional[int] = 10,
name: Optional[str] = "WebSearchExp",
duty: Optional[str] = 'Not only can I search for information on the internet, '
'but I can also answer questions using the Wolfram engine.',
work_flow: Optional[str] = """I hope you are an internet search expert. When you receive a search request, you have the following two tools to choose from:
1. web: You can search for information on the internet. When using it, please enclose the search keywords in your output with the ```web\n ``` symbol, for example:
```web
Your search keywords
```
2. wolfram: You can use the Wolfram engine to help you calculate or query data related to Mathematics, finance, unit conversion, data analysis, science, geography, history, culture, movies, music, etc.
When using it, please enclose the English question that Wolfram can understand in your output with the ```wolfram\n ``` symbol, for example:
```wolfram
one wolfram query
```
Note: When you decide to use a tool, please do not @ anyone.""",
# duty: Optional[str] = '我不但可以从网络上搜索资料，还可以通过 wolfram 引擎来回答问题。',
# work_flow: Optional[str] = """我希望你是一个网络搜索专家，当你收到搜索请求时，你有一下两种工具可供选择：
#
# 1. web: 可以在网络上查找资料。使用时请在你的输出内容中，将搜索关键词用```web\n ``` 符号封装，例如：
# ```web
# Your search keywords
# ```
#
# 2.wolfram: 可以使用wolfram引擎，帮你计算或查询数学、金融、单位转换、数据分析、科学、地理、历史、文化、电影、音乐等相关数据。使用时请在你的输出内容中，将 wolfram 可以理解的英文问题用```wolfram\n ``` 符号封装，例如：
# ```wolfram
# one wolfram query
# ```
#
# 注意：当你决定使用工具时，请不要@任何人""",
):
"""WebSearchExpert
1.Receive the user's question and convert it into search keywords.
2.Call the Google Search API to obtain a result and extract the webpage content.
3.If no content related to the user's question is extracted,
call the Google Search API again to obtain the next result.
4.Repeat operations 2 and 3 until reaching retry_times.
Within the same task session domain, if the search keywords are the same,
the offset of the search results will accumulate and move backwards.
:param agent_config: The agent configuration includes:
agent 配置包括：
- main_model: The LLM configuration of the agent's main body.
agent 主体的 LLM 配置。
- summary_model: The LLM configuration used for compressing context and generating text summaries.
用于压缩上下文以及生成文本摘要的 LLM 配置。
- request_interval_time: The interval time of LLM requests.
LLM 请求间隔时间。
- request_timeout:The timeout of LLM requests.
LLM 请求超时时间。
- max_retries: The maximum number of retries for LLM requests.
LLM 请求最大重试次数。
:param search_config: JSON format of email_config {"cx": "", "key": ""}
:param retry_times: Represent the maximum number of attempts for each search, the default is 10.
:param name: The agent name should be unique in the organizational structure.
:param duty: Used to explain one's job responsibilities to other agents.
:param work_flow: Defines the workflow of the agent.
定义 agent 的工作流程。
"""
super().__init__(
name,
agent_config=agent_config,
duty=duty,
work_flow=work_flow,
use_tool="join"
)
self._web_search = WebSearch(search_config["google_search"]) if "google_search" in search_config else None
self._wolfram_alpha = WolframAlphaAPIWrapper(
search_config["wolfram_alpha"]) if "wolfram_alpha" in search_config else None
self._conversation_search_index = defaultdict(int)
self._retry_times = retry_times
def tool_function(self, task_id: str, param: Optional[str] = None,
tokens: Optional[int] = None) -> tuple[str, int]:
lang, code = CodeExecution.extract_code(param)
if lang == "web" and code:
if self._web_search:
return self._web_function(task_id, code)
else:
return "Please add the Google Custom Search JSON API configuration.", 0
elif lang == "wolfram" and code:
if self._wolfram_alpha:
return self._wolfram_alpha_function(code)
else:
return "Please add the WolframAlphaAPI configuration.", 0
else:
return """Please make a choice between web and wolfram, and use the ``` symbol for encapsulation, for example:
```wolfram
one wolfram query
```""", 18
def _web_function(self, task_id: str, param: str) -> tuple[str, int]:
loop = self._retry_times
for i in range(loop):
# Accumulate the search offset of the same task and the same keyword.
self._conversation_search_index[task_id] += 1
start = self._conversation_search_index[task_id]
# Get webpage content.
detail = self._web_search.get_search_detail(param, start, self.name, "search", self.response_func)
if detail:
# Extract content related to the user's question from the webpage content. | compressed_text, total_tokens = compressed_text_universal( | 4 | 2023-12-06 03:24:34+00:00 | 16k |
TACJu/Compositor | Compositor_Mask2Former/mask2former_video/data_video/ytvis_eval.py | [
{
"identifier": "YTVOS",
"path": "Compositor_Mask2Former/mask2former_video/data_video/datasets/ytvis_api/ytvos.py",
"snippet": "class YTVOS:\n def __init__(self, annotation_file=None):\n \"\"\"\n Constructor of Microsoft COCO helper class for reading and visualizing annotations.\n ... | import contextlib
import copy
import io
import itertools
import json
import logging
import numpy as np
import os
import pycocotools.mask as mask_util
import torch
import detectron2.utils.comm as comm
from collections import OrderedDict
from .datasets.ytvis_api.ytvos import YTVOS
from .datasets.ytvis_api.ytvoseval import YTVOSeval
from tabulate import tabulate
from detectron2.config import CfgNode
from detectron2.data import MetadataCatalog
from detectron2.evaluation import DatasetEvaluator
from detectron2.utils.file_io import PathManager
from detectron2.utils.logger import create_small_table | 12,570 | Derive the desired score numbers from summarized COCOeval.
Args:
coco_eval (None or COCOEval): None represents no predictions from model.
iou_type (str):
class_names (None or list[str]): if provided, will use it to predict
per-category AP.
Returns:
a dict of {metric name: score}
"""
metrics = ["AP", "AP50", "AP75", "APs", "APm", "APl", "AR1", "AR10"]
if coco_eval is None:
self._logger.warn("No predictions from the model!")
return {metric: float("nan") for metric in metrics}
# the standard metrics
results = {
metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan")
for idx, metric in enumerate(metrics)
}
self._logger.info(
"Evaluation results for {}: \n".format("segm") + create_small_table(results)
)
if not np.isfinite(sum(results.values())):
self._logger.info("Some metrics cannot be computed and is shown as NaN.")
if class_names is None or len(class_names) <= 1:
return results
# Compute per-category AP
# from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa
precisions = coco_eval.eval["precision"]
# precision has dims (iou, recall, cls, area range, max dets)
assert len(class_names) == precisions.shape[2]
results_per_category = []
for idx, name in enumerate(class_names):
# area range index 0: all area ranges
# max dets index -1: typically 100 per image
precision = precisions[:, :, idx, 0, -1]
precision = precision[precision > -1]
ap = np.mean(precision) if precision.size else float("nan")
results_per_category.append(("{}".format(name), float(ap * 100)))
# tabulate it
N_COLS = min(6, len(results_per_category) * 2)
results_flatten = list(itertools.chain(*results_per_category))
results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])
table = tabulate(
results_2d,
tablefmt="pipe",
floatfmt=".3f",
headers=["category", "AP"] * (N_COLS // 2),
numalign="left",
)
self._logger.info("Per-category {} AP: \n".format("segm") + table)
results.update({"AP-" + name: ap for name, ap in results_per_category})
return results
def instances_to_coco_json_video(inputs, outputs):
"""
Dump an "Instances" object to a COCO-format json that's used for evaluation.
Args:
instances (Instances):
video_id (int): the image id
Returns:
list[dict]: list of json annotations in COCO format.
"""
assert len(inputs) == 1, "More than one inputs are loaded for inference!"
video_id = inputs[0]["video_id"]
video_length = inputs[0]["length"]
scores = outputs["pred_scores"]
labels = outputs["pred_labels"]
masks = outputs["pred_masks"]
ytvis_results = []
for instance_id, (s, l, m) in enumerate(zip(scores, labels, masks)):
segms = [
mask_util.encode(np.array(_mask[:, :, None], order="F", dtype="uint8"))[0]
for _mask in m
]
for rle in segms:
rle["counts"] = rle["counts"].decode("utf-8")
res = {
"video_id": video_id,
"score": s,
"category_id": l,
"segmentations": segms,
}
ytvis_results.append(res)
return ytvis_results
def _evaluate_predictions_on_coco(
coco_gt,
coco_results,
img_ids=None,
):
"""
Evaluate the coco results using COCOEval API.
"""
assert len(coco_results) > 0
coco_results = copy.deepcopy(coco_results)
# When evaluating mask AP, if the results contain bbox, cocoapi will
# use the box area as the area of the instance, instead of the mask area.
# This leads to a different definition of small/medium/large.
# We remove the bbox field to let mask AP use mask area.
for c in coco_results:
c.pop("bbox", None)
coco_dt = coco_gt.loadRes(coco_results)
| # Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/sukjunhwang/IFC
class YTVISEvaluator(DatasetEvaluator):
"""
Evaluate AR for object proposals, AP for instance detection/segmentation, AP
for keypoint detection outputs using COCO's metrics.
See http://cocodataset.org/#detection-eval and
http://cocodataset.org/#keypoints-eval to understand its metrics.
In addition to COCO, this evaluator is able to support any bounding box detection,
instance segmentation, or keypoint detection dataset.
"""
def __init__(
self,
dataset_name,
tasks=None,
distributed=True,
output_dir=None,
*,
use_fast_impl=True,
):
"""
Args:
dataset_name (str): name of the dataset to be evaluated.
It must have either the following corresponding metadata:
"json_file": the path to the COCO format annotation
Or it must be in detectron2's standard dataset format
so it can be converted to COCO format automatically.
tasks (tuple[str]): tasks that can be evaluated under the given
configuration. A task is one of "bbox", "segm", "keypoints".
By default, will infer this automatically from predictions.
distributed (True): if True, will collect results from all ranks and run evaluation
in the main process.
Otherwise, will only evaluate the results in the current process.
output_dir (str): optional, an output directory to dump all
results predicted on the dataset. The dump contains two files:
1. "instances_predictions.pth" a file in torch serialization
format that contains all the raw original predictions.
2. "coco_instances_results.json" a json file in COCO's result
format.
use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.
Although the results should be very close to the official implementation in COCO
API, it is still recommended to compute results with the official API for use in
papers. The faster implementation also uses more RAM.
"""
self._logger = logging.getLogger(__name__)
self._distributed = distributed
self._output_dir = output_dir
self._use_fast_impl = use_fast_impl
if tasks is not None and isinstance(tasks, CfgNode):
self._logger.warning(
"COCO Evaluator instantiated using config, this is deprecated behavior."
" Please pass in explicit arguments instead."
)
self._tasks = None # Infering it from predictions should be better
else:
self._tasks = tasks
self._cpu_device = torch.device("cpu")
self._metadata = MetadataCatalog.get(dataset_name)
json_file = PathManager.get_local_path(self._metadata.json_file)
with contextlib.redirect_stdout(io.StringIO()):
self._ytvis_api = YTVOS(json_file)
# Test set json files do not contain annotations (evaluation must be
# performed using the COCO evaluation server).
self._do_evaluation = "annotations" in self._ytvis_api.dataset
def reset(self):
self._predictions = []
def process(self, inputs, outputs):
"""
Args:
inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
It is a list of dict. Each dict corresponds to an image and
contains keys like "height", "width", "file_name", "image_id".
outputs: the outputs of a COCO model. It is a list of dicts with key
"instances" that contains :class:`Instances`.
"""
prediction = instances_to_coco_json_video(inputs, outputs)
self._predictions.extend(prediction)
def evaluate(self):
"""
Args:
img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset
"""
if self._distributed:
comm.synchronize()
predictions = comm.gather(self._predictions, dst=0)
predictions = list(itertools.chain(*predictions))
if not comm.is_main_process():
return {}
else:
predictions = self._predictions
if len(predictions) == 0:
self._logger.warning("[COCOEvaluator] Did not receive valid predictions.")
return {}
if self._output_dir:
PathManager.mkdirs(self._output_dir)
file_path = os.path.join(self._output_dir, "instances_predictions.pth")
with PathManager.open(file_path, "wb") as f:
torch.save(predictions, f)
self._results = OrderedDict()
self._eval_predictions(predictions)
# Copy so the caller can do whatever with results
return copy.deepcopy(self._results)
def _eval_predictions(self, predictions):
"""
Evaluate predictions. Fill self._results with the metrics of the tasks.
"""
self._logger.info("Preparing results for YTVIS format ...")
# unmap the category ids for COCO
if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id
all_contiguous_ids = list(dataset_id_to_contiguous_id.values())
num_classes = len(all_contiguous_ids)
assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1
reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}
for result in predictions:
category_id = result["category_id"]
assert category_id < num_classes, (
f"A prediction has class={category_id}, "
f"but the dataset only has {num_classes} classes and "
f"predicted class id should be in [0, {num_classes - 1}]."
)
result["category_id"] = reverse_id_mapping[category_id]
if self._output_dir:
file_path = os.path.join(self._output_dir, "results.json")
self._logger.info("Saving results to {}".format(file_path))
with PathManager.open(file_path, "w") as f:
f.write(json.dumps(predictions))
f.flush()
if not self._do_evaluation:
self._logger.info("Annotations are not available for evaluation.")
return
coco_eval = (
_evaluate_predictions_on_coco(
self._ytvis_api,
predictions,
)
if len(predictions) > 0
else None # cocoapi does not handle empty results very well
)
res = self._derive_coco_results(
coco_eval, class_names=self._metadata.get("thing_classes")
)
self._results["segm"] = res
def _derive_coco_results(self, coco_eval, class_names=None):
"""
Derive the desired score numbers from summarized COCOeval.
Args:
coco_eval (None or COCOEval): None represents no predictions from model.
iou_type (str):
class_names (None or list[str]): if provided, will use it to predict
per-category AP.
Returns:
a dict of {metric name: score}
"""
metrics = ["AP", "AP50", "AP75", "APs", "APm", "APl", "AR1", "AR10"]
if coco_eval is None:
self._logger.warn("No predictions from the model!")
return {metric: float("nan") for metric in metrics}
# the standard metrics
results = {
metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan")
for idx, metric in enumerate(metrics)
}
self._logger.info(
"Evaluation results for {}: \n".format("segm") + create_small_table(results)
)
if not np.isfinite(sum(results.values())):
self._logger.info("Some metrics cannot be computed and is shown as NaN.")
if class_names is None or len(class_names) <= 1:
return results
# Compute per-category AP
# from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa
precisions = coco_eval.eval["precision"]
# precision has dims (iou, recall, cls, area range, max dets)
assert len(class_names) == precisions.shape[2]
results_per_category = []
for idx, name in enumerate(class_names):
# area range index 0: all area ranges
# max dets index -1: typically 100 per image
precision = precisions[:, :, idx, 0, -1]
precision = precision[precision > -1]
ap = np.mean(precision) if precision.size else float("nan")
results_per_category.append(("{}".format(name), float(ap * 100)))
# tabulate it
N_COLS = min(6, len(results_per_category) * 2)
results_flatten = list(itertools.chain(*results_per_category))
results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])
table = tabulate(
results_2d,
tablefmt="pipe",
floatfmt=".3f",
headers=["category", "AP"] * (N_COLS // 2),
numalign="left",
)
self._logger.info("Per-category {} AP: \n".format("segm") + table)
results.update({"AP-" + name: ap for name, ap in results_per_category})
return results
def instances_to_coco_json_video(inputs, outputs):
"""
Dump an "Instances" object to a COCO-format json that's used for evaluation.
Args:
instances (Instances):
video_id (int): the image id
Returns:
list[dict]: list of json annotations in COCO format.
"""
assert len(inputs) == 1, "More than one inputs are loaded for inference!"
video_id = inputs[0]["video_id"]
video_length = inputs[0]["length"]
scores = outputs["pred_scores"]
labels = outputs["pred_labels"]
masks = outputs["pred_masks"]
ytvis_results = []
for instance_id, (s, l, m) in enumerate(zip(scores, labels, masks)):
segms = [
mask_util.encode(np.array(_mask[:, :, None], order="F", dtype="uint8"))[0]
for _mask in m
]
for rle in segms:
rle["counts"] = rle["counts"].decode("utf-8")
res = {
"video_id": video_id,
"score": s,
"category_id": l,
"segmentations": segms,
}
ytvis_results.append(res)
return ytvis_results
def _evaluate_predictions_on_coco(
coco_gt,
coco_results,
img_ids=None,
):
"""
Evaluate the coco results using COCOEval API.
"""
assert len(coco_results) > 0
coco_results = copy.deepcopy(coco_results)
# When evaluating mask AP, if the results contain bbox, cocoapi will
# use the box area as the area of the instance, instead of the mask area.
# This leads to a different definition of small/medium/large.
# We remove the bbox field to let mask AP use mask area.
for c in coco_results:
c.pop("bbox", None)
coco_dt = coco_gt.loadRes(coco_results) | coco_eval = YTVOSeval(coco_gt, coco_dt) | 1 | 2023-12-12 11:49:28+00:00 | 16k |
ebb-earl-co/tidal-wave | tidal_wave/playlist.py | [
{
"identifier": "AudioFormat",
"path": "tidal_wave/media.py",
"snippet": "class AudioFormat(str, Enum):\n sony_360_reality_audio = \"360\"\n dolby_atmos = \"Atmos\"\n hi_res = \"HiRes\"\n mqa = \"MQA\"\n lossless = \"Lossless\"\n high = \"High\"\n low = \"Low\""
},
{
"identi... | from dataclasses import dataclass
from pathlib import Path
from types import SimpleNamespace
from typing import Dict, List, Optional, Set, Tuple, Union
from requests import HTTPError, Session
from .media import AudioFormat
from .models import (
PlaylistsEndpointResponseJSON,
TracksEndpointResponseJSON,
VideosEndpointResponseJSON,
)
from .requesting import request_playlists
from .track import Track
from .utils import download_cover_image, temporary_file, TIDAL_API_URL
from .video import Video
import json
import logging
import math
import shutil
import sys
import ffmpeg
import mutagen | 11,213 | else:
subdirs.add(tv.album_dir)
subdirs.add(tv.album_dir.parent)
files[i - 1] = {i: None}
continue
_path: Optional[Path] = Path(tv.outfile) if tv is not None else None
# if the item never got turned into a track or video
if _path is None:
files[i - 1] = {i: None}
continue
# if the track or video didn't download
if _path.exists():
if _path.stat().st_size == 0:
files[i - 1] = {i: None}
continue
else:
files[i - 1] = {i: None}
continue
# otherwise, move files and clean up
if isinstance(tv, Track):
new_path: Path = self.playlist_dir / f"{i:03d} - {tv.trackname}"
new_path.write_bytes(_path.read_bytes())
_path.unlink()
files[i - 1] = {i: str(new_path.absolute())}
elif isinstance(tv, Video):
new_path: Path = self.playlist_dir / f"{i:03d} - {_path.name}"
new_path.write_bytes(_path.read_bytes())
_path.unlink()
files[i - 1] = {i: str(new_path.absolute())}
else:
self.files: List[Dict[int, Optional[str]]] = files
# Find all subdirectories written to
subdirs: Set[Path] = set()
for tv in self.tracks_videos:
if isinstance(tv, Track):
try:
getattr(tv, "album_dir")
except AttributeError:
pass
else:
subdirs.add(tv.album_dir)
subdirs.add(tv.album_dir.parent)
elif isinstance(tv, Video):
subdirs.add(tv.artist_dir)
# Copy all artist images, artist bio JSON files out
# of subdirs
artist_images: Set[Path] = set()
for subdir in subdirs:
for p in subdir.glob("*.jpg"):
if p.name == "cover.jpg":
continue
artist_images.add(p)
else:
for artist_image_path in artist_images:
if artist_image_path.exists():
shutil.copyfile(
artist_image_path.absolute(),
self.playlist_dir / artist_image_path.name,
)
artist_bios: Set[Path] = set()
for subdir in subdirs:
for p in subdir.glob("*bio.json"):
artist_bios.add(p)
else:
for artist_bio_path in artist_bios:
if artist_bio_path.exists():
shutil.copyfile(
artist_bio_path.absolute(),
self.playlist_dir / artist_bio_path.name,
)
# Remove all subdirs
for subdir in subdirs:
if subdir.exists():
shutil.rmtree(subdir)
else:
return self.playlist_dir
def craft_m3u8_text(self):
"""This method creates a file called playlist.m3u8 in self.playlist_dir
that is a standard M3U. Needs to be called after self.flatten_playlist_dir
in order to be able to access self.files
N.b. the already-written file is temporarily copied to a .mp4 version in a
temporary directory because .m4a files cannot be read with mutagen."""
m3u_text: str = f"#EXTM3U\n#EXTENC:UTF-8\n#EXTIMG:{str(self.cover_path.absolute())}\n#PLAYLIST:{self.name}\n"
logger.info(
f"Creating .m3u8 playlist file for Playlist with ID '{self.playlist_id}'"
)
for d in self.files:
file: str = next(iter(d.values()))
if file is None:
continue
elif file.endswith(".flac"):
m = mutagen.File(file)
artist: str = m.get("artist", [""])[0]
title: str = m.get("title", [""])[0]
extinf: str = (
f"#EXTINF:{math.ceil(m.info.length)},"
f"{artist} - {title}\n{file}\n"
)
m3u_text += extinf
elif file.endswith(".mka"):
m = mutagen.File(file)
artist: str = m.get("ARTI", [""])[0]
title: str = m.get("TITL", [""])[0]
extinf: str = (
f"#EXTINF:{math.ceil(m.info.length)},"
f"{artist} - {title}\n{file}\n"
)
m3u_text += extinf
elif file.endswith(".m4a"):
# Mutagen cannot read .m4a files, so make a copy with all
# of the metadata tags as a .mp4 in a temporary directory
|
logger = logging.getLogger("__name__")
@dataclass
class Playlist:
playlist_id: str # UUID4
def __post_init__(self):
self.playlist_dir: Optional[Path] = None
self.playlist_cover_saved: bool = False
def get_metadata(self, session: Session):
"""Request from TIDAL API /playlists endpoint"""
self.metadata: Optional[PlaylistsEndpointResponseJSON] = request_playlists(
session=session, identifier=self.playlist_id
)
if self.metadata is None:
return
self.name = (
self.metadata.title.replace("/", "_")
.replace("|", "_")
.replace(":", " -")
.replace('"', "")
.replace("..", "")
)
def set_items(self, session: Session):
"""Uses data from TIDAL API /playlists/items endpoint to
populate self.items"""
playlist_items: Optional[PlaylistsItemsResponseJSON] = get_playlist(
session=session, playlist_id=self.playlist_id
)
if playlist_items is None:
self.items = tuple()
else:
self.items: Tuple[Optional[PlaylistItem]] = tuple(playlist_items.items)
def set_dir(self, out_dir: Path):
"""Populates self.playlist_dir based on self.name, self.playlist_id"""
playlist_substring: str = f"{self.name} [{self.playlist_id}]"
self.playlist_dir: Path = out_dir / "Playlists" / playlist_substring
self.playlist_dir.mkdir(parents=True, exist_ok=True)
def save_cover_image(self, session: Session, out_dir: Path):
"""Requests self.metadata.image and attempts to write it to disk"""
if self.playlist_dir is None:
self.set_dir(out_dir=out_dir)
self.cover_path: Path = self.playlist_dir / "cover.jpg"
if not self.cover_path.exists():
download_cover_image(
session=session,
cover_uuid=self.metadata.square_image,
output_dir=self.playlist_dir,
dimension=1080,
)
else:
self.playlist_cover_saved = True
def save_description(self):
"""Requests self.metadata.description and attempts to write it to disk"""
description_path: Path = self.playlist_dir / "PlaylistDescription.txt"
if self.metadata.description is not None and len(self.metadata.description) > 0:
if not description_path.exists():
description_path.write_text(f"{self.metadata.description}\n")
def get_items(self, session: Session, audio_format: AudioFormat):
"""Using either Track.get() or Video.get(), attempt to request
the data for each track or video in self.items"""
if len(self.items) == 0:
return
tracks_videos: list = [None] * len(self.items)
for i, item in enumerate(self.items):
if item is None:
tracks_videos[i] = None
continue
elif isinstance(item, TracksEndpointResponseJSON):
track: Track = Track(track_id=item.id)
track.get(
session=session,
audio_format=audio_format,
out_dir=self.playlist_dir,
metadata=item,
)
tracks_videos[i] = track
elif isinstance(item, VideosEndpointResponseJSON):
video: Video = Video(video_id=item.id)
video.get(
session=session,
out_dir=self.playlist_dir,
metadata=item,
)
tracks_videos[i] = video
else:
tracks_videos[i] = None
continue
else:
self.tracks_videos: Tuple[
Tuple[int, Optional[Union[Track, Video]]]
] = tuple(tracks_videos)
return tracks_videos
def flatten_playlist_dir(self):
"""When self.get_items() is called, the tracks and/or videos in
self.items are downloaded using their self-contained .get() logic;
this means that they will be downloaded to albums. This function
"flattens" self.playlist_dir, meaning that it moves all downloaded
audio and video files to self.playlist_dir, and removes the various
subdirectories created"""
files: List[Dict[int, Optional[str]]] = [None] * len(self.tracks_videos)
if len(self.tracks_videos) == 0:
return
subdirs: Set[Path] = set()
for i, tv in enumerate(self.tracks_videos, 1):
if getattr(tv, "outfile") is None:
try:
getattr(tv, "album_dir")
except AttributeError:
pass
else:
subdirs.add(tv.album_dir)
subdirs.add(tv.album_dir.parent)
files[i - 1] = {i: None}
continue
_path: Optional[Path] = Path(tv.outfile) if tv is not None else None
# if the item never got turned into a track or video
if _path is None:
files[i - 1] = {i: None}
continue
# if the track or video didn't download
if _path.exists():
if _path.stat().st_size == 0:
files[i - 1] = {i: None}
continue
else:
files[i - 1] = {i: None}
continue
# otherwise, move files and clean up
if isinstance(tv, Track):
new_path: Path = self.playlist_dir / f"{i:03d} - {tv.trackname}"
new_path.write_bytes(_path.read_bytes())
_path.unlink()
files[i - 1] = {i: str(new_path.absolute())}
elif isinstance(tv, Video):
new_path: Path = self.playlist_dir / f"{i:03d} - {_path.name}"
new_path.write_bytes(_path.read_bytes())
_path.unlink()
files[i - 1] = {i: str(new_path.absolute())}
else:
self.files: List[Dict[int, Optional[str]]] = files
# Find all subdirectories written to
subdirs: Set[Path] = set()
for tv in self.tracks_videos:
if isinstance(tv, Track):
try:
getattr(tv, "album_dir")
except AttributeError:
pass
else:
subdirs.add(tv.album_dir)
subdirs.add(tv.album_dir.parent)
elif isinstance(tv, Video):
subdirs.add(tv.artist_dir)
# Copy all artist images, artist bio JSON files out
# of subdirs
artist_images: Set[Path] = set()
for subdir in subdirs:
for p in subdir.glob("*.jpg"):
if p.name == "cover.jpg":
continue
artist_images.add(p)
else:
for artist_image_path in artist_images:
if artist_image_path.exists():
shutil.copyfile(
artist_image_path.absolute(),
self.playlist_dir / artist_image_path.name,
)
artist_bios: Set[Path] = set()
for subdir in subdirs:
for p in subdir.glob("*bio.json"):
artist_bios.add(p)
else:
for artist_bio_path in artist_bios:
if artist_bio_path.exists():
shutil.copyfile(
artist_bio_path.absolute(),
self.playlist_dir / artist_bio_path.name,
)
# Remove all subdirs
for subdir in subdirs:
if subdir.exists():
shutil.rmtree(subdir)
else:
return self.playlist_dir
def craft_m3u8_text(self):
"""This method creates a file called playlist.m3u8 in self.playlist_dir
that is a standard M3U. Needs to be called after self.flatten_playlist_dir
in order to be able to access self.files
N.b. the already-written file is temporarily copied to a .mp4 version in a
temporary directory because .m4a files cannot be read with mutagen."""
m3u_text: str = f"#EXTM3U\n#EXTENC:UTF-8\n#EXTIMG:{str(self.cover_path.absolute())}\n#PLAYLIST:{self.name}\n"
logger.info(
f"Creating .m3u8 playlist file for Playlist with ID '{self.playlist_id}'"
)
for d in self.files:
file: str = next(iter(d.values()))
if file is None:
continue
elif file.endswith(".flac"):
m = mutagen.File(file)
artist: str = m.get("artist", [""])[0]
title: str = m.get("title", [""])[0]
extinf: str = (
f"#EXTINF:{math.ceil(m.info.length)},"
f"{artist} - {title}\n{file}\n"
)
m3u_text += extinf
elif file.endswith(".mka"):
m = mutagen.File(file)
artist: str = m.get("ARTI", [""])[0]
title: str = m.get("TITL", [""])[0]
extinf: str = (
f"#EXTINF:{math.ceil(m.info.length)},"
f"{artist} - {title}\n{file}\n"
)
m3u_text += extinf
elif file.endswith(".m4a"):
# Mutagen cannot read .m4a files, so make a copy with all
# of the metadata tags as a .mp4 in a temporary directory | with temporary_file(suffix=".mp4") as tf: | 7 | 2023-12-12 21:50:25+00:00 | 16k |
Deltares/imod-python | imod/tests/fixtures/mf6_flow_with_transport_fixture.py | [
{
"identifier": "InitialConditions",
"path": "imod/mf6/ic.py",
"snippet": "class InitialConditions(Package):\n \"\"\"\n Initial Conditions (IC) Package information is read from the file that is\n specified by \"IC6\" as the file type. Only one IC Package can be specified\n for a GWF model.\n... | import numpy as np
import pytest
import xarray as xr
from imod.mf6 import (
GroundwaterFlowModel,
InitialConditions,
NodePropertyFlow,
OutputControl,
River,
SpecificStorage,
) | 12,564 | def conductance_fc():
globaltimes = np.array(
[
"2000-01-01",
"2000-01-02",
"2000-01-03",
],
dtype="datetime64[ns]",
)
idomain = get_data_array(grid_dimensions(), globaltimes)
# Constant head
conductance = xr.full_like(idomain, np.nan)
return conductance
@pytest.fixture(scope="session")
def elevation_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
elevation = xr.full_like(idomain, np.nan)
elevation[:, 0, 7, 7:9] = 1.0
return elevation
@pytest.fixture(scope="session")
def rate_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
rate = xr.full_like(idomain, np.nan)
rate[:, 0, 7, 7:9] = 0.001
return rate
@pytest.fixture(scope="session")
def proportion_rate_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
proportion_rate = xr.full_like(idomain, np.nan)
proportion_rate[:, 0, 7, 7:9] = 0.3
return proportion_rate
@pytest.fixture(scope="session")
def proportion_depth_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
proportion_depth = xr.full_like(idomain, np.nan)
proportion_depth[:, 0, 7, 7:9] = 0.4
return proportion_depth
@pytest.fixture(scope="session")
def porosity_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
porosity_fc = xr.full_like(idomain, np.nan).isel(time=0)
return porosity_fc
@pytest.fixture(scope="session")
def decay_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
decay_fc = xr.full_like(idomain, np.nan).isel(time=0)
return decay_fc
@pytest.fixture(scope="session")
def decay_sorbed_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
decay_sorbed_fc = xr.full_like(idomain, np.nan).isel(time=0)
return decay_sorbed_fc
@pytest.fixture(scope="session")
def bulk_density_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
bulk_density_fc = xr.full_like(idomain, np.nan).isel(time=0)
return bulk_density_fc
@pytest.fixture(scope="session")
def distcoef_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
distcoef_fc = xr.full_like(idomain, np.nan).isel(time=0)
return distcoef_fc
@pytest.fixture(scope="session")
def sp2_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
sp2_fc = xr.full_like(idomain, np.nan).isel(time=0)
return sp2_fc
@pytest.fixture(scope="function")
@pytest.mark.usefixtures("concentration_fc")
def flow_model_with_concentration(concentration_fc):
idomain = get_data_array(grid_dimensions(), globaltimes)
cellType = xr.full_like(idomain.isel(time=0), 1, dtype=np.int32)
k = xr.full_like(idomain.isel(time=0), 10.0)
k33 = xr.full_like(idomain.isel(time=0), 10.0)
# River
riv_dict = dict(
stage=idomain.sel(layer=1),
conductance=idomain.sel(layer=1),
bottom_elevation=idomain.sel(layer=1) - 1.0,
concentration=concentration_fc.sel(layer=1),
)
gwf_model = GroundwaterFlowModel()
|
globaltimes = np.array(
[
"2000-01-01",
"2000-01-02",
"2000-01-03",
],
dtype="datetime64[ns]",
)
class grid_dimensions:
nlay = 3
nrow = 15
ncol = 15
dx = 5000
dy = -5000
xmin = 0
ymin = 0
def get_data_array(dimensions, globaltimes):
ntimes = len(globaltimes)
shape = (ntimes, dimensions.nlay, dimensions.nrow, dimensions.ncol)
dims = ("time", "layer", "y", "x")
layer = np.array([1, 2, 3])
xmax = dimensions.dx * dimensions.ncol
ymax = abs(dimensions.dy) * dimensions.nrow
y = np.arange(ymax, dimensions.ymin, dimensions.dy) + 0.5 * dimensions.dy
x = np.arange(dimensions.xmin, xmax, dimensions.dx) + 0.5 * dimensions.dx
coords = {"time": globaltimes, "layer": layer, "y": y, "x": x}
# Discretization data
return xr.DataArray(
np.ones(shape),
coords=coords,
dims=dims,
)
@pytest.fixture(scope="session")
def head_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
# Constant head
head = xr.full_like(idomain, np.nan)
return head
@pytest.fixture(scope="session")
def concentration_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
idomain = idomain.expand_dims(species=["salinity", "temperature"])
concentration = xr.full_like(idomain, np.nan)
return concentration
@pytest.fixture(scope="session")
def conductance_fc():
globaltimes = np.array(
[
"2000-01-01",
"2000-01-02",
"2000-01-03",
],
dtype="datetime64[ns]",
)
idomain = get_data_array(grid_dimensions(), globaltimes)
# Constant head
conductance = xr.full_like(idomain, np.nan)
return conductance
@pytest.fixture(scope="session")
def elevation_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
elevation = xr.full_like(idomain, np.nan)
elevation[:, 0, 7, 7:9] = 1.0
return elevation
@pytest.fixture(scope="session")
def rate_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
rate = xr.full_like(idomain, np.nan)
rate[:, 0, 7, 7:9] = 0.001
return rate
@pytest.fixture(scope="session")
def proportion_rate_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
proportion_rate = xr.full_like(idomain, np.nan)
proportion_rate[:, 0, 7, 7:9] = 0.3
return proportion_rate
@pytest.fixture(scope="session")
def proportion_depth_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
proportion_depth = xr.full_like(idomain, np.nan)
proportion_depth[:, 0, 7, 7:9] = 0.4
return proportion_depth
@pytest.fixture(scope="session")
def porosity_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
porosity_fc = xr.full_like(idomain, np.nan).isel(time=0)
return porosity_fc
@pytest.fixture(scope="session")
def decay_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
decay_fc = xr.full_like(idomain, np.nan).isel(time=0)
return decay_fc
@pytest.fixture(scope="session")
def decay_sorbed_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
decay_sorbed_fc = xr.full_like(idomain, np.nan).isel(time=0)
return decay_sorbed_fc
@pytest.fixture(scope="session")
def bulk_density_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
bulk_density_fc = xr.full_like(idomain, np.nan).isel(time=0)
return bulk_density_fc
@pytest.fixture(scope="session")
def distcoef_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
distcoef_fc = xr.full_like(idomain, np.nan).isel(time=0)
return distcoef_fc
@pytest.fixture(scope="session")
def sp2_fc():
idomain = get_data_array(grid_dimensions(), globaltimes)
sp2_fc = xr.full_like(idomain, np.nan).isel(time=0)
return sp2_fc
@pytest.fixture(scope="function")
@pytest.mark.usefixtures("concentration_fc")
def flow_model_with_concentration(concentration_fc):
idomain = get_data_array(grid_dimensions(), globaltimes)
cellType = xr.full_like(idomain.isel(time=0), 1, dtype=np.int32)
k = xr.full_like(idomain.isel(time=0), 10.0)
k33 = xr.full_like(idomain.isel(time=0), 10.0)
# River
riv_dict = dict(
stage=idomain.sel(layer=1),
conductance=idomain.sel(layer=1),
bottom_elevation=idomain.sel(layer=1) - 1.0,
concentration=concentration_fc.sel(layer=1),
)
gwf_model = GroundwaterFlowModel()
| gwf_model["npf"] = NodePropertyFlow( | 2 | 2023-12-08 13:57:59+00:00 | 16k |
camenduru/MotionDirector-hf | MotionDirector_train.py | [
{
"identifier": "UNet3DConditionModel",
"path": "models/unet_3d_condition.py",
"snippet": "class UNet3DConditionModel(ModelMixin, ConfigMixin):\n r\"\"\"\n UNet3DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep\n and returns sample sh... | import argparse
import datetime
import logging
import inspect
import math
import os
import random
import gc
import copy
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import diffusers
import transformers
import imageio
import numpy as np
import itertools
import bitsandbytes as bnb
from typing import Dict, Optional, Tuple
from omegaconf import OmegaConf
from torchvision import transforms
from tqdm.auto import tqdm
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from models.unet_3d_condition import UNet3DConditionModel
from diffusers.models import AutoencoderKL
from diffusers import DDIMScheduler, TextToVideoSDPipeline
from diffusers.optimization import get_scheduler
from diffusers.utils.import_utils import is_xformers_available
from diffusers.models.attention_processor import AttnProcessor2_0, Attention
from diffusers.models.attention import BasicTransformerBlock
from transformers import CLIPTextModel, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPEncoder
from utils.dataset import VideoJsonDataset, SingleVideoDataset, \
ImageDataset, VideoFolderDataset, CachedDataset
from einops import rearrange, repeat
from utils.lora_handler import LoraHandler
from utils.lora import extract_lora_child_module
from utils.ddim_utils import ddim_inversion
from xformers.ops import MemoryEfficientAttentionFlashAttentionOp | 11,373 |
already_printed_trainables = False
logger = get_logger(__name__, log_level="INFO")
def create_logging(logging, logger, accelerator):
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
def accelerate_set_verbose(accelerator):
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
def get_train_dataset(dataset_types, train_data, tokenizer):
train_datasets = []
# Loop through all available datasets, get the name, then add to list of data to process.
|
already_printed_trainables = False
logger = get_logger(__name__, log_level="INFO")
def create_logging(logging, logger, accelerator):
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
def accelerate_set_verbose(accelerator):
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
def get_train_dataset(dataset_types, train_data, tokenizer):
train_datasets = []
# Loop through all available datasets, get the name, then add to list of data to process. | for DataSet in [VideoJsonDataset, SingleVideoDataset, ImageDataset, VideoFolderDataset]: | 2 | 2023-12-11 04:51:39+00:00 | 16k |
ZS-YANG/FemtoDet-v3 | mmdet/models/dense_heads/atss_vlfusion_head.py | [
{
"identifier": "MODELS",
"path": "mmdet/registry.py",
"snippet": "MODELS = Registry('model', parent=MMENGINE_MODELS, locations=['mmdet.models'])"
},
{
"identifier": "cat_boxes",
"path": "mmdet/structures/bbox/transforms.py",
"snippet": "def cat_boxes(data_list: List[Union[Tensor, BaseBo... | import copy
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Callable, List, Optional, Sequence, Tuple, Union
from mmcv.cnn import Scale
from mmcv.ops.modulated_deform_conv import ModulatedDeformConv2d
from mmengine.config import ConfigDict
from mmengine.model import BaseModel
from mmengine.structures import InstanceData
from torch import Tensor
from transformers import BertConfig
from mmdet.registry import MODELS
from mmdet.structures.bbox import cat_boxes
from mmdet.utils import InstanceList, OptInstanceList, reduce_mean
from ..utils import (BertEncoderLayer, VLFuse, filter_scores_and_topk,
permute_and_flatten, select_single_mlvl,
unpack_gt_instances)
from ..utils.vlfuse_helper import MAX_CLAMP_VALUE
from .atss_head import ATSSHead | 10,959 | self.dot_product_projection_text = nn.Linear(
self.lang_dim,
self.num_base_priors * self.feat_channels,
bias=True)
self.log_scale = nn.Parameter(torch.Tensor([0.0]), requires_grad=True)
self.bias_lang = nn.Parameter(
torch.zeros(self.lang_dim), requires_grad=True)
self.bias0 = nn.Parameter(
torch.Tensor([bias_value]), requires_grad=True)
self.scales = nn.ModuleList([Scale(1.0) for _ in range(5)])
def forward(self, visual_feats: Tuple[Tensor],
language_feats: dict) -> Tuple:
feat_inputs = {'visual': visual_feats, 'lang': language_feats}
dyhead_tower = self.dyhead_tower(feat_inputs)
if self.early_fuse:
embedding = dyhead_tower['lang']['hidden']
else:
embedding = language_feats['embedded']
embedding = F.normalize(embedding, p=2, dim=-1)
dot_product_proj_tokens = self.dot_product_projection_text(embedding /
2.0)
dot_product_proj_tokens_bias = torch.matmul(
embedding, self.bias_lang) + self.bias0
bbox_preds = []
centerness = []
cls_logits = []
for i, feature in enumerate(visual_feats):
visual = dyhead_tower['visual'][i]
B, C, H, W = visual.shape
bbox_pred = self.scales[i](self.bbox_pred(visual))
bbox_preds.append(bbox_pred)
centerness.append(self.centerness(visual))
dot_product_proj_queries = permute_and_flatten(
visual, B, self.num_base_priors, C, H, W)
bias = dot_product_proj_tokens_bias.unsqueeze(1).repeat(
1, self.num_base_priors, 1)
dot_product_logit = (
torch.matmul(dot_product_proj_queries,
dot_product_proj_tokens.transpose(-1, -2)) /
self.log_scale.exp()) + bias
dot_product_logit = torch.clamp(
dot_product_logit, max=MAX_CLAMP_VALUE)
dot_product_logit = torch.clamp(
dot_product_logit, min=-MAX_CLAMP_VALUE)
cls_logits.append(dot_product_logit)
return bbox_preds, centerness, cls_logits
@MODELS.register_module()
class ATSSVLFusionHead(ATSSHead):
"""ATSS head with visual-language fusion module.
Args:
early_fuse (bool): Whether to fuse visual and language features
Defaults to False.
use_checkpoint (bool): Whether to use checkpoint. Defaults to False.
num_dyhead_blocks (int): Number of dynamic head blocks. Defaults to 6.
lang_model_name (str): Name of the language model.
Defaults to 'bert-base-uncased'.
"""
def __init__(self,
*args,
early_fuse: bool = False,
use_checkpoint: bool = False,
num_dyhead_blocks: int = 6,
lang_model_name: str = 'bert-base-uncased',
init_cfg=None,
**kwargs):
super().__init__(*args, **kwargs, init_cfg=init_cfg)
self.head = VLFusionModule(
in_channels=self.in_channels,
feat_channels=self.feat_channels,
num_base_priors=self.num_base_priors,
early_fuse=early_fuse,
use_checkpoint=use_checkpoint,
num_dyhead_blocks=num_dyhead_blocks,
lang_model_name=lang_model_name)
self.text_masks = None
def _init_layers(self) -> None:
"""No need to initialize the ATSS head layer."""
pass
def forward(self, visual_feats: Tuple[Tensor],
language_feats: dict) -> Tuple[Tensor]:
"""Forward function."""
bbox_preds, centerness, cls_logits = self.head(visual_feats,
language_feats)
return cls_logits, bbox_preds, centerness
def loss(self, visual_feats: Tuple[Tensor], language_feats: dict,
batch_data_samples):
outputs = unpack_gt_instances(batch_data_samples)
(batch_gt_instances, batch_gt_instances_ignore,
batch_img_metas) = outputs
outs = self(visual_feats, language_feats)
self.text_masks = language_feats['masks']
loss_inputs = outs + (batch_gt_instances, batch_img_metas,
batch_gt_instances_ignore)
losses = self.loss_by_feat(*loss_inputs)
return losses
def loss_by_feat(
self,
cls_scores: List[Tensor],
bbox_preds: List[Tensor],
centernesses: List[Tensor],
batch_gt_instances: InstanceList,
batch_img_metas: List[dict],
| # Copyright (c) OpenMMLab. All rights reserved.
try:
except ImportError:
BertConfig = None
def convert_grounding_to_cls_scores(logits: Tensor,
positive_maps: List[dict]) -> Tensor:
"""Convert logits to class scores."""
assert len(positive_maps) == logits.shape[0] # batch size
scores = torch.zeros(logits.shape[0], logits.shape[1],
len(positive_maps[0])).to(logits.device)
if positive_maps is not None:
if all(x == positive_maps[0] for x in positive_maps):
# only need to compute once
positive_map = positive_maps[0]
for label_j in positive_map:
scores[:, :, label_j -
1] = logits[:, :,
torch.LongTensor(positive_map[label_j]
)].mean(-1)
else:
for i, positive_map in enumerate(positive_maps):
for label_j in positive_map:
scores[i, :, label_j - 1] = logits[
i, :, torch.LongTensor(positive_map[label_j])].mean(-1)
return scores
class Conv3x3Norm(nn.Module):
"""Conv3x3 and norm."""
def __init__(self,
in_channels: int,
out_channels: int,
stride: int,
groups: int = 1,
use_dcn: bool = False,
norm_type: Optional[Union[Sequence, str]] = None):
super().__init__()
if use_dcn:
self.conv = ModulatedDeformConv2d(
in_channels,
out_channels,
kernel_size=3,
stride=stride,
padding=1,
groups=groups)
else:
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size=3,
stride=stride,
padding=1,
groups=groups)
if isinstance(norm_type, Sequence):
assert len(norm_type) == 2
assert norm_type[0] == 'gn'
gn_group = norm_type[1]
norm_type = norm_type[0]
if norm_type == 'bn':
bn_op = nn.BatchNorm2d(out_channels)
elif norm_type == 'gn':
bn_op = nn.GroupNorm(
num_groups=gn_group, num_channels=out_channels)
if norm_type is not None:
self.bn = bn_op
else:
self.bn = None
def forward(self, x, **kwargs):
x = self.conv(x, **kwargs)
if self.bn:
x = self.bn(x)
return x
class DyReLU(nn.Module):
"""Dynamic ReLU."""
def __init__(self,
in_channels: int,
out_channels: int,
expand_ratio: int = 4):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.expand_ratio = expand_ratio
self.out_channels = out_channels
self.fc = nn.Sequential(
nn.Linear(in_channels, in_channels // expand_ratio),
nn.ReLU(inplace=True),
nn.Linear(in_channels // expand_ratio,
out_channels * self.expand_ratio),
nn.Hardsigmoid(inplace=True))
def forward(self, x) -> Tensor:
x_out = x
b, c, h, w = x.size()
x = self.avg_pool(x).view(b, c)
x = self.fc(x).view(b, -1, 1, 1)
a1, b1, a2, b2 = torch.split(x, self.out_channels, dim=1)
a1 = (a1 - 0.5) * 2 + 1.0
a2 = (a2 - 0.5) * 2
b1 = b1 - 0.5
b2 = b2 - 0.5
out = torch.max(x_out * a1 + b1, x_out * a2 + b2)
return out
class DyConv(nn.Module):
"""Dynamic Convolution."""
def __init__(self,
conv_func: Callable,
in_channels: int,
out_channels: int,
use_dyfuse: bool = True,
use_dyrelu: bool = False,
use_dcn: bool = False):
super().__init__()
self.dyconvs = nn.ModuleList()
self.dyconvs.append(conv_func(in_channels, out_channels, 1))
self.dyconvs.append(conv_func(in_channels, out_channels, 1))
self.dyconvs.append(conv_func(in_channels, out_channels, 2))
if use_dyfuse:
self.attnconv = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels, 1, kernel_size=1),
nn.ReLU(inplace=True))
self.h_sigmoid = nn.Hardsigmoid(inplace=True)
else:
self.attnconv = None
if use_dyrelu:
self.relu = DyReLU(in_channels, out_channels)
else:
self.relu = nn.ReLU()
if use_dcn:
self.offset = nn.Conv2d(
in_channels, 27, kernel_size=3, stride=1, padding=1)
else:
self.offset = None
self.init_weights()
def init_weights(self):
for m in self.dyconvs.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight.data, 0, 0.01)
if m.bias is not None:
m.bias.data.zero_()
if self.attnconv is not None:
for m in self.attnconv.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight.data, 0, 0.01)
if m.bias is not None:
m.bias.data.zero_()
def forward(self, inputs: dict) -> dict:
visual_feats = inputs['visual']
out_vis_feats = []
for level, feature in enumerate(visual_feats):
offset_conv_args = {}
if self.offset is not None:
offset_mask = self.offset(feature)
offset = offset_mask[:, :18, :, :]
mask = offset_mask[:, 18:, :, :].sigmoid()
offset_conv_args = dict(offset=offset, mask=mask)
temp_feats = [self.dyconvs[1](feature, **offset_conv_args)]
if level > 0:
temp_feats.append(self.dyconvs[2](visual_feats[level - 1],
**offset_conv_args))
if level < len(visual_feats) - 1:
temp_feats.append(
F.upsample_bilinear(
self.dyconvs[0](visual_feats[level + 1],
**offset_conv_args),
size=[feature.size(2),
feature.size(3)]))
mean_feats = torch.mean(
torch.stack(temp_feats), dim=0, keepdim=False)
if self.attnconv is not None:
attn_feat = []
res_feat = []
for feat in temp_feats:
res_feat.append(feat)
attn_feat.append(self.attnconv(feat))
res_feat = torch.stack(res_feat)
spa_pyr_attn = self.h_sigmoid(torch.stack(attn_feat))
mean_feats = torch.mean(
res_feat * spa_pyr_attn, dim=0, keepdim=False)
out_vis_feats.append(mean_feats)
out_vis_feats = [self.relu(item) for item in out_vis_feats]
features_dict = {'visual': out_vis_feats, 'lang': inputs['lang']}
return features_dict
class VLFusionModule(BaseModel):
"""Visual-lang Fusion Module."""
def __init__(self,
in_channels: int,
feat_channels: int,
num_base_priors: int,
early_fuse: bool = False,
num_dyhead_blocks: int = 6,
lang_model_name: str = 'bert-base-uncased',
use_dyrelu: bool = True,
use_dyfuse: bool = True,
use_dcn: bool = True,
use_checkpoint: bool = False,
**kwargs) -> None:
super().__init__(**kwargs)
if BertConfig is None:
raise RuntimeError(
'transformers is not installed, please install it by: '
'pip install transformers.')
self.in_channels = in_channels
self.feat_channels = feat_channels
self.num_base_priors = num_base_priors
self.early_fuse = early_fuse
self.num_dyhead_blocks = num_dyhead_blocks
self.use_dyrelu = use_dyrelu
self.use_dyfuse = use_dyfuse
self.use_dcn = use_dcn
self.use_checkpoint = use_checkpoint
self.lang_cfg = BertConfig.from_pretrained(lang_model_name)
self.lang_dim = self.lang_cfg.hidden_size
self._init_layers()
def _init_layers(self) -> None:
"""Initialize layers of the model."""
bias_value = -math.log((1 - 0.01) / 0.01)
dyhead_tower = []
for i in range(self.num_dyhead_blocks):
if self.early_fuse:
# cross-modality fusion
dyhead_tower.append(VLFuse(use_checkpoint=self.use_checkpoint))
# lang branch
dyhead_tower.append(
BertEncoderLayer(
self.lang_cfg,
clamp_min_for_underflow=True,
clamp_max_for_overflow=True))
# vision branch
dyhead_tower.append(
DyConv(
lambda i, o, s: Conv3x3Norm(
i, o, s, use_dcn=self.use_dcn, norm_type=['gn', 16]),
self.in_channels if i == 0 else self.feat_channels,
self.feat_channels,
use_dyrelu=(self.use_dyrelu
and self.in_channels == self.feat_channels)
if i == 0 else self.use_dyrelu,
use_dyfuse=(self.use_dyfuse
and self.in_channels == self.feat_channels)
if i == 0 else self.use_dyfuse,
use_dcn=(self.use_dcn
and self.in_channels == self.feat_channels)
if i == 0 else self.use_dcn,
))
self.add_module('dyhead_tower', nn.Sequential(*dyhead_tower))
self.bbox_pred = nn.Conv2d(
self.feat_channels, self.num_base_priors * 4, kernel_size=1)
self.centerness = nn.Conv2d(
self.feat_channels, self.num_base_priors * 1, kernel_size=1)
self.dot_product_projection_text = nn.Linear(
self.lang_dim,
self.num_base_priors * self.feat_channels,
bias=True)
self.log_scale = nn.Parameter(torch.Tensor([0.0]), requires_grad=True)
self.bias_lang = nn.Parameter(
torch.zeros(self.lang_dim), requires_grad=True)
self.bias0 = nn.Parameter(
torch.Tensor([bias_value]), requires_grad=True)
self.scales = nn.ModuleList([Scale(1.0) for _ in range(5)])
def forward(self, visual_feats: Tuple[Tensor],
language_feats: dict) -> Tuple:
feat_inputs = {'visual': visual_feats, 'lang': language_feats}
dyhead_tower = self.dyhead_tower(feat_inputs)
if self.early_fuse:
embedding = dyhead_tower['lang']['hidden']
else:
embedding = language_feats['embedded']
embedding = F.normalize(embedding, p=2, dim=-1)
dot_product_proj_tokens = self.dot_product_projection_text(embedding /
2.0)
dot_product_proj_tokens_bias = torch.matmul(
embedding, self.bias_lang) + self.bias0
bbox_preds = []
centerness = []
cls_logits = []
for i, feature in enumerate(visual_feats):
visual = dyhead_tower['visual'][i]
B, C, H, W = visual.shape
bbox_pred = self.scales[i](self.bbox_pred(visual))
bbox_preds.append(bbox_pred)
centerness.append(self.centerness(visual))
dot_product_proj_queries = permute_and_flatten(
visual, B, self.num_base_priors, C, H, W)
bias = dot_product_proj_tokens_bias.unsqueeze(1).repeat(
1, self.num_base_priors, 1)
dot_product_logit = (
torch.matmul(dot_product_proj_queries,
dot_product_proj_tokens.transpose(-1, -2)) /
self.log_scale.exp()) + bias
dot_product_logit = torch.clamp(
dot_product_logit, max=MAX_CLAMP_VALUE)
dot_product_logit = torch.clamp(
dot_product_logit, min=-MAX_CLAMP_VALUE)
cls_logits.append(dot_product_logit)
return bbox_preds, centerness, cls_logits
@MODELS.register_module()
class ATSSVLFusionHead(ATSSHead):
"""ATSS head with visual-language fusion module.
Args:
early_fuse (bool): Whether to fuse visual and language features
Defaults to False.
use_checkpoint (bool): Whether to use checkpoint. Defaults to False.
num_dyhead_blocks (int): Number of dynamic head blocks. Defaults to 6.
lang_model_name (str): Name of the language model.
Defaults to 'bert-base-uncased'.
"""
def __init__(self,
*args,
early_fuse: bool = False,
use_checkpoint: bool = False,
num_dyhead_blocks: int = 6,
lang_model_name: str = 'bert-base-uncased',
init_cfg=None,
**kwargs):
super().__init__(*args, **kwargs, init_cfg=init_cfg)
self.head = VLFusionModule(
in_channels=self.in_channels,
feat_channels=self.feat_channels,
num_base_priors=self.num_base_priors,
early_fuse=early_fuse,
use_checkpoint=use_checkpoint,
num_dyhead_blocks=num_dyhead_blocks,
lang_model_name=lang_model_name)
self.text_masks = None
def _init_layers(self) -> None:
"""No need to initialize the ATSS head layer."""
pass
def forward(self, visual_feats: Tuple[Tensor],
language_feats: dict) -> Tuple[Tensor]:
"""Forward function."""
bbox_preds, centerness, cls_logits = self.head(visual_feats,
language_feats)
return cls_logits, bbox_preds, centerness
def loss(self, visual_feats: Tuple[Tensor], language_feats: dict,
batch_data_samples):
outputs = unpack_gt_instances(batch_data_samples)
(batch_gt_instances, batch_gt_instances_ignore,
batch_img_metas) = outputs
outs = self(visual_feats, language_feats)
self.text_masks = language_feats['masks']
loss_inputs = outs + (batch_gt_instances, batch_img_metas,
batch_gt_instances_ignore)
losses = self.loss_by_feat(*loss_inputs)
return losses
def loss_by_feat(
self,
cls_scores: List[Tensor],
bbox_preds: List[Tensor],
centernesses: List[Tensor],
batch_gt_instances: InstanceList,
batch_img_metas: List[dict], | batch_gt_instances_ignore: OptInstanceList = None) -> dict: | 3 | 2023-12-11 15:23:03+00:00 | 16k |
merlresearch/PixPNet | pixpnet/protonets/lit_model.py | [
{
"identifier": "get_metadata",
"path": "pixpnet/data.py",
"snippet": "def get_metadata(config):\n dataset = config.dataset.name.upper().replace(\"-\", \"\")\n metadata = DatasetMeta(\n output_size=DATA_NUM_OUTPUTS[dataset],\n input_channels=DATA_CHANNELS[dataset],\n input_siz... | import argparse
import torch
from typing import Tuple
from torch import nn
from torchmetrics import Accuracy
from pytorch_lightning.loops import FitLoop
from pytorch_lightning.loops.fit_loop import _FitLoop as FitLoop
from pytorch_lightning import LightningModule, Trainer
from pytorch_lightning.loggers import TensorBoardLogger
from pixpnet.data import get_metadata
from pixpnet.lightning.lightning_data import LitData
from pixpnet.lightning.lit_module import BaseLitModel
from pixpnet.optim import get_optimizer_cls, get_scheduler
from pixpnet.protonets.loss import ClusterLoss, L1ReadoutLoss, SeparationLoss
from pixpnet.protonets.models.protonet import ProtoNet, protonet
from pixpnet.protonets.push import push_prototypes
from pixpnet.utils import get_logger, intersect_func_and_kwargs | 10,856 | # Copyright (c) 2022-2023 Mitsubishi Electric Research Laboratories (MERL)
#
# SPDX-License-Identifier: AGPL-3.0-or-later
try:
except ImportError:
logger = get_logger(__name__)
def params_with_grad(parameters):
return filter(lambda p: p.requires_grad, parameters)
def make_optimizers_proto(
model: ProtoNet,
config: argparse.Namespace,
) -> Tuple[torch.optim.Optimizer, ...]:
""""""
| # Copyright (c) 2022-2023 Mitsubishi Electric Research Laboratories (MERL)
#
# SPDX-License-Identifier: AGPL-3.0-or-later
try:
except ImportError:
logger = get_logger(__name__)
def params_with_grad(parameters):
return filter(lambda p: p.requires_grad, parameters)
def make_optimizers_proto(
model: ProtoNet,
config: argparse.Namespace,
) -> Tuple[torch.optim.Optimizer, ...]:
"""""" | optimizer_cls, hparams = get_optimizer_cls(config, ignore={"fine_tune_lr", "readout_lr"}) | 3 | 2023-12-06 23:49:31+00:00 | 16k |
open-mmlab/PIA | animatediff/pipelines/i2v_pipeline.py | [
{
"identifier": "InflatedConv3d",
"path": "animatediff/models/resnet.py",
"snippet": "class InflatedConv3d(nn.Conv2d):\n def forward(self, x):\n video_length = x.shape[2]\n\n x = rearrange(x, \"b c f h w -> (b f) c h w\")\n x = super().forward(x)\n x = rearrange(x, \"(b f)... | import inspect
import os.path as osp
import numpy as np
import torch
from dataclasses import dataclass
from typing import Callable, List, Optional, Union
from diffusers.configuration_utils import FrozenDict
from diffusers.loaders import IPAdapterMixin, TextualInversionLoaderMixin
from diffusers.models import AutoencoderKL
from diffusers.pipelines import DiffusionPipeline
from diffusers.schedulers import (DDIMScheduler, DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler, LMSDiscreteScheduler,
PNDMScheduler)
from diffusers.utils import (BaseOutput, deprecate, is_accelerate_available,
logging)
from diffusers.utils.import_utils import is_xformers_available
from einops import rearrange
from omegaconf import OmegaConf
from packaging import version
from safetensors import safe_open
from tqdm import tqdm
from transformers import (CLIPImageProcessor, CLIPTextModel, CLIPTokenizer,
CLIPVisionModelWithProjection)
from animatediff.models.resnet import InflatedConv3d
from animatediff.models.unet import UNet3DConditionModel
from animatediff.utils.convert_from_ckpt import (convert_ldm_clip_checkpoint,
convert_ldm_unet_checkpoint,
convert_ldm_vae_checkpoint)
from animatediff.utils.convert_lora_safetensor_to_diffusers import \
convert_lora_model_level
from animatediff.utils.util import prepare_mask_coef_by_statistics
from accelerate import cpu_offload | 11,990 | # Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
DEFAULT_N_PROMPT = ('wrong white balance, dark, sketches,worst quality,'
'low quality, deformed, distorted, disfigured, bad eyes, '
'wrong lips,weird mouth, bad teeth, mutated hands and fingers, '
'bad anatomy,wrong anatomy, amputation, extra limb, '
'missing limb, floating,limbs, disconnected limbs, mutation, '
'ugly, disgusting, bad_pictures, negative_hand-neg')
@dataclass
class AnimationPipelineOutput(BaseOutput):
videos: Union[torch.Tensor, np.ndarray]
class I2VPipeline(DiffusionPipeline, IPAdapterMixin, TextualInversionLoaderMixin):
_optional_components = []
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
| # Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
DEFAULT_N_PROMPT = ('wrong white balance, dark, sketches,worst quality,'
'low quality, deformed, distorted, disfigured, bad eyes, '
'wrong lips,weird mouth, bad teeth, mutated hands and fingers, '
'bad anatomy,wrong anatomy, amputation, extra limb, '
'missing limb, floating,limbs, disconnected limbs, mutation, '
'ugly, disgusting, bad_pictures, negative_hand-neg')
@dataclass
class AnimationPipelineOutput(BaseOutput):
videos: Union[torch.Tensor, np.ndarray]
class I2VPipeline(DiffusionPipeline, IPAdapterMixin, TextualInversionLoaderMixin):
_optional_components = []
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer, | unet: UNet3DConditionModel, | 1 | 2023-12-21 03:29:34+00:00 | 16k |
xinghaochen/TinySAM | tinysam/hierarchical_mask_generator.py | [
{
"identifier": "Sam",
"path": "tinysam/modeling/sam.py",
"snippet": "class Sam(nn.Module):\n mask_threshold: float = 0.0\n image_format: str = \"RGB\"\n\n def __init__(\n self,\n image_encoder: Union[ImageEncoderViT, TinyViT],\n prompt_encoder: PromptEncoder,\n mask... | import numpy as np
import torch
import cv2 # type: ignore # noqa: F401
from torchvision.ops.boxes import batched_nms, box_area # type: ignore
from typing import Any, Dict, List, Optional, Tuple
from .modeling import Sam
from .predictor import SamPredictor
from .utils.amg import (
MaskData,
area_from_rle,
batch_iterator,
batched_mask_to_box,
box_xyxy_to_xywh,
build_all_layer_point_grids,
calculate_stability_score,
coco_encode_rle,
generate_crop_boxes,
is_box_near_crop_edge,
mask_to_rle_pytorch,
remove_small_regions,
rle_to_mask,
uncrop_boxes_xyxy,
uncrop_masks,
uncrop_points,
)
from pycocotools import mask as mask_utils # type: ignore # noqa: F401 | 10,985 | for sx in [-1, 0, 1]:
if or_results[int(point_coords[0] + wstride * 2), int(point_coords[1] + hstride * sx)]:
continue
new_points.append([(point_coords[0] + wstride * 2) / iw, (point_coords[1] + hstride * sx) / ih])
if point_coords[1] + hstride * 2 < ih:
for sy in [-1, 0, 1]:
if or_results[int(point_coords[0] + wstride * sy), int(point_coords[1] + hstride * 2)]:
continue
new_points.append([(point_coords[0] + wstride * sy) / iw, (point_coords[1] + hstride * 2) / ih])
if point_coords[0] + wstride * 2 < iw and point_coords[1] + hstride * 2 < ih:
if or_results[int(point_coords[0] + wstride * 2), int(point_coords[1] + hstride * 2)]:
continue
new_points.append([(point_coords[0] + wstride * 2) / iw, (point_coords[1] + hstride * 2) / ih])
self.set_point_grids([np.array(new_points)])
new_masks = self.generate(image, False)
new_masks.cat(ori_masks)
new_masks = self.post_process(image, new_masks)
return new_masks
@torch.no_grad()
def generate(self, image: np.ndarray, need_high: bool) -> MaskData:
orig_size = image.shape[:2]
# Get points for this crop
points_scale = np.array(orig_size)[None, ::-1]
points_for_image = self.point_grids[0] * points_scale
# Generate masks for this crop in batches
data = MaskData()
for (points,) in batch_iterator(self.points_per_batch, points_for_image):
orig_h, orig_w = orig_size
# Run model on this batch
transformed_points = self.predictor.transform.apply_coords(points, orig_size)
in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
masks, iou_preds, _ = self.predictor.predict_torch(
in_points[:, None, :],
in_labels[:, None],
return_logits=True,
)
# Serialize predictions and store in MaskData
batch_data = MaskData(
masks=masks.flatten(0, 1),
iou_preds=iou_preds.flatten(0, 1),
points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
)
del masks
if self.pred_iou_thresh > 0.0:
keep_mask = batch_data["iou_preds"] > self.pred_iou_thresh
batch_data.filter(keep_mask)
# Calculate stability score
batch_data["stability_score"] = calculate_stability_score(
batch_data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
)
if self.stability_score_thresh > 0.0:
keep_mask = batch_data["stability_score"] >= self.stability_score_thresh
batch_data.filter(keep_mask)
if need_high:
batch_data["high_masks"] = batch_data["masks"] > self.high_score_thresh
batch_data["masks"] = batch_data["masks"] > self.predictor.model.mask_threshold
batch_data["boxes"] = batched_mask_to_box(batch_data["masks"])
keep_mask = ~is_box_near_crop_edge(batch_data["boxes"], [0, 0, orig_w, orig_h], [0, 0, orig_w, orig_h])
if not torch.all(keep_mask):
batch_data.filter(keep_mask)
# Compress to RLE
batch_data["rles"] = mask_to_rle_pytorch(batch_data["masks"])
data.cat(batch_data)
del batch_data
if need_high:
high_masks = data["high_masks"]
or_results = torch.zeros([high_masks.shape[1], high_masks.shape[2]]).to(high_masks.device)
for mask in high_masks:
or_results = torch.logical_or(or_results, mask)
del data["high_masks"]
or_results = or_results.permute(1, 0)
del data['masks']
return data, or_results
else:
del data['masks']
return data
@torch.no_grad()
def reset_image(self):
self.predictor.reset_image()
@torch.no_grad()
def post_process(self, image: np.ndarray, data: MaskData) -> List[Dict[str, Any]]:
orig_size = image.shape[:2]
orig_h, orig_w = orig_size
keep_by_nms = batched_nms(
data["boxes"].float(),
data["iou_preds"],
torch.zeros_like(data["boxes"][:, 0]), # categories
iou_threshold=self.box_nms_thresh,
)
data.filter(keep_by_nms)
# Filter small disconnected regions and holes in masks
if self.min_mask_region_area > 0:
data = self.postprocess_small_regions(
data,
self.min_mask_region_area,
max(self.box_nms_thresh, self.crop_nms_thresh),
)
# Encode masks
if self.output_mode == "coco_rle":
data["segmentations"] = [coco_encode_rle(rle) for rle in data["rles"]]
elif self.output_mode == "binary_mask":
| # Copyright 2023 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
class SamHierarchicalMaskGenerator:
def __init__(
self,
model: Sam,
points_per_side: Optional[int] = 32,
points_per_batch: int = 64,
pred_iou_thresh: float = 0.88,
high_score_thresh: float = 8.5,
stability_score_thresh: float = 0.95,
stability_score_offset: float = 1.0,
box_nms_thresh: float = 0.7,
crop_n_layers: int = 0,
crop_nms_thresh: float = 0.7,
crop_overlap_ratio: float = 512 / 1500,
crop_n_points_downscale_factor: int = 1,
point_grids: Optional[List[np.ndarray]] = None,
min_mask_region_area: int = 0,
output_mode: str = "binary_mask",
) -> None:
"""
Using a SAM model, generates masks for the entire image.
Generates a grid of point prompts over the image, then filters
low quality and duplicate masks. The default settings are chosen
for SAM with a ViT-H backbone.
Arguments:
model (Sam): The SAM model to use for mask prediction.
points_per_side (int or None): The number of points to be sampled
along one side of the image. The total number of points is
points_per_side**2. If None, 'point_grids' must provide explicit
point sampling.
points_per_batch (int): Sets the number of points run simultaneously
by the model. Higher numbers may be faster but use more GPU memory.
pred_iou_thresh (float): A filtering threshold in [0,1], using the
model's predicted mask quality.
high_score_thresh (float): A filtering threshold in [-inf,inf], to find out
the unmasked area for the next generation.
stability_score_thresh (float): A filtering threshold in [0,1], using
the stability of the mask under changes to the cutoff used to binarize
the model's mask predictions.
stability_score_offset (float): The amount to shift the cutoff when
calculated the stability score.
box_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks.
crop_n_layers (int): If >0, mask prediction will be run again on
crops of the image. Sets the number of layers to run, where each
layer has 2**i_layer number of image crops.
crop_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks between different crops.
crop_overlap_ratio (float): Sets the degree to which crops overlap.
In the first crop layer, crops will overlap by this fraction of
the image length. Later layers with more crops scale down this overlap.
crop_n_points_downscale_factor (int): The number of points-per-side
sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
point_grids (list(np.ndarray) or None): A list over explicit grids
of points used for sampling, normalized to [0,1]. The nth grid in the
list is used in the nth crop layer. Exclusive with points_per_side.
min_mask_region_area (int): If >0, postprocessing will be applied
to remove disconnected regions and holes in masks with area smaller
than min_mask_region_area. Requires opencv.
output_mode (str): The form masks are returned in. Can be 'binary_mask',
'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
For large resolutions, 'binary_mask' may consume large amounts of
memory.
"""
assert (points_per_side is None) != (
point_grids is None
), "Exactly one of points_per_side or point_grid must be provided."
if points_per_side is not None:
self.point_grids = build_all_layer_point_grids(
points_per_side,
crop_n_layers,
crop_n_points_downscale_factor,
)
elif point_grids is not None:
self.point_grids = point_grids
else:
raise ValueError("Can't have both points_per_side and point_grid be None.")
assert output_mode in [
"binary_mask",
"uncompressed_rle",
"coco_rle",
], f"Unknown output_mode {output_mode}."
if output_mode == "coco_rle":
if min_mask_region_area > 0:
self.predictor = SamPredictor(model)
self.points_per_side = points_per_side
self.points_per_batch = points_per_batch
self.pred_iou_thresh = pred_iou_thresh
self.high_score_thresh = high_score_thresh
self.stability_score_thresh = stability_score_thresh
self.stability_score_offset = stability_score_offset
self.box_nms_thresh = box_nms_thresh
self.crop_n_layers = crop_n_layers
self.crop_nms_thresh = crop_nms_thresh
self.crop_overlap_ratio = crop_overlap_ratio
self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
self.min_mask_region_area = min_mask_region_area
self.output_mode = output_mode
def set_point_grids(self, point_grids):
self.point_grids = point_grids
def set_points_per_side(self, points_per_side):
self.point_grids = build_all_layer_point_grids(
points_per_side,
0,
1,
)
@torch.no_grad()
def set_image(self, image: np.ndarray) -> MaskData:
# Crop the image and calculate embeddings
self.predictor.set_image(image)
@torch.no_grad()
def hierarchical_generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
self.set_image(image)
self.set_points_per_side(self.points_per_side // 4)
ori_masks, or_results = self.generate(image, True)
ih, iw, _ = image.shape
hstride = ih // self.points_per_side
wstride = iw // self.points_per_side
new_points = []
pass_counter = 0
full_point_grids = np.array(self.point_grids)
for mask in range(full_point_grids.shape[1]):
point_coords = [full_point_grids[0, mask, 0] * iw, full_point_grids[0, mask, 1] * ih]
for sy in [-1, 0, 1]:
for sx in [-1, 0, 1]:
if (sy == 0 and sx == 0) or or_results[int(point_coords[0] + wstride * sy), int(point_coords[1] + hstride * sx)]:
continue
new_points.append([(point_coords[0] + wstride * sy) / iw, (point_coords[1] + hstride * sx) / ih])
if point_coords[0] + wstride * 2 < iw:
for sx in [-1, 0, 1]:
if or_results[int(point_coords[0] + wstride * 2), int(point_coords[1] + hstride * sx)]:
continue
new_points.append([(point_coords[0] + wstride * 2) / iw, (point_coords[1] + hstride * sx) / ih])
if point_coords[1] + hstride * 2 < ih:
for sy in [-1, 0, 1]:
if or_results[int(point_coords[0] + wstride * sy), int(point_coords[1] + hstride * 2)]:
continue
new_points.append([(point_coords[0] + wstride * sy) / iw, (point_coords[1] + hstride * 2) / ih])
if point_coords[0] + wstride * 2 < iw and point_coords[1] + hstride * 2 < ih:
if or_results[int(point_coords[0] + wstride * 2), int(point_coords[1] + hstride * 2)]:
continue
new_points.append([(point_coords[0] + wstride * 2) / iw, (point_coords[1] + hstride * 2) / ih])
self.set_point_grids([np.array(new_points)])
new_masks = self.generate(image, False)
new_masks.cat(ori_masks)
new_masks = self.post_process(image, new_masks)
return new_masks
@torch.no_grad()
def generate(self, image: np.ndarray, need_high: bool) -> MaskData:
orig_size = image.shape[:2]
# Get points for this crop
points_scale = np.array(orig_size)[None, ::-1]
points_for_image = self.point_grids[0] * points_scale
# Generate masks for this crop in batches
data = MaskData()
for (points,) in batch_iterator(self.points_per_batch, points_for_image):
orig_h, orig_w = orig_size
# Run model on this batch
transformed_points = self.predictor.transform.apply_coords(points, orig_size)
in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
masks, iou_preds, _ = self.predictor.predict_torch(
in_points[:, None, :],
in_labels[:, None],
return_logits=True,
)
# Serialize predictions and store in MaskData
batch_data = MaskData(
masks=masks.flatten(0, 1),
iou_preds=iou_preds.flatten(0, 1),
points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
)
del masks
if self.pred_iou_thresh > 0.0:
keep_mask = batch_data["iou_preds"] > self.pred_iou_thresh
batch_data.filter(keep_mask)
# Calculate stability score
batch_data["stability_score"] = calculate_stability_score(
batch_data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
)
if self.stability_score_thresh > 0.0:
keep_mask = batch_data["stability_score"] >= self.stability_score_thresh
batch_data.filter(keep_mask)
if need_high:
batch_data["high_masks"] = batch_data["masks"] > self.high_score_thresh
batch_data["masks"] = batch_data["masks"] > self.predictor.model.mask_threshold
batch_data["boxes"] = batched_mask_to_box(batch_data["masks"])
keep_mask = ~is_box_near_crop_edge(batch_data["boxes"], [0, 0, orig_w, orig_h], [0, 0, orig_w, orig_h])
if not torch.all(keep_mask):
batch_data.filter(keep_mask)
# Compress to RLE
batch_data["rles"] = mask_to_rle_pytorch(batch_data["masks"])
data.cat(batch_data)
del batch_data
if need_high:
high_masks = data["high_masks"]
or_results = torch.zeros([high_masks.shape[1], high_masks.shape[2]]).to(high_masks.device)
for mask in high_masks:
or_results = torch.logical_or(or_results, mask)
del data["high_masks"]
or_results = or_results.permute(1, 0)
del data['masks']
return data, or_results
else:
del data['masks']
return data
@torch.no_grad()
def reset_image(self):
self.predictor.reset_image()
@torch.no_grad()
def post_process(self, image: np.ndarray, data: MaskData) -> List[Dict[str, Any]]:
orig_size = image.shape[:2]
orig_h, orig_w = orig_size
keep_by_nms = batched_nms(
data["boxes"].float(),
data["iou_preds"],
torch.zeros_like(data["boxes"][:, 0]), # categories
iou_threshold=self.box_nms_thresh,
)
data.filter(keep_by_nms)
# Filter small disconnected regions and holes in masks
if self.min_mask_region_area > 0:
data = self.postprocess_small_regions(
data,
self.min_mask_region_area,
max(self.box_nms_thresh, self.crop_nms_thresh),
)
# Encode masks
if self.output_mode == "coco_rle":
data["segmentations"] = [coco_encode_rle(rle) for rle in data["rles"]]
elif self.output_mode == "binary_mask": | data["segmentations"] = [rle_to_mask(rle) for rle in data["rles"]] | 14 | 2023-12-19 11:25:54+00:00 | 16k |
DeepWok/mase | machop/chop/passes/graph/transforms/verilog/emit_tb.py | [
{
"identifier": "emit_data_in_tb_sv",
"path": "machop/chop/passes/graph/transforms/verilog/emit_tb_data_in.py",
"snippet": "def emit_data_in_tb_sv(data_width, load_path, out_file):\n buff = f\"\"\"\n`timescale 1 ns / 1 ps\n\nmodule AESL_autofifo_data_in_V (\n clk,\n reset,\n if_empty_n,\n ... | import math, time, os, logging, torch, glob, shutil
from chop.passes.graph.utils import vf, v2p, init_project
from chop.passes.graph.transforms.quantize.quantizers import integer_quantizer_for_hw
from .emit_tb_data_in import emit_data_in_tb_sv, emit_data_in_tb_dat
from .emit_tb_data_out import emit_data_out_tb_sv, emit_data_out_tb_dat
from .emit_tb_testbench import emit_top_tb
from pathlib import Path | 13,095 | w_in_param = graph.nodes_in[0].meta["mase"].parameters["common"]["args"]
in_width = w_in_param["data_in_0"]["precision"][0]
in_size = v_in_param["DATA_IN_0_TENSOR_SIZE_DIM_0"]
data_width = in_width * in_size
# TODO : need to check
addr_width = 1
depth = 1
load_path = os.path.join(tv_dir, f"sw_data_in.dat")
out_file = os.path.join(v_dir, f"top_data_in_fifo.sv")
emit_data_in_tb_sv(data_width, load_path, out_file)
v_out_param = (
graph.nodes_out[0].meta["mase"].parameters["hardware"]["verilog_param"]
)
w_out_param = graph.nodes_in[0].meta["mase"].parameters["common"]["results"]
out_width = w_out_param["data_out_0"]["precision"][0]
out_size = v_out_param["DATA_OUT_0_TENSOR_SIZE_0_DIM_0"]
data_width = out_width * out_size
# TODO : need to check
addr_width = 1
depth = 1
load_path = os.path.join(tv_dir, f"sw_data_out.dat")
store_path = os.path.join(tv_dir, f"hw_data_out.dat")
out_file = os.path.join(v_dir, f"top_data_out_fifo.sv")
emit_data_out_tb_sv(data_width, load_path, store_path, out_file)
out_file = os.path.join(v_dir, f"top_tb.sv")
# in_trans_num = v_in_param["DATA_IN_0_DEPTH"] * trans_num
in_trans_num = trans_num
out_trans_num = trans_num
emit_top_tb(
tv_dir,
"top",
out_file,
in_width,
in_size,
out_width,
out_size,
in_trans_num,
out_trans_num,
)
out_file = os.path.join(v_dir, f"fifo_para.v")
with open(out_file, "w", encoding="utf-8") as outf:
outf.write("// initial a empty file")
# Copy testbench components
dut_dir = os.path.join(project_dir, "hardware", "rtl")
for svfile in glob.glob(os.path.join(dut_dir, "*.sv")):
shutil.copy(svfile, v_dir)
def emit_tb_dat(graph, trans_num=1, project_dir="top", test_inputs=None):
"""
Emit the test vectors in dat files for simulation
"""
sim_dir = os.path.join(project_dir, "hardware", "sim")
tv_dir = os.path.join(sim_dir, "tv")
if not os.path.exists(tv_dir):
os.mkdir(tv_dir)
in_type = (
graph.nodes_in[0].meta["mase"].parameters["common"]["args"]["data_in_0"]["type"]
)
in_width = (
graph.nodes_in[0]
.meta["mase"]
.parameters["common"]["args"]["data_in_0"]["precision"][0]
)
in_frac_width = (
graph.nodes_in[0]
.meta["mase"]
.parameters["common"]["args"]["data_in_0"]["precision"][1]
)
sw_data_out = [graph.model(trans) for trans in test_inputs]
out_type = (
graph.nodes_out[0]
.meta["mase"]
.parameters["common"]["results"]["data_out_0"]["type"]
)
prec = (
graph.nodes_out[0]
.meta["mase"]
.parameters["common"]["results"]["data_out_0"]["precision"]
)
out_width = (
graph.nodes_out[0]
.meta["mase"]
.parameters["common"]["results"]["data_out_0"]["precision"][0]
)
if len(prec) > 1:
out_frac_width = (
graph.nodes_out[0]
.meta["mase"]
.parameters["common"]["results"]["data_out_0"]["precision"][1]
)
else:
out_frac_width = 0
# TODO: Make out_type as input to support casting to any type
hw_data_out = [
integer_quantizer_for_hw(trans, width=out_width, frac_width=out_frac_width)
.squeeze(0)
.to(torch.int)
for trans in sw_data_out
]
# TODO: for now
for i, trans in enumerate(test_inputs):
test_inputs[i] = torch.flatten(trans).tolist()
for i, trans in enumerate(hw_data_out):
hw_data_out[i] = torch.flatten(trans).tolist()
load_path = os.path.join(tv_dir, "sw_data_in.dat")
|
logger = logging.getLogger(__name__)
def emit_tb_verilog(graph, trans_num=1, project_dir="top"):
sim_dir = os.path.join(project_dir, "hardware", "sim")
tv_dir = os.path.join(sim_dir, "tv")
if not os.path.exists(tv_dir):
os.mkdir(tv_dir)
v_dir = os.path.join(sim_dir, "verilog")
if not os.path.exists(v_dir):
os.mkdir(v_dir)
# TODO : need to emit all the inputs
v_in_param = graph.nodes_in[0].meta["mase"].parameters["hardware"]["verilog_param"]
w_in_param = graph.nodes_in[0].meta["mase"].parameters["common"]["args"]
in_width = w_in_param["data_in_0"]["precision"][0]
in_size = v_in_param["DATA_IN_0_TENSOR_SIZE_DIM_0"]
data_width = in_width * in_size
# TODO : need to check
addr_width = 1
depth = 1
load_path = os.path.join(tv_dir, f"sw_data_in.dat")
out_file = os.path.join(v_dir, f"top_data_in_fifo.sv")
emit_data_in_tb_sv(data_width, load_path, out_file)
v_out_param = (
graph.nodes_out[0].meta["mase"].parameters["hardware"]["verilog_param"]
)
w_out_param = graph.nodes_in[0].meta["mase"].parameters["common"]["results"]
out_width = w_out_param["data_out_0"]["precision"][0]
out_size = v_out_param["DATA_OUT_0_TENSOR_SIZE_0_DIM_0"]
data_width = out_width * out_size
# TODO : need to check
addr_width = 1
depth = 1
load_path = os.path.join(tv_dir, f"sw_data_out.dat")
store_path = os.path.join(tv_dir, f"hw_data_out.dat")
out_file = os.path.join(v_dir, f"top_data_out_fifo.sv")
emit_data_out_tb_sv(data_width, load_path, store_path, out_file)
out_file = os.path.join(v_dir, f"top_tb.sv")
# in_trans_num = v_in_param["DATA_IN_0_DEPTH"] * trans_num
in_trans_num = trans_num
out_trans_num = trans_num
emit_top_tb(
tv_dir,
"top",
out_file,
in_width,
in_size,
out_width,
out_size,
in_trans_num,
out_trans_num,
)
out_file = os.path.join(v_dir, f"fifo_para.v")
with open(out_file, "w", encoding="utf-8") as outf:
outf.write("// initial a empty file")
# Copy testbench components
dut_dir = os.path.join(project_dir, "hardware", "rtl")
for svfile in glob.glob(os.path.join(dut_dir, "*.sv")):
shutil.copy(svfile, v_dir)
def emit_tb_dat(graph, trans_num=1, project_dir="top", test_inputs=None):
"""
Emit the test vectors in dat files for simulation
"""
sim_dir = os.path.join(project_dir, "hardware", "sim")
tv_dir = os.path.join(sim_dir, "tv")
if not os.path.exists(tv_dir):
os.mkdir(tv_dir)
in_type = (
graph.nodes_in[0].meta["mase"].parameters["common"]["args"]["data_in_0"]["type"]
)
in_width = (
graph.nodes_in[0]
.meta["mase"]
.parameters["common"]["args"]["data_in_0"]["precision"][0]
)
in_frac_width = (
graph.nodes_in[0]
.meta["mase"]
.parameters["common"]["args"]["data_in_0"]["precision"][1]
)
sw_data_out = [graph.model(trans) for trans in test_inputs]
out_type = (
graph.nodes_out[0]
.meta["mase"]
.parameters["common"]["results"]["data_out_0"]["type"]
)
prec = (
graph.nodes_out[0]
.meta["mase"]
.parameters["common"]["results"]["data_out_0"]["precision"]
)
out_width = (
graph.nodes_out[0]
.meta["mase"]
.parameters["common"]["results"]["data_out_0"]["precision"][0]
)
if len(prec) > 1:
out_frac_width = (
graph.nodes_out[0]
.meta["mase"]
.parameters["common"]["results"]["data_out_0"]["precision"][1]
)
else:
out_frac_width = 0
# TODO: Make out_type as input to support casting to any type
hw_data_out = [
integer_quantizer_for_hw(trans, width=out_width, frac_width=out_frac_width)
.squeeze(0)
.to(torch.int)
for trans in sw_data_out
]
# TODO: for now
for i, trans in enumerate(test_inputs):
test_inputs[i] = torch.flatten(trans).tolist()
for i, trans in enumerate(hw_data_out):
hw_data_out[i] = torch.flatten(trans).tolist()
load_path = os.path.join(tv_dir, "sw_data_in.dat") | emit_data_in_tb_dat(graph.nodes_in[0], test_inputs, load_path) | 1 | 2023-12-18 12:50:53+00:00 | 16k |
OPPOMKLab/u-LLaVA | models/GroundingDINO/groundingdino/models/GroundingDINO/groundingdino.py | [
{
"identifier": "box_ops",
"path": "models/GroundingDINO/groundingdino/util/box_ops.py",
"snippet": "def box_cxcywh_to_xyxy(x):\ndef box_xyxy_to_cxcywh(x):\ndef box_iou(boxes1, boxes2):\ndef generalized_box_iou(boxes1, boxes2):\ndef box_iou_pairwise(boxes1, boxes2):\ndef generalized_box_iou_pairwise(box... | import copy
import torch
import torch.nn.functional as F
from typing import List
from torch import nn
from torchvision.ops.boxes import nms
from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
from models.GroundingDINO.groundingdino.util import box_ops, get_tokenlizer
from models.GroundingDINO.groundingdino.util.misc import (
NestedTensor,
accuracy,
get_world_size,
interpolate,
inverse_sigmoid,
is_dist_avail_and_initialized,
nested_tensor_from_tensor_list,
)
from models.GroundingDINO.groundingdino.util.utils import get_phrases_from_posmap
from models.GroundingDINO.groundingdino.util.visualizer import COCOVisualizer
from models.GroundingDINO.groundingdino.util.vl_utils import create_positive_map_from_span
from ..registry import MODULE_BUILD_FUNCS
from .backbone import build_backbone
from .bertwarper import (
BertModelWarper,
generate_masks_with_special_tokens,
generate_masks_with_special_tokens_and_transfer_map,
)
from .transformer import build_transformer
from .utils import MLP, ContrastiveEmbed, sigmoid_focal_loss | 11,415 | self.nheads = nheads
self.max_text_len = 256
self.sub_sentence_present = sub_sentence_present
# setting query dim
self.query_dim = query_dim
assert query_dim == 4
# for dn training
self.num_patterns = num_patterns
self.dn_number = dn_number
self.dn_box_noise_scale = dn_box_noise_scale
self.dn_label_noise_ratio = dn_label_noise_ratio
self.dn_labelbook_size = dn_labelbook_size
# bert
self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type)
self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)
self.bert.pooler.dense.weight.requires_grad_(False)
self.bert.pooler.dense.bias.requires_grad_(False)
self.bert = BertModelWarper(bert_model=self.bert)
self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)
nn.init.constant_(self.feat_map.bias.data, 0)
nn.init.xavier_uniform_(self.feat_map.weight.data)
# freeze
# special tokens
self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
# prepare input projection layers
if num_feature_levels > 1:
num_backbone_outs = len(backbone.num_channels)
input_proj_list = []
for _ in range(num_backbone_outs):
in_channels = backbone.num_channels[_]
input_proj_list.append(
nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
)
)
for _ in range(num_feature_levels - num_backbone_outs):
input_proj_list.append(
nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
nn.GroupNorm(32, hidden_dim),
)
)
in_channels = hidden_dim
self.input_proj = nn.ModuleList(input_proj_list)
else:
assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!"
self.input_proj = nn.ModuleList(
[
nn.Sequential(
nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
)
]
)
self.backbone = backbone
self.aux_loss = aux_loss
self.box_pred_damping = box_pred_damping = None
self.iter_update = iter_update
assert iter_update, "Why not iter_update?"
# prepare pred layers
self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share
# prepare class & box embed
_class_embed = ContrastiveEmbed()
_bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0)
nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)
if dec_pred_bbox_embed_share:
box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]
else:
box_embed_layerlist = [
copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)
]
class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]
self.bbox_embed = nn.ModuleList(box_embed_layerlist)
self.class_embed = nn.ModuleList(class_embed_layerlist)
self.transformer.decoder.bbox_embed = self.bbox_embed
self.transformer.decoder.class_embed = self.class_embed
# two stage
self.two_stage_type = two_stage_type
assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
two_stage_type
)
if two_stage_type != "no":
if two_stage_bbox_embed_share:
assert dec_pred_bbox_embed_share
self.transformer.enc_out_bbox_embed = _bbox_embed
else:
self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)
if two_stage_class_embed_share:
assert dec_pred_bbox_embed_share
self.transformer.enc_out_class_embed = _class_embed
else:
self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)
self.refpoint_embed = None
self._reset_parameters()
def _reset_parameters(self):
# init input_proj
for proj in self.input_proj:
nn.init.xavier_uniform_(proj[0].weight, gain=1)
nn.init.constant_(proj[0].bias, 0)
def set_image_tensor(self, samples: NestedTensor):
if isinstance(samples, (list, torch.Tensor)):
| # ------------------------------------------------------------------------
# Grounding DINO
# url: https://github.com/IDEA-Research/GroundingDINO
# Copyright (c) 2023 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Conditional DETR model and criterion classes.
# Copyright (c) 2021 Microsoft. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
class GroundingDINO(nn.Module):
"""This is the Cross-Attention Detector module that performs object detection"""
def __init__(
self,
backbone,
transformer,
num_queries,
aux_loss=False,
iter_update=False,
query_dim=2,
num_feature_levels=1,
nheads=8,
# two stage
two_stage_type="no", # ['no', 'standard']
dec_pred_bbox_embed_share=True,
two_stage_class_embed_share=True,
two_stage_bbox_embed_share=True,
num_patterns=0,
dn_number=100,
dn_box_noise_scale=0.4,
dn_label_noise_ratio=0.5,
dn_labelbook_size=100,
text_encoder_type="bert-base-uncased",
sub_sentence_present=True,
max_text_len=256,
):
"""Initializes the model.
Parameters:
backbone: torch module of the backbone to be used. See backbone.py
transformer: torch module of the transformer architecture. See transformer.py
num_queries: number of object queries, ie detection slot. This is the maximal number of objects
Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.
aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
"""
super().__init__()
self.num_queries = num_queries
self.transformer = transformer
self.hidden_dim = hidden_dim = transformer.d_model
self.num_feature_levels = num_feature_levels
self.nheads = nheads
self.max_text_len = 256
self.sub_sentence_present = sub_sentence_present
# setting query dim
self.query_dim = query_dim
assert query_dim == 4
# for dn training
self.num_patterns = num_patterns
self.dn_number = dn_number
self.dn_box_noise_scale = dn_box_noise_scale
self.dn_label_noise_ratio = dn_label_noise_ratio
self.dn_labelbook_size = dn_labelbook_size
# bert
self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type)
self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)
self.bert.pooler.dense.weight.requires_grad_(False)
self.bert.pooler.dense.bias.requires_grad_(False)
self.bert = BertModelWarper(bert_model=self.bert)
self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)
nn.init.constant_(self.feat_map.bias.data, 0)
nn.init.xavier_uniform_(self.feat_map.weight.data)
# freeze
# special tokens
self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
# prepare input projection layers
if num_feature_levels > 1:
num_backbone_outs = len(backbone.num_channels)
input_proj_list = []
for _ in range(num_backbone_outs):
in_channels = backbone.num_channels[_]
input_proj_list.append(
nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
)
)
for _ in range(num_feature_levels - num_backbone_outs):
input_proj_list.append(
nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
nn.GroupNorm(32, hidden_dim),
)
)
in_channels = hidden_dim
self.input_proj = nn.ModuleList(input_proj_list)
else:
assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!"
self.input_proj = nn.ModuleList(
[
nn.Sequential(
nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
)
]
)
self.backbone = backbone
self.aux_loss = aux_loss
self.box_pred_damping = box_pred_damping = None
self.iter_update = iter_update
assert iter_update, "Why not iter_update?"
# prepare pred layers
self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share
# prepare class & box embed
_class_embed = ContrastiveEmbed()
_bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0)
nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)
if dec_pred_bbox_embed_share:
box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]
else:
box_embed_layerlist = [
copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)
]
class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]
self.bbox_embed = nn.ModuleList(box_embed_layerlist)
self.class_embed = nn.ModuleList(class_embed_layerlist)
self.transformer.decoder.bbox_embed = self.bbox_embed
self.transformer.decoder.class_embed = self.class_embed
# two stage
self.two_stage_type = two_stage_type
assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
two_stage_type
)
if two_stage_type != "no":
if two_stage_bbox_embed_share:
assert dec_pred_bbox_embed_share
self.transformer.enc_out_bbox_embed = _bbox_embed
else:
self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)
if two_stage_class_embed_share:
assert dec_pred_bbox_embed_share
self.transformer.enc_out_class_embed = _class_embed
else:
self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)
self.refpoint_embed = None
self._reset_parameters()
def _reset_parameters(self):
# init input_proj
for proj in self.input_proj:
nn.init.xavier_uniform_(proj[0].weight, gain=1)
nn.init.constant_(proj[0].bias, 0)
def set_image_tensor(self, samples: NestedTensor):
if isinstance(samples, (list, torch.Tensor)): | samples = nested_tensor_from_tensor_list(samples) | 8 | 2023-12-21 08:10:23+00:00 | 16k |
chinhsuanwu/ifusion | ldm/models/diffusion/ddpm.py | [
{
"identifier": "log_txt_as_img",
"path": "ldm/util.py",
"snippet": "def log_txt_as_img(wh, xc, size=10):\n # wh a tuple of (width, height)\n # xc a list of captions to plot\n b = len(xc)\n txts = list()\n for bi in range(b):\n txt = Image.new(\"RGB\", wh, color=\"white\")\n ... | import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
import itertools
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager, nullcontext
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities import rank_zero_only
from omegaconf import ListConfig
from ldm.util import (
log_txt_as_img,
exists,
default,
ismap,
isimage,
mean_flat,
count_params,
instantiate_from_config,
)
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import (
normal_kl,
DiagonalGaussianDistribution,
)
from ldm.models.autoencoder import (
VQModelInterface,
IdentityFirstStage,
AutoencoderKL,
)
from ldm.modules.diffusionmodules.util import (
make_beta_schedule,
extract_into_tensor,
noise_like,
)
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.modules.attention import CrossAttention | 12,198 | opt = torch.optim.AdamW(params, lr=lr)
return opt
class LatentDiffusion(DDPM):
"""main class"""
def __init__(
self,
first_stage_config,
cond_stage_config,
num_timesteps_cond=None,
cond_stage_key="image_cond",
cond_stage_trainable=False,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
unet_trainable=True,
*args,
**kwargs,
):
self.num_timesteps_cond = default(num_timesteps_cond, 1)
self.scale_by_std = scale_by_std
assert self.num_timesteps_cond <= kwargs["timesteps"]
# for backwards compatibility after implementation of DiffusionWrapper
if conditioning_key is None:
conditioning_key = "concat" if concat_mode else "crossattn"
if cond_stage_config == "__is_unconditional__":
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.unet_trainable = unet_trainable
self.cond_stage_key = cond_stage_key
try:
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
except:
self.num_downs = 0
if not scale_by_std:
self.scale_factor = scale_factor
else:
self.register_buffer("scale_factor", torch.tensor(scale_factor))
self.instantiate_first_stage(first_stage_config)
self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward
# construct linear projection layer for concatenating image CLIP embedding and RT
self.cc_projection = nn.Linear(772, 768)
nn.init.eye_(list(self.cc_projection.parameters())[0][:768, :768])
nn.init.zeros_(list(self.cc_projection.parameters())[1])
self.cc_projection.requires_grad_(True)
self.clip_denoised = False
self.bbox_tokenizer = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys)
self.restarted_from_ckpt = True
def make_cond_schedule(
self,
):
self.cond_ids = torch.full(
size=(self.num_timesteps,),
fill_value=self.num_timesteps - 1,
dtype=torch.long,
)
ids = torch.round(
torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)
).long()
self.cond_ids[: self.num_timesteps_cond] = ids
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
# only for very first batch
if (
self.scale_by_std
and self.current_epoch == 0
and self.global_step == 0
and batch_idx == 0
and not self.restarted_from_ckpt
):
assert (
self.scale_factor == 1.0
), "rather not use custom rescaling and std-rescaling simultaneously"
# set rescale weight to 1./std of encodings
print("### USING STD-RESCALING ###")
x = super().get_input(batch, self.first_stage_key)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
del self.scale_factor
self.register_buffer("scale_factor", 1.0 / z.flatten().std())
print(f"setting self.scale_factor to {self.scale_factor}")
print("### USING STD-RESCALING ###")
def register_schedule(
self,
given_betas=None,
beta_schedule="linear",
timesteps=1000,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
):
super().register_schedule(
given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s
)
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
self.make_cond_schedule()
def instantiate_first_stage(self, config):
| """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {"concat": "c_concat", "crossattn": "c_crossattn", "adm": "y"}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(
self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image_target",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.0,
v_posterior=0.0, # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.0,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.0,
make_it_fit=False,
ucg_training=None,
):
super().__init__()
assert parameterization in [
"eps",
"x0",
], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(
f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode"
)
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
self.make_it_fit = make_it_fit
if ckpt_path is not None:
self.init_from_ckpt(
ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet
)
self.register_schedule(
given_betas=given_betas,
beta_schedule=beta_schedule,
timesteps=timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s,
)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
self.ucg_training = ucg_training or dict()
if self.ucg_training:
self.ucg_prng = np.random.RandomState()
def register_schedule(
self,
given_betas=None,
beta_schedule="linear",
timesteps=1000,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(
beta_schedule,
timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s,
)
alphas = 1.0 - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
(timesteps,) = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert (
alphas_cumprod.shape[0] == self.num_timesteps
), "alphas have to be defined for each timestep"
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer("betas", to_torch(betas))
self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer(
"sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
)
self.register_buffer(
"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
)
self.register_buffer(
"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
)
self.register_buffer(
"sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
)
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (
1.0 - alphas_cumprod_prev
) / (1.0 - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer("posterior_variance", to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer(
"posterior_log_variance_clipped",
to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
)
self.register_buffer(
"posterior_mean_coef1",
to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
)
self.register_buffer(
"posterior_mean_coef2",
to_torch(
(1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
),
)
if self.parameterization == "eps":
lvlb_weights = self.betas**2 / (
2
* self.posterior_variance
* to_torch(alphas)
* (1 - self.alphas_cumprod)
)
elif self.parameterization == "x0":
lvlb_weights = (
0.5
* np.sqrt(torch.Tensor(alphas_cumprod))
/ (2.0 * 1 - torch.Tensor(alphas_cumprod))
)
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer("lvlb_weights", lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
@torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
if self.make_it_fit:
n_params = len(
[
name
for name, _ in itertools.chain(
self.named_parameters(), self.named_buffers()
)
]
)
for name, param in tqdm(
itertools.chain(self.named_parameters(), self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params,
):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape) == len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[
i % old_shape[0], j % old_shape[1]
]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
missing, unexpected = (
self.load_state_dict(sd, strict=False)
if not only_model
else self.model.load_state_dict(sd, strict=False)
)
print(
f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
)
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(
self.log_one_minus_alphas_cumprod, t, x_start.shape
)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
- extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
* noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+ extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(
self.posterior_log_variance_clipped, t, x_t.shape
)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1.0, 1.0)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
x_start=x_recon, x_t=x, t=t
)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(
x=x, t=t, clip_denoised=clip_denoised
)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(
reversed(range(0, self.num_timesteps)),
desc="Sampling t",
total=self.num_timesteps,
):
img = self.p_sample(
img,
torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised,
)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop(
(batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates,
)
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
* noise
)
def get_loss(self, pred, target, mean=True):
if self.loss_type == "l1":
loss = (target - pred).abs()
if mean:
loss = loss.mean()
elif self.loss_type == "l2":
if mean:
loss = torch.nn.functional.mse_loss(target, pred)
else:
loss = torch.nn.functional.mse_loss(target, pred, reduction="none")
elif self.loss_type == "smooth_l1":
if mean:
loss = torch.nn.functional.smooth_l1_loss(target, pred)
else:
loss = torch.nn.functional.smooth_l1_loss(
target, pred, reduction="none"
)
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
return loss
def p_losses(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_out = self.model(x_noisy, t)
loss_dict = {}
if self.parameterization == "eps":
target = noise
elif self.parameterization == "x0":
target = x_start
else:
raise NotImplementedError(
f"Paramterization {self.parameterization} not yet supported"
)
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
log_prefix = "train" if self.training else "val"
loss_dict.update({f"{log_prefix}/loss_simple": loss.mean()})
loss_simple = loss.mean() * self.l_simple_weight
loss_vlb = (self.lvlb_weights[t] * loss).mean()
loss_dict.update({f"{log_prefix}/loss_vlb": loss_vlb})
loss = loss_simple + self.original_elbo_weight * loss_vlb
loss_dict.update({f"{log_prefix}/loss": loss})
return loss, loss_dict
def forward(self, x, *args, **kwargs):
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
t = torch.randint(
0, self.num_timesteps, (x.shape[0],), device=self.device
).long()
return self.p_losses(x, t, *args, **kwargs)
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = rearrange(x, "b h w c -> b c h w")
x = x.to(memory_format=torch.contiguous_format).float()
return x
def shared_step(self, batch):
x = self.get_input(batch, self.first_stage_key)
loss, loss_dict = self(x)
return loss, loss_dict
def training_step(self, batch, batch_idx):
for k in self.ucg_training:
p = self.ucg_training[k]["p"]
val = self.ucg_training[k]["val"]
if val is None:
val = ""
for i in range(len(batch[k])):
if self.ucg_prng.choice(2, p=[1 - p, p]):
batch[k][i] = val
loss, loss_dict = self.shared_step(batch)
self.log_dict(
loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True
)
self.log(
"global_step",
self.global_step,
prog_bar=True,
logger=True,
on_step=True,
on_epoch=False,
)
if self.use_scheduler:
lr = self.optimizers().param_groups[0]["lr"]
self.log(
"lr_abs", lr, prog_bar=True, logger=True, on_step=True, on_epoch=False
)
return loss
@torch.no_grad()
def validation_step(self, batch, batch_idx):
_, loss_dict_no_ema = self.shared_step(batch)
with self.ema_scope():
_, loss_dict_ema = self.shared_step(batch)
loss_dict_ema = {key + "_ema": loss_dict_ema[key] for key in loss_dict_ema}
self.log_dict(
loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True
)
self.log_dict(
loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True
)
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self.model)
def _get_rows_from_list(self, samples):
n_imgs_per_row = len(samples)
denoise_grid = rearrange(samples, "n b c h w -> b n c h w")
denoise_grid = rearrange(denoise_grid, "b n c h w -> (b n) c h w")
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
@torch.no_grad()
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
log = dict()
x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
x = x.to(self.device)[:N]
log["inputs"] = x
# get diffusion row
diffusion_row = list()
x_start = x[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), "1 -> b", b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(x_start)
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
diffusion_row.append(x_noisy)
log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, denoise_row = self.sample(
batch_size=N, return_intermediates=True
)
log["samples"] = samples
log["denoise_row"] = self._get_rows_from_list(denoise_row)
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.model.parameters())
if self.learn_logvar:
params = params + [self.logvar]
opt = torch.optim.AdamW(params, lr=lr)
return opt
class LatentDiffusion(DDPM):
"""main class"""
def __init__(
self,
first_stage_config,
cond_stage_config,
num_timesteps_cond=None,
cond_stage_key="image_cond",
cond_stage_trainable=False,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
unet_trainable=True,
*args,
**kwargs,
):
self.num_timesteps_cond = default(num_timesteps_cond, 1)
self.scale_by_std = scale_by_std
assert self.num_timesteps_cond <= kwargs["timesteps"]
# for backwards compatibility after implementation of DiffusionWrapper
if conditioning_key is None:
conditioning_key = "concat" if concat_mode else "crossattn"
if cond_stage_config == "__is_unconditional__":
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.unet_trainable = unet_trainable
self.cond_stage_key = cond_stage_key
try:
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
except:
self.num_downs = 0
if not scale_by_std:
self.scale_factor = scale_factor
else:
self.register_buffer("scale_factor", torch.tensor(scale_factor))
self.instantiate_first_stage(first_stage_config)
self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward
# construct linear projection layer for concatenating image CLIP embedding and RT
self.cc_projection = nn.Linear(772, 768)
nn.init.eye_(list(self.cc_projection.parameters())[0][:768, :768])
nn.init.zeros_(list(self.cc_projection.parameters())[1])
self.cc_projection.requires_grad_(True)
self.clip_denoised = False
self.bbox_tokenizer = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys)
self.restarted_from_ckpt = True
def make_cond_schedule(
self,
):
self.cond_ids = torch.full(
size=(self.num_timesteps,),
fill_value=self.num_timesteps - 1,
dtype=torch.long,
)
ids = torch.round(
torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)
).long()
self.cond_ids[: self.num_timesteps_cond] = ids
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
# only for very first batch
if (
self.scale_by_std
and self.current_epoch == 0
and self.global_step == 0
and batch_idx == 0
and not self.restarted_from_ckpt
):
assert (
self.scale_factor == 1.0
), "rather not use custom rescaling and std-rescaling simultaneously"
# set rescale weight to 1./std of encodings
print("### USING STD-RESCALING ###")
x = super().get_input(batch, self.first_stage_key)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
del self.scale_factor
self.register_buffer("scale_factor", 1.0 / z.flatten().std())
print(f"setting self.scale_factor to {self.scale_factor}")
print("### USING STD-RESCALING ###")
def register_schedule(
self,
given_betas=None,
beta_schedule="linear",
timesteps=1000,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
):
super().register_schedule(
given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s
)
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
self.make_cond_schedule()
def instantiate_first_stage(self, config): | model = instantiate_from_config(config) | 7 | 2023-12-17 12:45:38+00:00 | 16k |
wangzhecheng/SkyScript | customized_train_and_test.py | [
{
"identifier": "create_model_and_transforms",
"path": "src/open_clip/factory.py",
"snippet": "def create_model_and_transforms(\n model_name: str,\n pretrained: Optional[str] = None,\n precision: str = 'fp32',\n device: Union[str, torch.device] = 'cpu',\n jit: bool = F... | import glob
import json
import logging
import os
import re
import subprocess
import sys
import random
import numpy as np
import torch
import wandb
import torch.utils.tensorboard as tensorboard
import horovod.torch as hvd
from datetime import datetime
from torch import optim
from torch.cuda.amp import GradScaler
from torchvision import transforms
from src.open_clip.factory import create_model_and_transforms, get_tokenizer, create_loss
from src.open_clip.model import trace_model
from src.training.data import get_data
from src.training.distributed import is_master, init_distributed_device, broadcast_object
from src.training.logger import setup_logging
from src.training.scheduler import cosine_lr, const_lr, const_lr_cooldown
from src.training.train import train_one_epoch, evaluate
from src.training.file_utils import pt_load, check_exists, start_sync_process, remote_sync
from src.training.main import natural_key, get_latest_checkpoint, copy_codebase
from test_zero_shot_classification import *
from params import parse_args | 11,284 | """
Adapted from https://github.com/mlfoundations/open_clip.
Copyright (c) 2012-2021 Gabriel Ilharco, Mitchell Wortsman, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, John Miller, Hongseok Namkoong, Hannaneh Hajishirzi, Ali Farhadi, Ludwig Schmidt
"""
try:
except ImportError:
wandb = None
try:
except ImportError:
tensorboard = None
try:
except ImportError:
hvd = None
# from src.open_clip import create_model_and_transforms, trace_model, get_tokenizer, create_loss
LATEST_CHECKPOINT_NAME = "epoch_latest.pt"
def RandomRotationNew(image):
angle = random.choice([0, 90, 180, 270])
image = transforms.functional.rotate(image, angle)
return image
def zero_shot_eval_during_training(model, test_dataloaders, epoch, args, tb_writer=None):
logging.info('Starting zero-shot evaluation.')
zero_shot_metrics = {}
for dataset_name in test_dataloaders:
logging.info(f'Evaluating zero-shot classification for dataset {dataset_name}')
results = test_zero_shot_classification(model, test_dataloaders[dataset_name]['dataloader'],
test_dataloaders[dataset_name]['labels'],
test_dataloaders[dataset_name]['is_binary'], args,
dataset_name=dataset_name, debugging=args.debugging)
for k, v in results.items():
if type(v) in [float, int, np.float16, np.float32, np.float64, np.int8, np.int16, np.int32, np.int64]:
zero_shot_metrics[k] = v
logging.info(
f"Zero-Shot Eval Epoch: {epoch} "
+ "\t".join([f"{k}: {round(v, 4):.4f}" for k, v in zero_shot_metrics.items()])
)
if args.save_logs:
for name, val in zero_shot_metrics.items():
if tb_writer is not None:
tb_writer.add_scalar(f"val/{name}", val, epoch)
with open(os.path.join(args.checkpoint_path, "results.jsonl"), "a+") as f:
f.write(json.dumps(zero_shot_metrics))
f.write("\n")
# if args.wandb:
# assert wandb is not None, 'Please install wandb.'
# for name, val in zero_shot_metrics.items():
# wandb.log({f"val/{name}": val, 'epoch': epoch})
logging.info('Finished zero-shot evaluation.')
return zero_shot_metrics
def train_and_test(args):
args = parse_args(args)
if torch.cuda.is_available():
# This enables tf32 on Ampere GPUs which is only 8% slower than
# float16 and almost as accurate as float32
# This was a default in pytorch until 1.12
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
# fully initialize distributed device environment
device = init_distributed_device(args)
# get the name of the experiments
if args.name is None:
# sanitize model name for filesystem / uri use, easier if we don't use / in name as a rule?
model_name_safe = args.model.replace('/', '-')
date_str = datetime.now().strftime("%Y_%m_%d-%H_%M_%S")
if args.distributed:
# sync date_str from master to all ranks
date_str = broadcast_object(args, date_str)
args.name = '-'.join([
date_str,
f"model_{model_name_safe}",
f"lr_{args.lr}",
f"b_{args.batch_size}",
f"j_{args.workers}",
f"p_{args.precision}",
])
resume_latest = args.resume == 'latest'
log_base_path = os.path.join(args.logs, args.name)
args.log_path = None
if is_master(args, local=args.log_local):
os.makedirs(log_base_path, exist_ok=True)
log_filename = f'out-{args.rank}' if args.log_local else 'out.log'
args.log_path = os.path.join(log_base_path, log_filename)
if os.path.exists(args.log_path) and not resume_latest:
print(
"Error. Experiment already exists. Use --name {} to specify a new experiment."
)
return -1
# Setup text logger
args.log_level = logging.DEBUG if args.debug else logging.INFO
| """
Adapted from https://github.com/mlfoundations/open_clip.
Copyright (c) 2012-2021 Gabriel Ilharco, Mitchell Wortsman, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, John Miller, Hongseok Namkoong, Hannaneh Hajishirzi, Ali Farhadi, Ludwig Schmidt
"""
try:
except ImportError:
wandb = None
try:
except ImportError:
tensorboard = None
try:
except ImportError:
hvd = None
# from src.open_clip import create_model_and_transforms, trace_model, get_tokenizer, create_loss
LATEST_CHECKPOINT_NAME = "epoch_latest.pt"
def RandomRotationNew(image):
angle = random.choice([0, 90, 180, 270])
image = transforms.functional.rotate(image, angle)
return image
def zero_shot_eval_during_training(model, test_dataloaders, epoch, args, tb_writer=None):
logging.info('Starting zero-shot evaluation.')
zero_shot_metrics = {}
for dataset_name in test_dataloaders:
logging.info(f'Evaluating zero-shot classification for dataset {dataset_name}')
results = test_zero_shot_classification(model, test_dataloaders[dataset_name]['dataloader'],
test_dataloaders[dataset_name]['labels'],
test_dataloaders[dataset_name]['is_binary'], args,
dataset_name=dataset_name, debugging=args.debugging)
for k, v in results.items():
if type(v) in [float, int, np.float16, np.float32, np.float64, np.int8, np.int16, np.int32, np.int64]:
zero_shot_metrics[k] = v
logging.info(
f"Zero-Shot Eval Epoch: {epoch} "
+ "\t".join([f"{k}: {round(v, 4):.4f}" for k, v in zero_shot_metrics.items()])
)
if args.save_logs:
for name, val in zero_shot_metrics.items():
if tb_writer is not None:
tb_writer.add_scalar(f"val/{name}", val, epoch)
with open(os.path.join(args.checkpoint_path, "results.jsonl"), "a+") as f:
f.write(json.dumps(zero_shot_metrics))
f.write("\n")
# if args.wandb:
# assert wandb is not None, 'Please install wandb.'
# for name, val in zero_shot_metrics.items():
# wandb.log({f"val/{name}": val, 'epoch': epoch})
logging.info('Finished zero-shot evaluation.')
return zero_shot_metrics
def train_and_test(args):
args = parse_args(args)
if torch.cuda.is_available():
# This enables tf32 on Ampere GPUs which is only 8% slower than
# float16 and almost as accurate as float32
# This was a default in pytorch until 1.12
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
# fully initialize distributed device environment
device = init_distributed_device(args)
# get the name of the experiments
if args.name is None:
# sanitize model name for filesystem / uri use, easier if we don't use / in name as a rule?
model_name_safe = args.model.replace('/', '-')
date_str = datetime.now().strftime("%Y_%m_%d-%H_%M_%S")
if args.distributed:
# sync date_str from master to all ranks
date_str = broadcast_object(args, date_str)
args.name = '-'.join([
date_str,
f"model_{model_name_safe}",
f"lr_{args.lr}",
f"b_{args.batch_size}",
f"j_{args.workers}",
f"p_{args.precision}",
])
resume_latest = args.resume == 'latest'
log_base_path = os.path.join(args.logs, args.name)
args.log_path = None
if is_master(args, local=args.log_local):
os.makedirs(log_base_path, exist_ok=True)
log_filename = f'out-{args.rank}' if args.log_local else 'out.log'
args.log_path = os.path.join(log_base_path, log_filename)
if os.path.exists(args.log_path) and not resume_latest:
print(
"Error. Experiment already exists. Use --name {} to specify a new experiment."
)
return -1
# Setup text logger
args.log_level = logging.DEBUG if args.debug else logging.INFO | setup_logging(args.log_path, args.log_level) | 8 | 2023-12-19 11:50:56+00:00 | 16k |
penghao-wu/vstar | VisualSearch/utils/dataset.py | [
{
"identifier": "conversation",
"path": "VisualSearch/model/llava/conversation.py",
"snippet": "class SeparatorStyle(Enum):\nclass Conversation:\n SINGLE = auto()\n TWO = auto()\n MPT = auto()\n PLAIN = auto()\n LLAMA_2 = auto()\n W, H = image.size\n ... | import glob
import os
import random
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from pycocotools import mask
from transformers import CLIPImageProcessor
from transformers import OwlViTProcessor
from VisualSearch.model.llava import conversation as conversation_lib
from VisualSearch.model.llava.constants import (DEFAULT_IMAGE_TOKEN, IGNORE_INDEX,
IMAGE_TOKEN_INDEX)
from VisualSearch.model.llava.mm_utils import tokenizer_image_token
from VisualSearch.utils.data_processing import get_mask_from_json
from VisualSearch.utils.refer import REFER
from VisualSearch.utils.refer_seg_dataset import ReferSegDataset
from VisualSearch.utils.general_segdet_dataset import SegDetDataset
from VisualSearch.utils.mixed_grounding_dataset import MixedGroundingDataset
from VisualSearch.utils.vqa_dataset import VQADataset
from VisualSearch.utils.utils import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
DEFAULT_IMAGE_TOKEN)
from VisualSearch.utils.utils import box_xyxy_to_cxcywh, expand2square | 14,360 | cv2.setNumThreads(1)
def collate_fn(
batch, tokenizer=None, conv_type="llava_v1", use_mm_start_end=True, local_rank=-1
):
image_path_list = []
images_list = []
images_clip_list = []
conversation_list = []
masks_list = []
label_list = []
bboxes_labels_list = []
bboxes_valid_list = []
masks_valid_list = []
resize_list = []
questions_list = []
sampled_classes_list = []
offset_list = [0]
cnt = 0
inferences = []
for (
image_path,
images,
images_clip,
conversations,
masks,
label,
bboxes_labels,
bboxes_valid,
masks_valid,
resize,
questions,
sampled_classes,
inference,
) in batch:
image_path_list.append(image_path)
images_list.append(images)
images_clip_list.append(images_clip)
conversation_list.extend(conversations)
label_list.append(label)
masks_list.append(masks.float())
bboxes_labels_list.extend(bboxes_labels)
bboxes_valid_list.extend(bboxes_valid)
masks_valid_list.append(torch.tensor(masks_valid))
resize_list.append(resize)
questions_list.append(questions)
sampled_classes_list.append(sampled_classes)
cnt += len(conversations)
offset_list.append(cnt)
inferences.append(inference)
if use_mm_start_end:
# replace <image> token
for i in range(len(conversation_list)):
replace_token = DEFAULT_IMAGE_TOKEN
replace_token = (
DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
)
conversation_list[i] = conversation_list[i].replace(
DEFAULT_IMAGE_TOKEN, replace_token
)
input_ids = [
| cv2.setNumThreads(1)
def collate_fn(
batch, tokenizer=None, conv_type="llava_v1", use_mm_start_end=True, local_rank=-1
):
image_path_list = []
images_list = []
images_clip_list = []
conversation_list = []
masks_list = []
label_list = []
bboxes_labels_list = []
bboxes_valid_list = []
masks_valid_list = []
resize_list = []
questions_list = []
sampled_classes_list = []
offset_list = [0]
cnt = 0
inferences = []
for (
image_path,
images,
images_clip,
conversations,
masks,
label,
bboxes_labels,
bboxes_valid,
masks_valid,
resize,
questions,
sampled_classes,
inference,
) in batch:
image_path_list.append(image_path)
images_list.append(images)
images_clip_list.append(images_clip)
conversation_list.extend(conversations)
label_list.append(label)
masks_list.append(masks.float())
bboxes_labels_list.extend(bboxes_labels)
bboxes_valid_list.extend(bboxes_valid)
masks_valid_list.append(torch.tensor(masks_valid))
resize_list.append(resize)
questions_list.append(questions)
sampled_classes_list.append(sampled_classes)
cnt += len(conversations)
offset_list.append(cnt)
inferences.append(inference)
if use_mm_start_end:
# replace <image> token
for i in range(len(conversation_list)):
replace_token = DEFAULT_IMAGE_TOKEN
replace_token = (
DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
)
conversation_list[i] = conversation_list[i].replace(
DEFAULT_IMAGE_TOKEN, replace_token
)
input_ids = [ | tokenizer_image_token(prompt, tokenizer, return_tensors="pt") | 4 | 2023-12-15 14:58:24+00:00 | 16k |
foocker/Bert-VITS2-Faster | train_ms.py | [
{
"identifier": "config",
"path": "config.py",
"snippet": "class Resample_config:\nclass Preprocess_text_config:\nclass Bert_gen_config:\nclass Emo_gen_config:\nclass Train_ms_config:\nclass Webui_config:\nclass Server_config:\nclass Translate_config:\nclass Config:\n def __init__(self, in_dir: str, ... | import platform
import os
import torch
import torch.distributed as dist
import logging
import argparse
import commons
import utils
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, GradScaler
from tqdm import tqdm
from config import config
from data_utils import (
TextAudioSpeakerLoader,
TextAudioSpeakerCollate,
DistributedBucketSampler,
)
from models import (
SynthesizerTrn,
MultiPeriodDiscriminator,
DurationDiscriminator,
)
from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from text.symbols import symbols | 13,258 | if rank == 0:
train_and_evaluate(
rank,
local_rank,
epoch,
hps,
[net_g, net_d, net_dur_disc],
[optim_g, optim_d, optim_dur_disc],
[scheduler_g, scheduler_d, scheduler_dur_disc],
scaler,
[train_loader, eval_loader],
logger,
[writer, writer_eval],
)
else:
train_and_evaluate(
rank,
local_rank,
epoch,
hps,
[net_g, net_d, net_dur_disc],
[optim_g, optim_d, optim_dur_disc],
[scheduler_g, scheduler_d, scheduler_dur_disc],
scaler,
[train_loader, None],
None,
None,
)
scheduler_g.step()
scheduler_d.step()
if net_dur_disc is not None:
scheduler_dur_disc.step()
def train_and_evaluate(
rank,
local_rank,
epoch,
hps,
nets,
optims,
schedulers,
scaler,
loaders,
logger,
writers,
):
net_g, net_d, net_dur_disc = nets
optim_g, optim_d, optim_dur_disc = optims
scheduler_g, scheduler_d, scheduler_dur_disc = schedulers
train_loader, eval_loader = loaders
if writers is not None:
writer, writer_eval = writers
train_loader.batch_sampler.set_epoch(epoch)
global global_step
net_g.train()
net_d.train()
if net_dur_disc is not None:
net_dur_disc.train()
for batch_idx, (
x,
x_lengths,
spec,
spec_lengths,
y,
y_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
) in tqdm(enumerate(train_loader)):
if net_g.module.use_noise_scaled_mas:
current_mas_noise_scale = (
net_g.module.mas_noise_scale_initial
- net_g.module.noise_scale_delta * global_step
)
net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
x, x_lengths = x.cuda(local_rank, non_blocking=True), x_lengths.cuda(
local_rank, non_blocking=True
)
spec, spec_lengths = spec.cuda(
local_rank, non_blocking=True
), spec_lengths.cuda(local_rank, non_blocking=True)
y, y_lengths = y.cuda(local_rank, non_blocking=True), y_lengths.cuda(
local_rank, non_blocking=True
)
speakers = speakers.cuda(local_rank, non_blocking=True)
tone = tone.cuda(local_rank, non_blocking=True)
language = language.cuda(local_rank, non_blocking=True)
bert = bert.cuda(local_rank, non_blocking=True)
ja_bert = ja_bert.cuda(local_rank, non_blocking=True)
en_bert = en_bert.cuda(local_rank, non_blocking=True)
with autocast(enabled=hps.train.fp16_run):
(
y_hat,
l_length,
attn,
ids_slice,
x_mask,
z_mask,
(z, z_p, m_p, logs_p, m_q, logs_q),
(hidden_x, logw, logw_),
) = net_g(
x,
x_lengths,
spec,
spec_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
)
| # flake8: noqa: E402
logging.getLogger("numba").setLevel(logging.WARNING)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = (
True # If encontered training problem,please try to disable TF32.
)
torch.set_float32_matmul_precision("medium")
torch.backends.cudnn.benchmark = True
torch.backends.cuda.sdp_kernel("flash")
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_mem_efficient_sdp(
True
) # Not available if torch version is lower than 2.0
torch.backends.cuda.enable_math_sdp(True)
global_step = 0
def run():
# 环境变量解析
envs = config.train_ms_config.env
for env_name, env_value in envs.items():
if env_name not in os.environ.keys():
print("加载config中的配置{}".format(str(env_value)))
os.environ[env_name] = str(env_value)
print(
"加载环境变量 \nMASTER_ADDR: {},\nMASTER_PORT: {},\nWORLD_SIZE: {},\nRANK: {},\nLOCAL_RANK: {}".format(
os.environ["MASTER_ADDR"],
os.environ["MASTER_PORT"],
os.environ["WORLD_SIZE"],
os.environ["RANK"],
os.environ["LOCAL_RANK"],
)
)
# 多卡训练设置
backend = "nccl"
if platform.system() == "Windows":
backend = "gloo"
dist.init_process_group(
backend=backend,
init_method="env://", # If Windows,switch to gloo backend.
) # Use torchrun instead of mp.spawn
rank = dist.get_rank()
local_rank = int(os.environ["LOCAL_RANK"])
n_gpus = dist.get_world_size()
# 命令行/config.yml配置解析
# hps = utils.get_hparams()
parser = argparse.ArgumentParser()
# 非必要不建议使用命令行配置，请使用config.yml文件
parser.add_argument(
"-c",
"--config",
type=str,
default=config.train_ms_config.config_path,
help="JSON file for configuration",
)
parser.add_argument(
"-m",
"--model",
type=str,
help="数据集文件夹路径，请注意，数据不再默认放在/logs文件夹下。如果需要用命令行配置，请声明相对于根目录的路径",
default=config.dataset_path,
)
args = parser.parse_args()
model_dir = os.path.join(args.model, config.train_ms_config.model)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
hps = utils.get_hparams_from_file(args.config)
hps.model_dir = model_dir
# 比较路径是否相同
if os.path.realpath(args.config) != os.path.realpath(
config.train_ms_config.config_path
):
with open(args.config, "r", encoding="utf-8") as f:
data = f.read()
with open(config.train_ms_config.config_path, "w", encoding="utf-8") as f:
f.write(data)
torch.manual_seed(hps.train.seed)
torch.cuda.set_device(local_rank)
global global_step
if rank == 0:
logger = utils.get_logger(hps.model_dir)
logger.info(hps)
utils.check_git_hash(hps.model_dir)
writer = SummaryWriter(log_dir=hps.model_dir)
writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
train_sampler = DistributedBucketSampler(
train_dataset,
hps.train.batch_size,
[32, 300, 400, 500, 600, 700, 800, 900, 1000],
num_replicas=n_gpus,
rank=rank,
shuffle=True,
)
collate_fn = TextAudioSpeakerCollate()
train_loader = DataLoader(
train_dataset,
num_workers=16,
shuffle=False,
pin_memory=True,
collate_fn=collate_fn,
batch_sampler=train_sampler,
persistent_workers=True,
prefetch_factor=4,
) # DataLoader config could be adjusted.
if rank == 0:
eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
eval_loader = DataLoader(
eval_dataset,
num_workers=0,
shuffle=False,
batch_size=1,
pin_memory=True,
drop_last=False,
collate_fn=collate_fn,
)
if (
"use_noise_scaled_mas" in hps.model.keys()
and hps.model.use_noise_scaled_mas is True
):
print("Using noise scaled MAS for VITS2")
mas_noise_scale_initial = 0.01
noise_scale_delta = 2e-6
else:
print("Using normal MAS for VITS1")
mas_noise_scale_initial = 0.0
noise_scale_delta = 0.0
if (
"use_duration_discriminator" in hps.model.keys()
and hps.model.use_duration_discriminator is True
):
print("Using duration discriminator for VITS2")
net_dur_disc = DurationDiscriminator(
hps.model.hidden_channels,
hps.model.hidden_channels,
3,
0.1,
gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
).cuda(local_rank)
if (
"use_spk_conditioned_encoder" in hps.model.keys()
and hps.model.use_spk_conditioned_encoder is True
):
if hps.data.n_speakers == 0:
raise ValueError(
"n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model"
)
else:
print("Using normal encoder for VITS1")
net_g = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
mas_noise_scale_initial=mas_noise_scale_initial,
noise_scale_delta=noise_scale_delta,
**hps.model,
).cuda(local_rank)
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(local_rank)
optim_g = torch.optim.AdamW(
filter(lambda p: p.requires_grad, net_g.parameters()),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps,
)
optim_d = torch.optim.AdamW(
net_d.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps,
)
if net_dur_disc is not None:
optim_dur_disc = torch.optim.AdamW(
net_dur_disc.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps,
)
else:
optim_dur_disc = None
net_g = DDP(net_g, device_ids=[local_rank])
net_d = DDP(net_d, device_ids=[local_rank])
dur_resume_lr = None
if net_dur_disc is not None:
net_dur_disc = DDP(
net_dur_disc, device_ids=[local_rank], find_unused_parameters=True
)
# 下载底模
if config.train_ms_config.base["use_base_model"]:
utils.download_checkpoint(
hps.model_dir,
config.train_ms_config.base,
token=config.openi_token,
mirror=config.mirror,
)
try:
if net_dur_disc is not None:
_, _, dur_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"),
net_dur_disc,
optim_dur_disc,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
_, optim_g, g_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"),
net_g,
optim_g,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
_, optim_d, d_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"),
net_d,
optim_d,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
if not optim_g.param_groups[0].get("initial_lr"):
optim_g.param_groups[0]["initial_lr"] = g_resume_lr
if not optim_d.param_groups[0].get("initial_lr"):
optim_d.param_groups[0]["initial_lr"] = d_resume_lr
if not optim_dur_disc.param_groups[0].get("initial_lr"):
optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
epoch_str = max(epoch_str, 1)
global_step = (epoch_str - 1) * len(train_loader)
print(
f"******************检测到模型存在，epoch为 {epoch_str}，gloabl step为 {global_step}*********************"
)
except Exception as e:
print(e)
epoch_str = 1
global_step = 0
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
)
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
)
if net_dur_disc is not None:
if not optim_dur_disc.param_groups[0].get("initial_lr"):
optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(
optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
)
else:
scheduler_dur_disc = None
scaler = GradScaler(enabled=hps.train.fp16_run)
for epoch in range(epoch_str, hps.train.epochs + 1):
if rank == 0:
train_and_evaluate(
rank,
local_rank,
epoch,
hps,
[net_g, net_d, net_dur_disc],
[optim_g, optim_d, optim_dur_disc],
[scheduler_g, scheduler_d, scheduler_dur_disc],
scaler,
[train_loader, eval_loader],
logger,
[writer, writer_eval],
)
else:
train_and_evaluate(
rank,
local_rank,
epoch,
hps,
[net_g, net_d, net_dur_disc],
[optim_g, optim_d, optim_dur_disc],
[scheduler_g, scheduler_d, scheduler_dur_disc],
scaler,
[train_loader, None],
None,
None,
)
scheduler_g.step()
scheduler_d.step()
if net_dur_disc is not None:
scheduler_dur_disc.step()
def train_and_evaluate(
rank,
local_rank,
epoch,
hps,
nets,
optims,
schedulers,
scaler,
loaders,
logger,
writers,
):
net_g, net_d, net_dur_disc = nets
optim_g, optim_d, optim_dur_disc = optims
scheduler_g, scheduler_d, scheduler_dur_disc = schedulers
train_loader, eval_loader = loaders
if writers is not None:
writer, writer_eval = writers
train_loader.batch_sampler.set_epoch(epoch)
global global_step
net_g.train()
net_d.train()
if net_dur_disc is not None:
net_dur_disc.train()
for batch_idx, (
x,
x_lengths,
spec,
spec_lengths,
y,
y_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
) in tqdm(enumerate(train_loader)):
if net_g.module.use_noise_scaled_mas:
current_mas_noise_scale = (
net_g.module.mas_noise_scale_initial
- net_g.module.noise_scale_delta * global_step
)
net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
x, x_lengths = x.cuda(local_rank, non_blocking=True), x_lengths.cuda(
local_rank, non_blocking=True
)
spec, spec_lengths = spec.cuda(
local_rank, non_blocking=True
), spec_lengths.cuda(local_rank, non_blocking=True)
y, y_lengths = y.cuda(local_rank, non_blocking=True), y_lengths.cuda(
local_rank, non_blocking=True
)
speakers = speakers.cuda(local_rank, non_blocking=True)
tone = tone.cuda(local_rank, non_blocking=True)
language = language.cuda(local_rank, non_blocking=True)
bert = bert.cuda(local_rank, non_blocking=True)
ja_bert = ja_bert.cuda(local_rank, non_blocking=True)
en_bert = en_bert.cuda(local_rank, non_blocking=True)
with autocast(enabled=hps.train.fp16_run):
(
y_hat,
l_length,
attn,
ids_slice,
x_mask,
z_mask,
(z, z_p, m_p, logs_p, m_q, logs_q),
(hidden_x, logw, logw_),
) = net_g(
x,
x_lengths,
spec,
spec_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
) | mel = spec_to_mel_torch( | 12 | 2023-12-18 09:53:41+00:00 | 16k |
sinoyou/nelf-pro | nerfstudio/models/nelfpro.py | [
{
"identifier": "RayBundle",
"path": "nerfstudio/cameras/rays.py",
"snippet": "class RayBundle(TensorDataclass):\n \"\"\"A bundle of ray parameters.\"\"\"\n\n # TODO(ethan): make sure the sizes with ... are correct\n origins: TensorType[..., 3]\n \"\"\"Ray origins (XYZ)\"\"\"\n directions... | from dataclasses import dataclass, field
from typing import Dict, List, Tuple, Type
from torch.nn import Parameter
from torchmetrics import PeakSignalNoiseRatio
from torchmetrics.functional import structural_similarity_index_measure
from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
from typing_extensions import Literal
from nerfstudio.cameras.rays import RayBundle
from nerfstudio.engine.callbacks import (
TrainingCallback,
TrainingCallbackAttributes,
TrainingCallbackLocation,
)
from nerfstudio.fields.nelfpro_field import NeLFProField
from nerfstudio.fields.density_fields import NeLFDensityField
from nerfstudio.model_components.losses import (
MSELoss,
distortion_loss,
interlevel_loss,
field_tv_loss,
)
from nerfstudio.model_components.ray_samplers import ProposalNetworkSampler
from nerfstudio.model_components.renderers import (
AccumulationRenderer,
DepthRenderer,
RGBRenderer,
)
from nerfstudio.model_components.scene_colliders import NearFarCollider, EarthCollider
from nerfstudio.models.base_model import Model, ModelConfig
from nerfstudio.utils import colormaps
from nerfstudio.utils.colors import get_color
import numpy as np
import torch | 13,802 | callbacks = []
def reinitialize_optimizer(params_name, training_callback_attributes, step):
'''reinitialize optimizer and scheduler after upsampling. '''
optimizers_config = training_callback_attributes.optimizers.config
data = training_callback_attributes.pipeline.get_param_groups()[params_name]
lr_init = optimizers_config[params_name]["optimizer"].lr
opt_state_param_groups = training_callback_attributes.optimizers.optimizers[params_name].state_dict()['param_groups']
training_callback_attributes.optimizers.optimizers[params_name] = optimizers_config[params_name]["optimizer"].setup(params=data)
# note: we load_state_dict() for loading param_groups's _lr_initial, which is used for scheduler's last_epoch.
training_callback_attributes.optimizers.optimizers[params_name].load_state_dict({
'state': training_callback_attributes.optimizers.optimizers[params_name].state_dict()['state'],
'param_groups': opt_state_param_groups
})
if optimizers_config[params_name]["scheduler"]:
# save current state dict
training_callback_attributes.optimizers.schedulers[params_name] = optimizers_config[params_name]["scheduler"].setup(
optimizer=training_callback_attributes.optimizers.optimizers[params_name], lr_init=lr_init, last_epoch=step,
)
# upsampling core factor
if self.config.resolution_core_angular < self.config.resolution_max_core_angular or self.config.resolution_core_radial < self.config.resolution_max_core_radial:
def upsample_core(self, training_callback_attributes: TrainingCallbackAttributes, step: int):
angular_res = self.core_angular_upsamling_step.pop(0)
radial_res = self.core_radial_upsamling_step.pop(0)
self.field.upsample_core((angular_res, radial_res))
reinitialize_optimizer('field_core_angular', training_callback_attributes, step)
reinitialize_optimizer('field_core_radial', training_callback_attributes, step)
callbacks.append(
TrainingCallback(
where_to_run=[TrainingCallbackLocation.AFTER_TRAIN_ITERATION],
iters=self.config.iters_core_upsampling,
func=upsample_core,
args=[self, training_callback_attributes]
)
)
# update distortion loss multiplier
if self.config.distortion_loss_mult_factor_max != 1.0:
def update_distortion_loss_mult_factor(self, step: int):
self.current_distort_loss_mult_factor = self.distortion_loss_mult_factor_step.pop(0) * self.config.distortion_loss_mult
callbacks.append(
TrainingCallback(
where_to_run=[TrainingCallbackLocation.AFTER_TRAIN_ITERATION],
iters=self.config.distortion_loss_mult_factor_iters,
func=update_distortion_loss_mult_factor,
args=[self]
)
)
# Proposal Network Annealing and Status Update
if self.config.use_proposal_weight_anneal:
N = self.config.proposal_weights_anneal_max_num_iters
def set_anneal(step):
train_frac = np.clip(step / N, 0, 1)
bias = lambda x, b: (b * x) / ((b - 1) * x + 1)
anneal = bias(train_frac, self.config.proposal_weights_anneal_slope)
self.proposal_sampler.set_anneal(anneal)
callbacks.append(
TrainingCallback(
where_to_run=[TrainingCallbackLocation.BEFORE_TRAIN_ITERATION],
update_every_num_iters=1,
func=set_anneal,
)
)
callbacks.append(
TrainingCallback(
where_to_run=[TrainingCallbackLocation.AFTER_TRAIN_ITERATION],
update_every_num_iters=1,
func=self.proposal_sampler.step_cb,
)
)
return callbacks
def get_outputs(self, ray_bundle: RayBundle):
ray_samples, weights_list, ray_samples_list = self.proposal_sampler(ray_bundle, density_fns=self.density_fns)
field_outputs = self.field(ray_samples)
weights = ray_samples.get_weights(field_outputs['density'])
weights_list.append(weights)
ray_samples_list.append(ray_samples)
rgb = self.renderer_rgb(rgb=field_outputs['rgb'], weights=weights)
depth = self.renderer_depth(weights=weights, ray_samples=ray_samples)
accumulation = self.renderer_accumulation(weights=weights)
outputs = {
"rgb": rgb,
"accumulation": accumulation,
"depth": depth,
}
if self.training:
outputs["tensors"] = {
'basis': field_outputs['basis'],
'core_angular': field_outputs['core_angular'],
}
# These use a lot of GPU memory, so we avoid storing them for eval.
if self.training:
outputs["weights_list"] = weights_list
outputs["ray_samples_list"] = ray_samples_list
for i in range(self.config.num_proposal_iterations):
outputs[f"prop_depth_{i}"] = self.renderer_depth(weights=weights_list[i], ray_samples=ray_samples_list[i])
return outputs
def get_metrics_dict(self, outputs, batch):
metrics_dict = {}
image = batch["image"].to(self.device)
metrics_dict["psnr"] = self.psnr(outputs["rgb"], image)
if self.training:
| from __future__ import annotations
@dataclass
class NeLFProModelConfig(ModelConfig):
_target: Type = field(default_factory=lambda: NeLFProModel)
# basis factor configs
num_basis: int = 64
"""Number of basis factors."""
near_basis: int = 16
"""Number of near basis factors for each ray. """
dim_basis: int = 2
"""Feature dimension of basis factor. """
resolution_basis: int = 256
"""Tensor resolution of basis factor. """
freq_theta: float = 1
"""Frequency of multiplicative warping for theta. """
freq_phi: float = 1
"""Frequency of multiplicative warping for phi."""
# core factor configs
num_core: int = 3
"""Number of core factors."""
near_core: int = 3
"""Number of near core factors for each ray."""
dim_core: int = 32
"""Feature dimension of core factor."""
resolution_core_angular: int = 64
"""Initial tensor resolution of angular core factor. (a.k.a theta and phi)"""
resolution_max_core_angular: int = 128
"""Max tensor resolution of angular core factor. (a.k.a theta and phi)"""
resolution_core_radial: int = 64
"""Initial tensor resolution of radial core factor. (a.k.a depth direction)"""
resolution_max_core_radial: int = 1024
"""Max tensor resolution of radial core factor. (a.k.a depth direction)"""
iters_core_upsampling: Tuple[int, ...] = (2000, 3000, 4000, 5500, 7000)
"""Iterations for upsampling core factor. """
# apperance embedding settings
use_appearance_embedding: bool = False
"""Whether to use appearance embedding. """
# sampler config
near_plane: float = 0.05
"""Near plane for initial ray sampler. """
far_plane: float = 1000.0
"""Far plane for initial ray sampler."""
use_earth_collider: bool = False
"""Whether to use earth model collider, must pass scene-specific scale. (e.g. for bungeenerf dataset)"""
earth_collider_scale: float = 1.0
"""Scale of the earth model collider. """
use_single_jitter: bool = False
"""Whether to use single jitter for initial ray sampler."""
init_sampler: Literal['uniform', 'sqrt', 'log', 'uniformdisp'] = 'uniformdisp'
"""Initial ray sampler function type. """
# proposal network config
num_proposal_iterations: int = 2
"""Number of proposal network iterations."""
num_proposal_samples_per_ray_1: int = 256
"""Number of proposal samples per ray for the #1 proposal network iteration."""
num_proposal_samples_per_ray_2: int = 96
"""Number of proposal samples per ray for the #2 proposal network iteration."""
num_nerf_samples_per_ray: int = 48
"""Number of nerf samples per ray for the main field. """
proposal_update_every: int = 5
"""Update proposal network every # iterations."""
proposal_warmup: int = 5000
"""Warmup proposal network by linear learning rate for the first # iterations."""
proposal_net_args_list: List[Dict] = field(
default_factory=lambda: [
{"angular_resolution": 64, "radial_resolution": 128, "feat_dim": 8, },
{"angular_resolution": 128, "radial_resolution": 256, "feat_dim": 8, },
]
)
"""List of proposal network arguments for each proposal networks"""
proposal_weights_anneal_slope: float = 10.0
"""Slope of the annealing function for the proposal weights."""
proposal_weights_anneal_max_num_iters: int = 100
"""Max num iterations for the annealing function."""
use_proposal_weight_anneal: bool = True
"""Whether to use proposal weight annealing."""
# rendering and training config
background_color: Literal["random", "last_sample", "white", "black"] = "random"
"""Background color for rendering when accumulation doesn't reach 1."""
interlevel_loss_mult: float = 1.0
"""Interlevel loss multiplier."""
distortion_loss_mult: float = 0.002
"""Initial distortion loss multiplier."""
distortion_loss_mult_factor_max: int = 1
"""Max multiplication factor for distortion loss multiplier."""
distortion_loss_mult_factor_iters: Tuple[int, ...] = (500, 1000, 2000, 4000)
"""Iterations for upsampling distortion loss multiplier."""""
basis_tv_loss_mult: float = 0.0
"""Tv loss multiplier for basis factor."""
core_tv_loss_mult: float = 0.0
"""Tv loss multiplier for core factor."""
def get_upsample_steps(res_base, res_max, num_iters):
x = (
np.round(
np.exp(
np.linspace(
np.log(res_base),
np.log(res_max),
num_iters + 1,
)
)
).astype("int").tolist()[1:]
)
return x
class NeLFProModel(Model):
config: NeLFProModelConfig
def populate_modules(self):
"""Set the fields and modules."""
super().populate_modules()
# Resolution and Loss Multiplier Upsampling Config
if self.config.resolution_core_angular < self.config.resolution_max_core_angular or self.config.resolution_core_radial < self.config.resolution_max_core_radial:
self.core_angular_upsamling_step = get_upsample_steps(self.config.resolution_core_angular, self.config.resolution_max_core_angular, len(self.config.iters_core_upsampling))
self.core_radial_upsamling_step = get_upsample_steps(self.config.resolution_core_radial, self.config.resolution_max_core_radial, len(self.config.iters_core_upsampling))
if self.config.distortion_loss_mult_factor_max != 1.0:
self.distortion_loss_mult_factor_step = get_upsample_steps(1.0, self.config.distortion_loss_mult_factor_max, len(self.config.distortion_loss_mult_factor_iters))
self.current_distort_loss_mult_factor = self.config.distortion_loss_mult * 1.0
else:
self.current_distort_loss_mult_factor = self.config.distortion_loss_mult * 1.0
# Main Field
self.field = NeLFProField(
num_images=self.num_train_data,
num_basis = self.config.num_basis,
near_basis = self.config.near_basis,
dim_basis = self.config.dim_basis,
resolution_basis = self.config.resolution_basis,
num_core = self.config.num_core,
near_core = self.config.near_core,
dim_core = self.config.dim_core,
resolution_core_angular = self.config.resolution_core_angular,
resolution_core_radial = self.config.resolution_core_radial,
freq_theta = self.config.freq_theta,
freq_phi = self.config.freq_phi,
use_appearance_embedding=self.config.use_appearance_embedding,
)
# Proposal Networks
self.proposal_networks = torch.nn.ModuleList()
assert len(self.config.proposal_net_args_list) == self.config.num_proposal_iterations, 'proposal_net_args_list should have the same length as num_proposal_iterations'
for i in range(self.config.num_proposal_iterations):
prop_net_args = self.config.proposal_net_args_list[i]
network = NeLFDensityField(num_core=self.config.num_core, near_core=self.config.near_core, **prop_net_args)
self.proposal_networks.append(network)
self.density_fns = [network.density_fn for network in self.proposal_networks]
# Proposal Sampler
update_schedule = lambda step: np.clip(
np.interp(step, [0, self.config.proposal_warmup], [0, self.config.proposal_update_every]),
1,
self.config.proposal_update_every,
)
self.proposal_sampler = ProposalNetworkSampler(
init_sampler=self.config.init_sampler,
num_nerf_samples_per_ray=self.config.num_nerf_samples_per_ray,
num_proposal_samples_per_ray=[self.config.num_proposal_samples_per_ray_1, self.config.num_proposal_samples_per_ray_2],
num_proposal_network_iterations=self.config.num_proposal_iterations,
single_jitter=self.config.use_single_jitter,
update_sched=update_schedule,
)
# Collider
if self.config.use_earth_collider:
self.collider = EarthCollider(scene_scaling_factor=self.config.earth_collider_scale)
else:
self.collider = NearFarCollider(near_plane=self.config.near_plane, far_plane=self.config.far_plane)
# Renders
background_color = (
get_color(self.config.background_color)
if self.config.background_color in set(["white", "black"])
else self.config.background_color
)
self.renderer_rgb = RGBRenderer(background_color=background_color)
self.renderer_accumulation = AccumulationRenderer()
self.renderer_depth = DepthRenderer()
# losses
self.rgb_loss = MSELoss()
# metrics
self.psnr = PeakSignalNoiseRatio(data_range=1.0)
self.ssim = structural_similarity_index_measure
self.lpips = LearnedPerceptualImagePatchSimilarity(normalize=True, net_type='vgg')
# adjust step-dependent components
self.adjust_step_dependent_components()
def n_parameters(self):
"""Return the number of parameters in the model."""
return sum(p.numel() for p in self.field.parameters()) + sum(p.numel() for p in self.proposal_networks.parameters())
def adjust_step_dependent_components(self) -> None:
"""Call the model's customized load function.
Args:
checkpoint_dir: directory of checkpoint
"""
load_step = self.load_step
if not load_step:
return
assert load_step >= 0, f"load_step must be non-negative, got {load_step}"
# distortion factor
if self.config.distortion_loss_mult_factor_max != 1.0:
i = 0
while len(self.distortion_loss_mult_factor_step) > 0 and load_step >= self.config.distortion_loss_mult_factor_iters[i]:
i += 1
self.current_distort_loss_mult_factor = self.distortion_loss_mult_factor_step.pop(0) * self.config.distortion_loss_mult
assert len(self.config.distortion_loss_mult_factor_iters) - i == len(self.distortion_loss_mult_factor_step), 'distortion_loss_mult_factor_step should have the same length as distortion_loss_mult_factor_iters'
# core upsampling
if self.config.resolution_core_angular < self.config.resolution_max_core_angular or self.config.resolution_core_radial < self.config.resolution_max_core_radial:
i = 0
while len(self.core_radial_upsamling_step) > 0 and load_step >= self.config.iters_core_upsampling[i]:
i += 1
angular_res = self.core_angular_upsamling_step.pop(0)
radial_res = self.core_radial_upsamling_step.pop(0)
self.field.upsample_core((angular_res, radial_res))
assert len(self.config.iters_core_upsampling) - i == len(self.core_radial_upsamling_step), 'core_radial_upsamling_step should have the same length as iters_core_upsampling'
# proposal network annealing: ignore as its update frequency is very high.
def get_param_groups(self) -> Dict[str, List[Parameter]]:
param_groups = {}
# seperate the parameters of the field into two groups: fields (e.g. MLP) and fields_coef (e.g. camera fields)
field_basis = self.field.get_basis_fields()
field_core_angular = self.field.get_core_fields(name='angular')
field_core_radial = self.field.get_core_fields(name='radial')
param_groups["field_mlp"] = list()
param_groups["field_basis"] = list()
param_groups["field_core_angular"] = list()
param_groups["field_core_radial"] = list()
param_groups["proposal_networks"] = list()
for field_params in self.field.parameters():
if field_params in field_basis:
param_groups["field_basis"].append(field_params)
elif field_params in field_core_angular:
param_groups["field_core_angular"].append(field_params)
elif field_params in field_core_radial:
param_groups["field_core_radial"].append(field_params)
else:
param_groups["field_mlp"].append(field_params)
for proposal_parameter in self.proposal_networks.parameters():
param_groups["proposal_networks"].append(proposal_parameter)
return param_groups
def get_training_callbacks(
self, training_callback_attributes: TrainingCallbackAttributes
) -> List[TrainingCallback]:
callbacks = []
def reinitialize_optimizer(params_name, training_callback_attributes, step):
'''reinitialize optimizer and scheduler after upsampling. '''
optimizers_config = training_callback_attributes.optimizers.config
data = training_callback_attributes.pipeline.get_param_groups()[params_name]
lr_init = optimizers_config[params_name]["optimizer"].lr
opt_state_param_groups = training_callback_attributes.optimizers.optimizers[params_name].state_dict()['param_groups']
training_callback_attributes.optimizers.optimizers[params_name] = optimizers_config[params_name]["optimizer"].setup(params=data)
# note: we load_state_dict() for loading param_groups's _lr_initial, which is used for scheduler's last_epoch.
training_callback_attributes.optimizers.optimizers[params_name].load_state_dict({
'state': training_callback_attributes.optimizers.optimizers[params_name].state_dict()['state'],
'param_groups': opt_state_param_groups
})
if optimizers_config[params_name]["scheduler"]:
# save current state dict
training_callback_attributes.optimizers.schedulers[params_name] = optimizers_config[params_name]["scheduler"].setup(
optimizer=training_callback_attributes.optimizers.optimizers[params_name], lr_init=lr_init, last_epoch=step,
)
# upsampling core factor
if self.config.resolution_core_angular < self.config.resolution_max_core_angular or self.config.resolution_core_radial < self.config.resolution_max_core_radial:
def upsample_core(self, training_callback_attributes: TrainingCallbackAttributes, step: int):
angular_res = self.core_angular_upsamling_step.pop(0)
radial_res = self.core_radial_upsamling_step.pop(0)
self.field.upsample_core((angular_res, radial_res))
reinitialize_optimizer('field_core_angular', training_callback_attributes, step)
reinitialize_optimizer('field_core_radial', training_callback_attributes, step)
callbacks.append(
TrainingCallback(
where_to_run=[TrainingCallbackLocation.AFTER_TRAIN_ITERATION],
iters=self.config.iters_core_upsampling,
func=upsample_core,
args=[self, training_callback_attributes]
)
)
# update distortion loss multiplier
if self.config.distortion_loss_mult_factor_max != 1.0:
def update_distortion_loss_mult_factor(self, step: int):
self.current_distort_loss_mult_factor = self.distortion_loss_mult_factor_step.pop(0) * self.config.distortion_loss_mult
callbacks.append(
TrainingCallback(
where_to_run=[TrainingCallbackLocation.AFTER_TRAIN_ITERATION],
iters=self.config.distortion_loss_mult_factor_iters,
func=update_distortion_loss_mult_factor,
args=[self]
)
)
# Proposal Network Annealing and Status Update
if self.config.use_proposal_weight_anneal:
N = self.config.proposal_weights_anneal_max_num_iters
def set_anneal(step):
train_frac = np.clip(step / N, 0, 1)
bias = lambda x, b: (b * x) / ((b - 1) * x + 1)
anneal = bias(train_frac, self.config.proposal_weights_anneal_slope)
self.proposal_sampler.set_anneal(anneal)
callbacks.append(
TrainingCallback(
where_to_run=[TrainingCallbackLocation.BEFORE_TRAIN_ITERATION],
update_every_num_iters=1,
func=set_anneal,
)
)
callbacks.append(
TrainingCallback(
where_to_run=[TrainingCallbackLocation.AFTER_TRAIN_ITERATION],
update_every_num_iters=1,
func=self.proposal_sampler.step_cb,
)
)
return callbacks
def get_outputs(self, ray_bundle: RayBundle):
ray_samples, weights_list, ray_samples_list = self.proposal_sampler(ray_bundle, density_fns=self.density_fns)
field_outputs = self.field(ray_samples)
weights = ray_samples.get_weights(field_outputs['density'])
weights_list.append(weights)
ray_samples_list.append(ray_samples)
rgb = self.renderer_rgb(rgb=field_outputs['rgb'], weights=weights)
depth = self.renderer_depth(weights=weights, ray_samples=ray_samples)
accumulation = self.renderer_accumulation(weights=weights)
outputs = {
"rgb": rgb,
"accumulation": accumulation,
"depth": depth,
}
if self.training:
outputs["tensors"] = {
'basis': field_outputs['basis'],
'core_angular': field_outputs['core_angular'],
}
# These use a lot of GPU memory, so we avoid storing them for eval.
if self.training:
outputs["weights_list"] = weights_list
outputs["ray_samples_list"] = ray_samples_list
for i in range(self.config.num_proposal_iterations):
outputs[f"prop_depth_{i}"] = self.renderer_depth(weights=weights_list[i], ray_samples=ray_samples_list[i])
return outputs
def get_metrics_dict(self, outputs, batch):
metrics_dict = {}
image = batch["image"].to(self.device)
metrics_dict["psnr"] = self.psnr(outputs["rgb"], image)
if self.training: | metrics_dict["distortion"] = distortion_loss(outputs["weights_list"], outputs["ray_samples_list"]) | 6 | 2023-12-15 20:07:22+00:00 | 16k |
amazon-science/c2f-seg | src/video_model.py | [
{
"identifier": "VQModel",
"path": "taming_src/taming_models.py",
"snippet": "class VQModel(nn.Module):\n def __init__(self, config):\n super(VQModel, self).__init__()\n self.config = config\n self.iteration = 0\n self.name = config.model_type\n self.m_path = os.pat... | import os
import math
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist
from torchvision import transforms
from taming_src.taming_models import VQModel
from src.video_component import MaskedTransformer, Resnet_Encoder, Refine_Module
from src.loss import VGG19, PerceptualLoss
from utils.pytorch_optimization import AdamW, get_linear_schedule_with_warmup
from utils.utils import torch_show_all_params, torch_init_model
from utils.utils import Config
from utils.evaluation import video_iou
from utils.loss import CrossEntropyLoss
from tqdm import tqdm | 11,267 | v, ix = torch.topk(logits, k)
out = logits.clone()
out[out < v[..., [-1]]] = -float('Inf')
return out
@torch.no_grad()
def batch_predict_maskgit(self, meta, iter, mode, temperature=1.0, T=3, start_iter=0):
'''
:param x:[B,3,H,W] image
:param c:[b,X,H,W] condition
:param mask: [1,1,H,W] mask
'''
self.sample_iter += 1
img_feat = self.img_encoder(meta['img_crop'].squeeze().permute((0,3,1,2)).to(torch.float32))
_, src_indices = self.encode_to_z(meta['vm_crop'])
# _, tgt_indices = self.encode_to_z(meta['fm_crop'])
bhwc = (_.shape[0], _.shape[2], _.shape[3], _.shape[1])
masked_indices = self.mask_token_idx * torch.ones_like(src_indices, device=src_indices.device) # [B, L]
unknown_number_in_the_beginning = torch.sum(masked_indices == self.mask_token_idx, dim=-1) # [B]
gamma = self.gamma_func("cosine")
cur_ids = masked_indices # [B, L]
seq_out = []
mask_out = []
for t in range(start_iter, T):
logits = self.transformer(img_feat[-1], src_indices, cur_ids, mask=None) # [B, L, N]
logits = logits[..., :-1]
logits = self.top_k_logits(logits, k=3)
probs = F.softmax(logits, dim=-1) # convert logits into probs [B, 256, vocab_size+1]
sampled_ids = torch.distributions.categorical.Categorical(probs=probs).sample() # [B, L]
unknown_map = (cur_ids == self.mask_token_idx) # which tokens need to be sampled -> bool [B, 256]
sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) # replace all -1 with their samples and leave the others untouched [B, 256]
seq_out.append(sampled_ids)
mask_out.append(1. * unknown_map)
ratio = 1. * (t + 1) / T # just a percentage e.g. 1 / 12
mask_ratio = gamma(ratio)
selected_probs = probs.gather(dim=-1, index=sampled_ids.unsqueeze(-1)).squeeze(-1)
selected_probs = torch.where(unknown_map, selected_probs, torch.Tensor([np.inf]).to(logits.device)) # ignore tokens which are already sampled [B, 256]
mask_len = torch.unsqueeze(torch.floor(unknown_number_in_the_beginning * mask_ratio), 1) # floor(256 * 0.99) = 254 --> [254, 254, 254, 254, ....] (B x 1)
mask_len = torch.maximum(torch.ones_like(mask_len), torch.minimum(torch.sum(unknown_map, dim=-1, keepdim=True) - 1, mask_len))
# Adds noise for randomness
masking = self.mask_by_random_topk(mask_len, selected_probs, temperature=self.choice_temperature * (1. - ratio))
# Masks tokens with lower confidence.
cur_ids = torch.where(masking, self.mask_token_idx, sampled_ids) # [B, L]
seq_ids = torch.stack(seq_out, dim=1) # [B, T, L]
quant_z = self.g_model.quantize.get_codebook_entry(seq_ids[:,-1,:].reshape(-1), shape=bhwc)
pred_fm_crop = self.g_model.decode(quant_z)
pred_fm_crop = pred_fm_crop.mean(dim=1, keepdim=True)
pred_fm_crop_old = torch.clamp(pred_fm_crop, min=0, max=1)
pred_vm_crop, pred_fm_crop = self.refine_module(img_feat, pred_fm_crop_old)
pred_vm_crop = F.interpolate(pred_vm_crop, size=(256, 256), mode="nearest")
pred_vm_crop = torch.sigmoid(pred_vm_crop)
loss_vm = self.refine_criterion(pred_vm_crop, meta['vm_crop'].transpose(1,0))
# pred_vm_crop = (pred_vm_crop>=0.5).to(torch.float32)
pred_fm_crop = F.interpolate(pred_fm_crop, size=(256, 256), mode="nearest")
pred_fm_crop = torch.sigmoid(pred_fm_crop)
loss_fm = self.refine_criterion(pred_fm_crop, meta['fm_crop'].transpose(1,0))
# pred_fm_crop = (pred_fm_crop>=0.5).to(torch.float32)
pred_fm_crop_old = self.align_raw_size(pred_fm_crop_old, meta['obj_position'], meta["vm_pad"], meta)
# pred_vm = self.align_raw_size(pred_vm_crop, meta['obj_position'], meta["vm_pad"], meta)
pred_fm = self.align_raw_size(pred_fm_crop, meta['obj_position'], meta["vm_pad"], meta)
pred_fm = pred_fm + pred_fm_crop_old
loss_eval = self.loss_and_evaluation(pred_fm, meta)
loss_eval["loss_fm"] = loss_fm
loss_eval["loss_vm"] = loss_vm
return loss_eval
def create_inputs_tokens_normal(self, num, device):
self.num_latent_size = self.config['resolution'] // self.config['patch_size']
blank_tokens = torch.ones((num, self.num_latent_size ** 2), device=device)
masked_tokens = self.mask_token_idx * blank_tokens
return masked_tokens.to(torch.int64)
def gamma_func(self, mode="cosine"):
if mode == "linear":
return lambda r: 1 - r
elif mode == "cosine":
return lambda r: np.cos(r * np.pi / 2)
elif mode == "square":
return lambda r: 1 - r ** 2
elif mode == "cubic":
return lambda r: 1 - r ** 3
elif mode == "log":
return lambda r, total_unknown: - np.log2(r) / np.log2(total_unknown)
else:
raise NotImplementedError
def mask_by_random_topk(self, mask_len, probs, temperature=1.0):
confidence = torch.log(probs) + temperature * torch.distributions.gumbel.Gumbel(0, 1).sample(probs.shape).to(probs.device)
sorted_confidence, _ = torch.sort(confidence, dim=-1) # from small to large
# Obtains cut off threshold given the mask lengths.
# cut_off = torch.take_along_dim(sorted_confidence, mask_len.to(torch.long), dim=-1)
cut_off = sorted_confidence.gather(dim=-1, index=mask_len.to(torch.long))
# Masks tokens with lower confidence.
masking = (confidence < cut_off)
return masking
def load(self, is_test=False, prefix=None):
if prefix is not None:
transformer_path = self.transformer_path + prefix + '.pth'
else:
transformer_path = self.transformer_path + '_last.pth'
if self.config.restore or is_test:
# transformer_path = '/home/ubuntu/AmodalVQGAN_pre/check_points/fish_amodal_transformer_gjx/GETransformer_210000.pth'
if os.path.exists(transformer_path):
print('Rank {} is loading {} Transformer...'.format(self.rank, transformer_path))
data = torch.load(transformer_path, map_location="cpu")
|
class C2F_Seg(nn.Module):
def __init__(self, config, g_path, mode, logger=None, save_eval_dict={}):
super(C2F_Seg, self).__init__()
self.config = config
self.iteration = 0
self.sample_iter = 0
self.name = config.model_type
# load g model for mask
self.g_config = Config(os.path.join(g_path, 'vqgan_{}.yml'.format(config.dataset)))
self.g_path = os.path.join(g_path, self.g_config.model_type)
self.root_path = config.path
self.transformer_path = os.path.join(config.path, self.name)
# self.refine_path = os.path.join(config.path, "Refine")
self.trans_size = config.trans_size
self.mode = mode
self.save_eval_dict = save_eval_dict
self.eps = 1e-6
self.train_sample_iters = config.train_sample_iters
self.g_model = VQModel(self.g_config).to(config.device)
self.img_encoder = Resnet_Encoder().to(config.device)
self.refine_module = Refine_Module().to(config.device)
self.transformer = MaskedTransformer(config).to(config.device)
self.g_model.eval()
self.refine_criterion = nn.BCELoss()
self.criterion = CrossEntropyLoss(num_classes=config.vocab_size+1, device=config.device)
if config.train_with_dec:
if not config.gumbel_softmax:
self.temperature = nn.Parameter(torch.tensor([config.tp], dtype=torch.float32),
requires_grad=True).to(config.device)
if config.use_vgg:
vgg = VGG19(pretrained=True, vgg_norm=config.vgg_norm).to(config.device)
vgg.eval()
reduction = 'mean' if config.balanced_loss is False else 'none'
self.perceptual_loss = PerceptualLoss(vgg, weights=config.vgg_weights,
reduction=reduction).to(config.device)
else:
self.perceptual_loss = None
if config.init_gpt_with_vqvae:
self.transformer.z_emb.weight = self.g_model.quantize.embedding.weight
if logger is not None:
logger.info('Gen Parameters:{}'.format(torch_show_all_params(self.g_model)))
logger.info('Transformer Parameters:{}'.format(torch_show_all_params(self.transformer)))
else:
print('Gen Parameters:{}'.format(torch_show_all_params(self.g_model)))
print('Transformer Parameters:{}'.format(torch_show_all_params(self.transformer)))
# loss
no_decay = ['bias', 'ln1.bias', 'ln1.weight', 'ln2.bias', 'ln2.weight']
ignored_param = ['z_emb.weight', 'c_emb.weight']
param_optimizer = self.transformer.named_parameters()
param_optimizer_encoder = self.img_encoder.named_parameters()
param_optimizer_refine= self.refine_module.named_parameters()
optimizer_parameters = [
{'params': [p for n, p in param_optimizer if not any([nd in n for nd in no_decay])],
'weight_decay': config.weight_decay},
{'params': [p for n, p in param_optimizer if any([nd in n for nd in no_decay])],
'weight_decay': 0.0},
{'params': [p for n, p in param_optimizer_encoder], 'weight_decay': config.weight_decay},
{'params': [p for n, p in param_optimizer_refine], 'weight_decay': config.weight_decay},
]
self.opt = AdamW(params=optimizer_parameters,
lr=float(config.lr), betas=(config.beta1, config.beta2))
self.sche = get_linear_schedule_with_warmup(self.opt, num_warmup_steps=config.warmup_iters,
num_training_steps=config.max_iters)
self.rank = dist.get_rank()
self.gamma = self.gamma_func(mode=config.gamma_mode)
self.mask_token_idx = config.vocab_size
self.choice_temperature = 4.5
self.Image_W = config.Image_W
self.Image_H = config.Image_H
self.patch_W = config.patch_W
self.patch_H = config.patch_H
@torch.no_grad()
def encode_to_z(self, x, mask=None):
if len(x.size())==5:
x = x[0]
x = x.permute((1,0,2,3))
quant_z, _, info = self.g_model.encode(x.float(), mask) # [B,D,H,W]
indices = info[2].view(quant_z.shape[0], -1) # [B, L]
return quant_z, indices
def data_augmentation(self, mask):
w = random.randint(5, 11)
h = random.randint(5, 11)
rdv = random.random()
n_repeat = random.randint(1, 3)
max_pool = nn.MaxPool2d(kernel_size=(w, h), stride=1, padding=(w//2, h//2))
if rdv < 0.3:
for i in range(n_repeat):
mask = max_pool(mask)
elif rdv >=0.3 and rdv < 0.6:
for i in range(n_repeat):
mask = -max_pool(-mask)
else:
mask = mask
return mask
def get_attn_map(self, feature, guidance):
guidance = F.interpolate(guidance, scale_factor=(1/16))
b,c,h,w = guidance.shape
q = torch.flatten(guidance, start_dim=2)
v = torch.flatten(feature, start_dim=2)
k = v * q
k = k.sum(dim=-1, keepdim=True) / (q.sum(dim=-1, keepdim=True) + 1e-6)
attn = (k.transpose(-2, -1) @ v) / 1
attn = F.softmax(attn, dim=-1)
attn = attn.reshape(b, c, h, w)
return attn
def get_losses(self, meta):
self.iteration += 1
z_loss = 0
img_feat = self.img_encoder(meta['img_crop'].squeeze().permute((0,3,1,2)).to(torch.float32))
_, src_indices = self.encode_to_z(meta['vm_crop'])
_, tgt_indices = self.encode_to_z(meta['fm_crop'])
bhwc = (_.shape[0], _.shape[2], _.shape[3], _.shape[1])
r = np.maximum(self.gamma(np.random.uniform()), self.config.min_mask_rate)
r = math.floor(r * tgt_indices.shape[1])
sample = torch.rand(tgt_indices.shape, device=tgt_indices.device).topk(r, dim=1).indices
random_mask = torch.zeros(tgt_indices.shape, dtype=torch.bool, device=tgt_indices.device)
random_mask.scatter_(dim=1, index=sample, value=True) # [B, L]
# concat mask
mask = random_mask
masked_indices = self.mask_token_idx * torch.ones_like(tgt_indices, device=tgt_indices.device) # [B, L]
z_indices = (~mask) * tgt_indices + mask * masked_indices # [B, L]
logits_z = self.transformer(img_feat[-1], src_indices, z_indices, mask=None)
target = tgt_indices
z_loss = self.criterion(logits_z.view(-1, logits_z.size(-1)), target.view(-1))
with torch.no_grad():
logits_z = logits_z[..., :-1]
logits_z = self.top_k_logits(logits_z, k=5)
probs = F.softmax(logits_z, dim=-1)
seq_ids = torch.distributions.categorical.Categorical(probs=probs).sample() # [B, L]
quant_z = self.g_model.quantize.get_codebook_entry(seq_ids.reshape(-1), shape=bhwc)
pred_fm_crop = self.g_model.decode(quant_z)
pred_fm_crop = pred_fm_crop.mean(dim=1, keepdim=True)
pred_fm_crop = torch.clamp(pred_fm_crop, min=0, max=1)
pred_vm_crop, pred_fm_crop = self.refine_module(img_feat, pred_fm_crop.detach())
pred_vm_crop = F.interpolate(pred_vm_crop, size=(256, 256), mode="nearest")
pred_vm_crop = torch.sigmoid(pred_vm_crop)
loss_vm = self.refine_criterion(pred_vm_crop, meta['vm_crop_gt'].transpose(1,0))
pred_fm_crop = F.interpolate(pred_fm_crop, size=(256, 256), mode="nearest")
pred_fm_crop = torch.sigmoid(pred_fm_crop)
loss_fm = self.refine_criterion(pred_fm_crop, meta['fm_crop'].transpose(1,0))
logs = [
("z_loss", z_loss.item()),
("loss_vm", loss_vm.item()),
("loss_fm", loss_fm.item()),
]
return z_loss, loss_vm+loss_fm, logs
def align_raw_size(self, full_mask, obj_position, vm_pad, meta):
vm_np_crop = meta["vm_no_crop"].squeeze()
H, W = vm_np_crop.shape[-2], vm_np_crop.shape[-1]
bz, seq_len = full_mask.shape[:2]
new_full_mask = torch.zeros((bz, seq_len, H, W)).to(torch.float32).cuda()
if len(vm_pad.shape)==3:
vm_pad = vm_pad[0]
obj_position = obj_position[0]
for b in range(bz):
paddings = vm_pad[b]
position = obj_position[b]
new_fm = full_mask[
b, :,
:-int(paddings[0]) if int(paddings[0]) !=0 else None,
:-int(paddings[1]) if int(paddings[1]) !=0 else None
]
vx_min = int(position[0])
vx_max = min(H, int(position[1])+1)
vy_min = int(position[2])
vy_max = min(W, int(position[3])+1)
resize = transforms.Resize([vx_max-vx_min, vy_max-vy_min])
try:
new_fm = resize(new_fm)
new_full_mask[b, :, vx_min:vx_max, vy_min:vy_max] = new_fm[0]
except:
new_fm = new_fm
return new_full_mask
def loss_and_evaluation(self, pred_fm, meta):
loss_eval = {}
pred_fm = pred_fm.squeeze()
loss_mask = meta["loss_mask"].squeeze().to(pred_fm.device)
counts = meta["counts"].reshape(-1).to(pred_fm.device)
fm_no_crop = meta["fm_no_crop"].squeeze()
vm_no_crop = meta["vm_no_crop"].squeeze()
pred_fm = (pred_fm > 0.5).to(torch.int64)
full_iou = video_iou(pred_fm, fm_no_crop)
occ_iou = video_iou(pred_fm-vm_no_crop, fm_no_crop-vm_no_crop)
m_full_iou = (counts * full_iou).sum() / counts.sum()
m_occ_iou = (counts * occ_iou).sum() / counts.sum()
loss_eval["iou"] = m_full_iou
loss_eval["invisible_iou_"] = m_occ_iou
loss_eval["iou_count"] = torch.Tensor([1]).cuda()
loss_eval["occ_count"] = torch.Tensor([1]).cuda()
# post-process
pred_fm = pred_fm * (1 - loss_mask) + vm_no_crop * loss_mask
pred_fm = (pred_fm > 0.5).to(torch.int64)
full_iou = video_iou(pred_fm, fm_no_crop)
occ_iou = video_iou(pred_fm-vm_no_crop, fm_no_crop-vm_no_crop)
m_full_iou = (counts * full_iou).sum() / counts.sum()
m_occ_iou = (counts * occ_iou).sum() / counts.sum()
loss_eval["iou_post"] = m_full_iou
loss_eval["invisible_iou_post"] = m_occ_iou
return loss_eval
def backward(self, loss=None):
self.opt.zero_grad()
loss.backward()
self.opt.step()
self.sche.step()
def top_k_logits(self, logits, k):
v, ix = torch.topk(logits, k)
out = logits.clone()
out[out < v[..., [-1]]] = -float('Inf')
return out
@torch.no_grad()
def batch_predict_maskgit(self, meta, iter, mode, temperature=1.0, T=3, start_iter=0):
'''
:param x:[B,3,H,W] image
:param c:[b,X,H,W] condition
:param mask: [1,1,H,W] mask
'''
self.sample_iter += 1
img_feat = self.img_encoder(meta['img_crop'].squeeze().permute((0,3,1,2)).to(torch.float32))
_, src_indices = self.encode_to_z(meta['vm_crop'])
# _, tgt_indices = self.encode_to_z(meta['fm_crop'])
bhwc = (_.shape[0], _.shape[2], _.shape[3], _.shape[1])
masked_indices = self.mask_token_idx * torch.ones_like(src_indices, device=src_indices.device) # [B, L]
unknown_number_in_the_beginning = torch.sum(masked_indices == self.mask_token_idx, dim=-1) # [B]
gamma = self.gamma_func("cosine")
cur_ids = masked_indices # [B, L]
seq_out = []
mask_out = []
for t in range(start_iter, T):
logits = self.transformer(img_feat[-1], src_indices, cur_ids, mask=None) # [B, L, N]
logits = logits[..., :-1]
logits = self.top_k_logits(logits, k=3)
probs = F.softmax(logits, dim=-1) # convert logits into probs [B, 256, vocab_size+1]
sampled_ids = torch.distributions.categorical.Categorical(probs=probs).sample() # [B, L]
unknown_map = (cur_ids == self.mask_token_idx) # which tokens need to be sampled -> bool [B, 256]
sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) # replace all -1 with their samples and leave the others untouched [B, 256]
seq_out.append(sampled_ids)
mask_out.append(1. * unknown_map)
ratio = 1. * (t + 1) / T # just a percentage e.g. 1 / 12
mask_ratio = gamma(ratio)
selected_probs = probs.gather(dim=-1, index=sampled_ids.unsqueeze(-1)).squeeze(-1)
selected_probs = torch.where(unknown_map, selected_probs, torch.Tensor([np.inf]).to(logits.device)) # ignore tokens which are already sampled [B, 256]
mask_len = torch.unsqueeze(torch.floor(unknown_number_in_the_beginning * mask_ratio), 1) # floor(256 * 0.99) = 254 --> [254, 254, 254, 254, ....] (B x 1)
mask_len = torch.maximum(torch.ones_like(mask_len), torch.minimum(torch.sum(unknown_map, dim=-1, keepdim=True) - 1, mask_len))
# Adds noise for randomness
masking = self.mask_by_random_topk(mask_len, selected_probs, temperature=self.choice_temperature * (1. - ratio))
# Masks tokens with lower confidence.
cur_ids = torch.where(masking, self.mask_token_idx, sampled_ids) # [B, L]
seq_ids = torch.stack(seq_out, dim=1) # [B, T, L]
quant_z = self.g_model.quantize.get_codebook_entry(seq_ids[:,-1,:].reshape(-1), shape=bhwc)
pred_fm_crop = self.g_model.decode(quant_z)
pred_fm_crop = pred_fm_crop.mean(dim=1, keepdim=True)
pred_fm_crop_old = torch.clamp(pred_fm_crop, min=0, max=1)
pred_vm_crop, pred_fm_crop = self.refine_module(img_feat, pred_fm_crop_old)
pred_vm_crop = F.interpolate(pred_vm_crop, size=(256, 256), mode="nearest")
pred_vm_crop = torch.sigmoid(pred_vm_crop)
loss_vm = self.refine_criterion(pred_vm_crop, meta['vm_crop'].transpose(1,0))
# pred_vm_crop = (pred_vm_crop>=0.5).to(torch.float32)
pred_fm_crop = F.interpolate(pred_fm_crop, size=(256, 256), mode="nearest")
pred_fm_crop = torch.sigmoid(pred_fm_crop)
loss_fm = self.refine_criterion(pred_fm_crop, meta['fm_crop'].transpose(1,0))
# pred_fm_crop = (pred_fm_crop>=0.5).to(torch.float32)
pred_fm_crop_old = self.align_raw_size(pred_fm_crop_old, meta['obj_position'], meta["vm_pad"], meta)
# pred_vm = self.align_raw_size(pred_vm_crop, meta['obj_position'], meta["vm_pad"], meta)
pred_fm = self.align_raw_size(pred_fm_crop, meta['obj_position'], meta["vm_pad"], meta)
pred_fm = pred_fm + pred_fm_crop_old
loss_eval = self.loss_and_evaluation(pred_fm, meta)
loss_eval["loss_fm"] = loss_fm
loss_eval["loss_vm"] = loss_vm
return loss_eval
def create_inputs_tokens_normal(self, num, device):
self.num_latent_size = self.config['resolution'] // self.config['patch_size']
blank_tokens = torch.ones((num, self.num_latent_size ** 2), device=device)
masked_tokens = self.mask_token_idx * blank_tokens
return masked_tokens.to(torch.int64)
def gamma_func(self, mode="cosine"):
if mode == "linear":
return lambda r: 1 - r
elif mode == "cosine":
return lambda r: np.cos(r * np.pi / 2)
elif mode == "square":
return lambda r: 1 - r ** 2
elif mode == "cubic":
return lambda r: 1 - r ** 3
elif mode == "log":
return lambda r, total_unknown: - np.log2(r) / np.log2(total_unknown)
else:
raise NotImplementedError
def mask_by_random_topk(self, mask_len, probs, temperature=1.0):
confidence = torch.log(probs) + temperature * torch.distributions.gumbel.Gumbel(0, 1).sample(probs.shape).to(probs.device)
sorted_confidence, _ = torch.sort(confidence, dim=-1) # from small to large
# Obtains cut off threshold given the mask lengths.
# cut_off = torch.take_along_dim(sorted_confidence, mask_len.to(torch.long), dim=-1)
cut_off = sorted_confidence.gather(dim=-1, index=mask_len.to(torch.long))
# Masks tokens with lower confidence.
masking = (confidence < cut_off)
return masking
def load(self, is_test=False, prefix=None):
if prefix is not None:
transformer_path = self.transformer_path + prefix + '.pth'
else:
transformer_path = self.transformer_path + '_last.pth'
if self.config.restore or is_test:
# transformer_path = '/home/ubuntu/AmodalVQGAN_pre/check_points/fish_amodal_transformer_gjx/GETransformer_210000.pth'
if os.path.exists(transformer_path):
print('Rank {} is loading {} Transformer...'.format(self.rank, transformer_path))
data = torch.load(transformer_path, map_location="cpu") | torch_init_model(self.transformer, transformer_path, 'model') | 9 | 2023-12-21 04:25:47+00:00 | 16k |
lipku/metahuman-stream | main.py | [
{
"identifier": "NeRFDataset",
"path": "nerf_triplane/provider.py",
"snippet": "class NeRFDataset:\n def __init__(self, opt, device, type='train', downscale=1):\n super().__init__()\n \n self.opt = opt\n self.device = device\n self.type = type # train, val, test\n ... | import torch
import argparse
from nerf_triplane.provider import NeRFDataset
from nerf_triplane.utils import *
from nerf_triplane.network import NeRFNetwork
from nerf_triplane.gui import NeRFGUI | 11,006 | parser.add_argument('--dt_gamma', type=float, default=1/256, help="dt_gamma (>=0) for adaptive ray marching. set to 0 to disable, >0 to accelerate rendering (but usually with worse quality)")
parser.add_argument('--min_near', type=float, default=0.05, help="minimum near distance for camera")
parser.add_argument('--density_thresh', type=float, default=10, help="threshold for density grid to be occupied (sigma)")
parser.add_argument('--density_thresh_torso', type=float, default=0.01, help="threshold for density grid to be occupied (alpha)")
parser.add_argument('--patch_size', type=int, default=1, help="[experimental] render patches in training, so as to apply LPIPS loss. 1 means disabled, use [64, 32, 16] to enable")
parser.add_argument('--init_lips', action='store_true', help="init lips region")
parser.add_argument('--finetune_lips', action='store_true', help="use LPIPS and landmarks to fine tune lips region")
parser.add_argument('--smooth_lips', action='store_true', help="smooth the enc_a in a exponential decay way...")
parser.add_argument('--torso', action='store_true', help="fix head and train torso")
parser.add_argument('--head_ckpt', type=str, default='', help="head model")
### GUI options
parser.add_argument('--gui', action='store_true', help="start a GUI")
parser.add_argument('--W', type=int, default=450, help="GUI width")
parser.add_argument('--H', type=int, default=450, help="GUI height")
parser.add_argument('--radius', type=float, default=3.35, help="default GUI camera radius from center")
parser.add_argument('--fovy', type=float, default=21.24, help="default GUI camera fovy")
parser.add_argument('--max_spp', type=int, default=1, help="GUI rendering max sample per pixel")
### else
parser.add_argument('--att', type=int, default=2, help="audio attention mode (0 = turn off, 1 = left-direction, 2 = bi-direction)")
parser.add_argument('--aud', type=str, default='', help="audio source (empty will load the default, else should be a path to a npy file)")
parser.add_argument('--emb', action='store_true', help="use audio class + embedding instead of logits")
parser.add_argument('--ind_dim', type=int, default=4, help="individual code dim, 0 to turn off")
parser.add_argument('--ind_num', type=int, default=10000, help="number of individual codes, should be larger than training dataset size")
parser.add_argument('--ind_dim_torso', type=int, default=8, help="individual code dim, 0 to turn off")
parser.add_argument('--amb_dim', type=int, default=2, help="ambient dimension")
parser.add_argument('--part', action='store_true', help="use partial training data (1/10)")
parser.add_argument('--part2', action='store_true', help="use partial training data (first 15s)")
parser.add_argument('--train_camera', action='store_true', help="optimize camera pose")
parser.add_argument('--smooth_path', action='store_true', help="brute-force smooth camera pose trajectory with a window size")
parser.add_argument('--smooth_path_window', type=int, default=7, help="smoothing window size")
# asr
parser.add_argument('--asr', action='store_true', help="load asr for real-time app")
parser.add_argument('--asr_wav', type=str, default='', help="load the wav and use as input")
parser.add_argument('--asr_play', action='store_true', help="play out the audio")
parser.add_argument('--asr_model', type=str, default='deepspeech')
# parser.add_argument('--asr_model', type=str, default='cpierse/wav2vec2-large-xlsr-53-esperanto')
# parser.add_argument('--asr_model', type=str, default='facebook/wav2vec2-large-960h-lv60-self')
parser.add_argument('--asr_save_feats', action='store_true')
# audio FPS
parser.add_argument('--fps', type=int, default=50)
# sliding window left-middle-right length (unit: 20ms)
parser.add_argument('-l', type=int, default=10)
parser.add_argument('-m', type=int, default=50)
parser.add_argument('-r', type=int, default=10)
opt = parser.parse_args()
if opt.O:
opt.fp16 = True
opt.exp_eye = True
if opt.test and False:
opt.smooth_path = True
opt.smooth_eye = True
opt.smooth_lips = True
opt.cuda_ray = True
# assert opt.cuda_ray, "Only support CUDA ray mode."
if opt.patch_size > 1:
# assert opt.patch_size > 16, "patch_size should > 16 to run LPIPS loss."
assert opt.num_rays % (opt.patch_size ** 2) == 0, "patch_size ** 2 should be dividable by num_rays."
# if opt.finetune_lips:
# # do not update density grid in finetune stage
# opt.update_extra_interval = 1e9
print(opt)
seed_everything(opt.seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = NeRFNetwork(opt)
# manually load state dict for head
if opt.torso and opt.head_ckpt != '':
model_dict = torch.load(opt.head_ckpt, map_location='cpu')['model']
missing_keys, unexpected_keys = model.load_state_dict(model_dict, strict=False)
if len(missing_keys) > 0:
print(f"[WARN] missing keys: {missing_keys}")
if len(unexpected_keys) > 0:
print(f"[WARN] unexpected keys: {unexpected_keys}")
# freeze these keys
for k, v in model.named_parameters():
if k in model_dict:
# print(f'[INFO] freeze {k}, {v.shape}')
v.requires_grad = False
# print(model)
criterion = torch.nn.MSELoss(reduction='none')
if opt.test:
if opt.gui:
metrics = [] # use no metric in GUI for faster initialization...
else:
# metrics = [PSNRMeter(), LPIPSMeter(device=device)]
metrics = [PSNRMeter(), LPIPSMeter(device=device), LMDMeter(backend='fan')]
trainer = Trainer('ngp', opt, model, device=device, workspace=opt.workspace, criterion=criterion, fp16=opt.fp16, metrics=metrics, use_checkpoint=opt.ckpt)
if opt.test_train:
|
# torch.autograd.set_detect_anomaly(True)
# Close tf32 features. Fix low numerical accuracy on rtx30xx gpu.
try:
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.allow_tf32 = False
except AttributeError as e:
print('Info. This pytorch version is not support with tf32.')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('path', type=str)
parser.add_argument('-O', action='store_true', help="equals --fp16 --cuda_ray --exp_eye")
parser.add_argument('--test', action='store_true', help="test mode (load model and test dataset)")
parser.add_argument('--test_train', action='store_true', help="test mode (load model and train dataset)")
parser.add_argument('--data_range', type=int, nargs='*', default=[0, -1], help="data range to use")
parser.add_argument('--workspace', type=str, default='workspace')
parser.add_argument('--seed', type=int, default=0)
### training options
parser.add_argument('--iters', type=int, default=200000, help="training iters")
parser.add_argument('--lr', type=float, default=1e-2, help="initial learning rate")
parser.add_argument('--lr_net', type=float, default=1e-3, help="initial learning rate")
parser.add_argument('--ckpt', type=str, default='latest')
parser.add_argument('--num_rays', type=int, default=4096 * 16, help="num rays sampled per image for each training step")
parser.add_argument('--cuda_ray', action='store_true', help="use CUDA raymarching instead of pytorch")
parser.add_argument('--max_steps', type=int, default=16, help="max num steps sampled per ray (only valid when using --cuda_ray)")
parser.add_argument('--num_steps', type=int, default=16, help="num steps sampled per ray (only valid when NOT using --cuda_ray)")
parser.add_argument('--upsample_steps', type=int, default=0, help="num steps up-sampled per ray (only valid when NOT using --cuda_ray)")
parser.add_argument('--update_extra_interval', type=int, default=16, help="iter interval to update extra status (only valid when using --cuda_ray)")
parser.add_argument('--max_ray_batch', type=int, default=4096, help="batch size of rays at inference to avoid OOM (only valid when NOT using --cuda_ray)")
### loss set
parser.add_argument('--warmup_step', type=int, default=10000, help="warm up steps")
parser.add_argument('--amb_aud_loss', type=int, default=1, help="use ambient aud loss")
parser.add_argument('--amb_eye_loss', type=int, default=1, help="use ambient eye loss")
parser.add_argument('--unc_loss', type=int, default=1, help="use uncertainty loss")
parser.add_argument('--lambda_amb', type=float, default=1e-4, help="lambda for ambient loss")
### network backbone options
parser.add_argument('--fp16', action='store_true', help="use amp mixed precision training")
parser.add_argument('--bg_img', type=str, default='', help="background image")
parser.add_argument('--fbg', action='store_true', help="frame-wise bg")
parser.add_argument('--exp_eye', action='store_true', help="explicitly control the eyes")
parser.add_argument('--fix_eye', type=float, default=-1, help="fixed eye area, negative to disable, set to 0-0.3 for a reasonable eye")
parser.add_argument('--smooth_eye', action='store_true', help="smooth the eye area sequence")
parser.add_argument('--torso_shrink', type=float, default=0.8, help="shrink bg coords to allow more flexibility in deform")
### dataset options
parser.add_argument('--color_space', type=str, default='srgb', help="Color space, supports (linear, srgb)")
parser.add_argument('--preload', type=int, default=0, help="0 means load data from disk on-the-fly, 1 means preload to CPU, 2 means GPU.")
# (the default value is for the fox dataset)
parser.add_argument('--bound', type=float, default=1, help="assume the scene is bounded in box[-bound, bound]^3, if > 1, will invoke adaptive ray marching.")
parser.add_argument('--scale', type=float, default=4, help="scale camera location into box[-bound, bound]^3")
parser.add_argument('--offset', type=float, nargs='*', default=[0, 0, 0], help="offset of camera location")
parser.add_argument('--dt_gamma', type=float, default=1/256, help="dt_gamma (>=0) for adaptive ray marching. set to 0 to disable, >0 to accelerate rendering (but usually with worse quality)")
parser.add_argument('--min_near', type=float, default=0.05, help="minimum near distance for camera")
parser.add_argument('--density_thresh', type=float, default=10, help="threshold for density grid to be occupied (sigma)")
parser.add_argument('--density_thresh_torso', type=float, default=0.01, help="threshold for density grid to be occupied (alpha)")
parser.add_argument('--patch_size', type=int, default=1, help="[experimental] render patches in training, so as to apply LPIPS loss. 1 means disabled, use [64, 32, 16] to enable")
parser.add_argument('--init_lips', action='store_true', help="init lips region")
parser.add_argument('--finetune_lips', action='store_true', help="use LPIPS and landmarks to fine tune lips region")
parser.add_argument('--smooth_lips', action='store_true', help="smooth the enc_a in a exponential decay way...")
parser.add_argument('--torso', action='store_true', help="fix head and train torso")
parser.add_argument('--head_ckpt', type=str, default='', help="head model")
### GUI options
parser.add_argument('--gui', action='store_true', help="start a GUI")
parser.add_argument('--W', type=int, default=450, help="GUI width")
parser.add_argument('--H', type=int, default=450, help="GUI height")
parser.add_argument('--radius', type=float, default=3.35, help="default GUI camera radius from center")
parser.add_argument('--fovy', type=float, default=21.24, help="default GUI camera fovy")
parser.add_argument('--max_spp', type=int, default=1, help="GUI rendering max sample per pixel")
### else
parser.add_argument('--att', type=int, default=2, help="audio attention mode (0 = turn off, 1 = left-direction, 2 = bi-direction)")
parser.add_argument('--aud', type=str, default='', help="audio source (empty will load the default, else should be a path to a npy file)")
parser.add_argument('--emb', action='store_true', help="use audio class + embedding instead of logits")
parser.add_argument('--ind_dim', type=int, default=4, help="individual code dim, 0 to turn off")
parser.add_argument('--ind_num', type=int, default=10000, help="number of individual codes, should be larger than training dataset size")
parser.add_argument('--ind_dim_torso', type=int, default=8, help="individual code dim, 0 to turn off")
parser.add_argument('--amb_dim', type=int, default=2, help="ambient dimension")
parser.add_argument('--part', action='store_true', help="use partial training data (1/10)")
parser.add_argument('--part2', action='store_true', help="use partial training data (first 15s)")
parser.add_argument('--train_camera', action='store_true', help="optimize camera pose")
parser.add_argument('--smooth_path', action='store_true', help="brute-force smooth camera pose trajectory with a window size")
parser.add_argument('--smooth_path_window', type=int, default=7, help="smoothing window size")
# asr
parser.add_argument('--asr', action='store_true', help="load asr for real-time app")
parser.add_argument('--asr_wav', type=str, default='', help="load the wav and use as input")
parser.add_argument('--asr_play', action='store_true', help="play out the audio")
parser.add_argument('--asr_model', type=str, default='deepspeech')
# parser.add_argument('--asr_model', type=str, default='cpierse/wav2vec2-large-xlsr-53-esperanto')
# parser.add_argument('--asr_model', type=str, default='facebook/wav2vec2-large-960h-lv60-self')
parser.add_argument('--asr_save_feats', action='store_true')
# audio FPS
parser.add_argument('--fps', type=int, default=50)
# sliding window left-middle-right length (unit: 20ms)
parser.add_argument('-l', type=int, default=10)
parser.add_argument('-m', type=int, default=50)
parser.add_argument('-r', type=int, default=10)
opt = parser.parse_args()
if opt.O:
opt.fp16 = True
opt.exp_eye = True
if opt.test and False:
opt.smooth_path = True
opt.smooth_eye = True
opt.smooth_lips = True
opt.cuda_ray = True
# assert opt.cuda_ray, "Only support CUDA ray mode."
if opt.patch_size > 1:
# assert opt.patch_size > 16, "patch_size should > 16 to run LPIPS loss."
assert opt.num_rays % (opt.patch_size ** 2) == 0, "patch_size ** 2 should be dividable by num_rays."
# if opt.finetune_lips:
# # do not update density grid in finetune stage
# opt.update_extra_interval = 1e9
print(opt)
seed_everything(opt.seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = NeRFNetwork(opt)
# manually load state dict for head
if opt.torso and opt.head_ckpt != '':
model_dict = torch.load(opt.head_ckpt, map_location='cpu')['model']
missing_keys, unexpected_keys = model.load_state_dict(model_dict, strict=False)
if len(missing_keys) > 0:
print(f"[WARN] missing keys: {missing_keys}")
if len(unexpected_keys) > 0:
print(f"[WARN] unexpected keys: {unexpected_keys}")
# freeze these keys
for k, v in model.named_parameters():
if k in model_dict:
# print(f'[INFO] freeze {k}, {v.shape}')
v.requires_grad = False
# print(model)
criterion = torch.nn.MSELoss(reduction='none')
if opt.test:
if opt.gui:
metrics = [] # use no metric in GUI for faster initialization...
else:
# metrics = [PSNRMeter(), LPIPSMeter(device=device)]
metrics = [PSNRMeter(), LPIPSMeter(device=device), LMDMeter(backend='fan')]
trainer = Trainer('ngp', opt, model, device=device, workspace=opt.workspace, criterion=criterion, fp16=opt.fp16, metrics=metrics, use_checkpoint=opt.ckpt)
if opt.test_train: | test_set = NeRFDataset(opt, device=device, type='train') | 0 | 2023-12-19 01:32:46+00:00 | 16k |
MingtaoGuo/AnimateAnyone_unofficial | ldm/models/diffusion/ddpm.py | [
{
"identifier": "log_txt_as_img",
"path": "ldm/util.py",
"snippet": "def log_txt_as_img(wh, xc, size=10):\n # wh a tuple of (width, height)\n # xc a list of captions to plot\n b = len(xc)\n txts = list()\n for bi in range(b):\n txt = Image.new(\"RGB\", wh, color=\"white\")\n ... | import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
import itertools
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager, nullcontext
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from omegaconf import ListConfig
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler | 11,828 | self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
1. - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer('posterior_variance', to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
self.register_buffer('posterior_mean_coef1', to_torch(
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
self.register_buffer('posterior_mean_coef2', to_torch(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
if self.parameterization == "eps":
lvlb_weights = self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
elif self.parameterization == "x0":
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
elif self.parameterization == "v":
lvlb_weights = torch.ones_like(self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
else:
raise NotImplementedError("mu not supported")
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
@torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
if self.make_it_fit:
n_params = len([name for name, _ in
itertools.chain(self.named_parameters(),
self.named_buffers())])
for name, param in tqdm(
itertools.chain(self.named_parameters(),
self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params
):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape) == len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys:\n {missing}")
if len(unexpected) > 0:
print(f"\nUnexpected Keys:\n {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
| """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
make_it_fit=False,
ucg_training=None,
reset_ema=False,
reset_num_ema_updates=False,
):
super().__init__()
assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
self.parameterization = parameterization
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
self.make_it_fit = make_it_fit
if reset_ema: assert exists(ckpt_path)
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
if reset_ema:
assert self.use_ema
print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
self.model_ema = LitEma(self.model)
if reset_num_ema_updates:
print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
assert self.use_ema
self.model_ema.reset_num_updates()
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
else:
self.register_buffer('logvar', logvar)
self.ucg_training = ucg_training or dict()
if self.ucg_training:
self.ucg_prng = np.random.RandomState()
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
1. - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer('posterior_variance', to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
self.register_buffer('posterior_mean_coef1', to_torch(
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
self.register_buffer('posterior_mean_coef2', to_torch(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
if self.parameterization == "eps":
lvlb_weights = self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
elif self.parameterization == "x0":
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
elif self.parameterization == "v":
lvlb_weights = torch.ones_like(self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
else:
raise NotImplementedError("mu not supported")
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
@torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
if self.make_it_fit:
n_params = len([name for name, _ in
itertools.chain(self.named_parameters(),
self.named_buffers())])
for name, param in tqdm(
itertools.chain(self.named_parameters(),
self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params
):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape) == len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys:\n {missing}")
if len(unexpected) > 0:
print(f"\nUnexpected Keys:\n {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
""" | mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) | 14 | 2023-12-16 03:31:33+00:00 | 16k |
yasserben/CLOUDS | train_net.py | [
{
"identifier": "add_maskformer2_config",
"path": "clouds/config.py",
"snippet": "def add_maskformer2_config(cfg):\n \"\"\"\n Add config for MASK_FORMER.\n \"\"\"\n # NOTE: configs from original maskformer\n # data config\n # select the dataset mapper\n cfg.INPUT.DATASET_MAPPER_NAME... | from shapely.errors import ShapelyDeprecationWarning
from collections import OrderedDict
from typing import Any, Dict, List, Set
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import (
MetadataCatalog,
build_detection_train_loader,
build_detection_test_loader,
)
from detectron2.engine import (
DefaultTrainer,
default_argument_parser,
default_setup,
launch,
)
from detectron2.modeling import build_model
from detectron2.evaluation import (
CityscapesInstanceEvaluator,
CityscapesSemSegEvaluator,
COCOEvaluator,
COCOPanopticEvaluator,
DatasetEvaluators,
LVISEvaluator,
SemSegEvaluator,
verify_results,
inference_on_dataset,
print_csv_format,
DatasetEvaluator,
)
from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler
from detectron2.solver.build import maybe_add_gradient_clipping
from detectron2.utils.logger import setup_logger
from detectron2.engine import hooks
from fvcore.nn.precise_bn import get_bn_modules
from clouds import (
CityscapesSemSegEvaluator,
ClassicalSemSegEvaluator,
MapperTrain,
MapperTest,
add_maskformer2_config,
add_clouds_config,
add_wandb_config,
add_prerocessing_training_set_config,
PersoEvalHook,
add_repeat_factors,
)
from clouds.utils import setup_wandb, WandbWriter
import warnings
import copy
import itertools
import logging
import os
import ast
import torch
import detectron2.utils.comm as comm | 12,937 | """
Copyright 2023 Telecom Paris, Yasser BENIGMIM. All rights reserved.
Licensed under the Apache License, Version 2.0
Reference: https://github.com/facebookresearch/Mask2Former/blob/main/train_net.py
CLOUDS Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
try:
# ignore ShapelyDeprecationWarning from fvcore
warnings.filterwarnings("ignore", category=ShapelyDeprecationWarning)
except:
pass
class Trainer(DefaultTrainer):
"""
Extension of the Trainer class adapted to CLOUDS.
"""
def build_writers(self):
writers = super().build_writers()
# use wandb writer instead.
writers[-1] = WandbWriter()
return writers
@classmethod
def build_model(cls, cfg):
"""
Returns:
torch.nn.Module:
It now calls :func:`detectron2.modeling.build_model`.
Overwrite it if you'd like a different model.
"""
model = build_model(cfg)
# logger = logging.getLogger(__name__)
# logger.info("Model:\n{}".format(model))
return model
# @classmethod
# def build_model(cls, cfg):
# """
# Returns:
# torch.nn.Module:
#
# It now calls :func:`detectron2.modeling.build_model`.
# Overwrite it if you'd like a different model.
# """
# model = build_model(cfg)
# # logger = logging.getLogger(__name__)
# # logger.info("Model:\n{}".format(model))
# return model
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each
builtin dataset. For your own dataset, you can simply create an
evaluator manually in your script and do not have to worry about the
hacky if-else logic here.
"""
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
else:
output_folder = os.path.join(cfg.OUTPUT_DIR, output_folder, "inference")
evaluator_list = []
evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
# semantic segmentation
if (
evaluator_type == "bdd_sem_seg"
or evaluator_type == "mapillary_sem_seg"
or evaluator_type == "acdc_sem_seg"
):
evaluator_list.append(
ClassicalSemSegEvaluator(
dataset_name,
distributed=True,
output_dir=output_folder,
save_pl=cfg.MODEL.SAVE_PSEUDO_LABELS,
)
)
# Cityscapes
if evaluator_type == "cityscapes_sem_seg":
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
# return CityscapesSemSegEvaluator(dataset_name)
if cfg.MODEL.SAVE_PSEUDO_LABELS:
return CityscapesSemSegEvaluator(
dataset_name, save_pl=True, output_dir=output_folder
)
else:
return CityscapesSemSegEvaluator(dataset_name)
if len(evaluator_list) == 0:
raise NotImplementedError(
"no Evaluator for the dataset {} with the type {}".format(
dataset_name, evaluator_type
)
)
elif len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
@classmethod
def build_train_loader(cls, cfg):
# Semantic segmentation dataset mapper
| """
Copyright 2023 Telecom Paris, Yasser BENIGMIM. All rights reserved.
Licensed under the Apache License, Version 2.0
Reference: https://github.com/facebookresearch/Mask2Former/blob/main/train_net.py
CLOUDS Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
try:
# ignore ShapelyDeprecationWarning from fvcore
warnings.filterwarnings("ignore", category=ShapelyDeprecationWarning)
except:
pass
class Trainer(DefaultTrainer):
"""
Extension of the Trainer class adapted to CLOUDS.
"""
def build_writers(self):
writers = super().build_writers()
# use wandb writer instead.
writers[-1] = WandbWriter()
return writers
@classmethod
def build_model(cls, cfg):
"""
Returns:
torch.nn.Module:
It now calls :func:`detectron2.modeling.build_model`.
Overwrite it if you'd like a different model.
"""
model = build_model(cfg)
# logger = logging.getLogger(__name__)
# logger.info("Model:\n{}".format(model))
return model
# @classmethod
# def build_model(cls, cfg):
# """
# Returns:
# torch.nn.Module:
#
# It now calls :func:`detectron2.modeling.build_model`.
# Overwrite it if you'd like a different model.
# """
# model = build_model(cfg)
# # logger = logging.getLogger(__name__)
# # logger.info("Model:\n{}".format(model))
# return model
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each
builtin dataset. For your own dataset, you can simply create an
evaluator manually in your script and do not have to worry about the
hacky if-else logic here.
"""
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
else:
output_folder = os.path.join(cfg.OUTPUT_DIR, output_folder, "inference")
evaluator_list = []
evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
# semantic segmentation
if (
evaluator_type == "bdd_sem_seg"
or evaluator_type == "mapillary_sem_seg"
or evaluator_type == "acdc_sem_seg"
):
evaluator_list.append(
ClassicalSemSegEvaluator(
dataset_name,
distributed=True,
output_dir=output_folder,
save_pl=cfg.MODEL.SAVE_PSEUDO_LABELS,
)
)
# Cityscapes
if evaluator_type == "cityscapes_sem_seg":
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
# return CityscapesSemSegEvaluator(dataset_name)
if cfg.MODEL.SAVE_PSEUDO_LABELS:
return CityscapesSemSegEvaluator(
dataset_name, save_pl=True, output_dir=output_folder
)
else:
return CityscapesSemSegEvaluator(dataset_name)
if len(evaluator_list) == 0:
raise NotImplementedError(
"no Evaluator for the dataset {} with the type {}".format(
dataset_name, evaluator_type
)
)
elif len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
@classmethod
def build_train_loader(cls, cfg):
# Semantic segmentation dataset mapper | mapper = MapperTrain(cfg, True) | 5 | 2023-12-15 15:40:58+00:00 | 16k |
modelscope/scepter | scepter/studio/preprocess/preprocess.py | [
{
"identifier": "Config",
"path": "scepter/modules/utils/config.py",
"snippet": "class Config(object):\n def __init__(self,\n cfg_dict={},\n load=True,\n cfg_file=None,\n logger=None,\n parser_ins=None):\n '''\n ... | import os.path
import gradio as gr
from scepter.modules.utils.config import Config
from scepter.modules.utils.file_system import FS
from scepter.studio.preprocess.caption_editor_ui.create_dataset_ui import \
CreateDatasetUI
from scepter.studio.preprocess.caption_editor_ui.dataset_gallery_ui import \
DatasetGalleryUI
from scepter.studio.preprocess.caption_editor_ui.export_dataset_ui import \
ExportDatasetUI
from scepter.studio.utils.env import init_env | 13,149 | # -*- coding: utf-8 -*-
class PreprocessUI():
def __init__(self,
cfg_general_file,
is_debug=False,
language='en',
root_work_dir='./'):
cfg_general = Config(cfg_file=cfg_general_file)
cfg_general.WORK_DIR = os.path.join(root_work_dir,
cfg_general.WORK_DIR)
| # -*- coding: utf-8 -*-
class PreprocessUI():
def __init__(self,
cfg_general_file,
is_debug=False,
language='en',
root_work_dir='./'):
cfg_general = Config(cfg_file=cfg_general_file)
cfg_general.WORK_DIR = os.path.join(root_work_dir,
cfg_general.WORK_DIR) | if not FS.exists(cfg_general.WORK_DIR): | 1 | 2023-12-21 02:01:48+00:00 | 16k |
RomGai/BrainVis | dc_ldm/models/diffusion/ddpm.py | [
{
"identifier": "log_txt_as_img",
"path": "dc_ldm/util.py",
"snippet": "def log_txt_as_img(wh, xc, size=10):\n # wh a tuple of (width, height)\n # xc a list of captions to plot\n b = len(xc)\n txts = list()\n for bi in range(b):\n txt = Image.new(\"RGB\", wh, color=\"white\")\n ... | import os
import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
import torch.nn.functional as F
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.rank_zero import rank_zero_only
from dc_ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from dc_ldm.modules.ema import LitEma
from dc_ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from dc_ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from dc_ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from dc_ldm.models.diffusion.ddim import DDIMSampler
from dc_ldm.models.diffusion.plms import PLMSSampler
from PIL import Image
from eval_metrics import get_similarity_metric
from dc_ldm.modules.encoders.modules import FrozenImageEmbedder | 14,227 | b = batch_size if batch_size is not None else shape[0]
shape = [batch_size] + list(shape)
else:
b = batch_size = shape[0]
if x_T is None:
img = torch.randn(shape, device=self.device)
else:
img = x_T
intermediates = []
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
total=timesteps) if verbose else reversed(
range(0, timesteps))
if type(temperature) == float:
temperature = [temperature] * timesteps
for i in iterator:
ts = torch.full((b,), i, device=self.device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img, x0_partial = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised, return_x0=True,
temperature=temperature[i], noise_dropout=noise_dropout,
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
if mask is not None:
assert x0 is not None
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(x0_partial)
if callback: callback(i)
if img_callback: img_callback(img, i)
return img, intermediates
@torch.no_grad()
def p_sample_loop(self, cond, shape, return_intermediates=False,
x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
device = self.betas.device
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
intermediates = [img]
if timesteps is None:
timesteps = self.num_timesteps
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
range(0, timesteps))
if mask is not None:
assert x0 is not None
assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
for i in iterator:
ts = torch.full((b,), i, device=device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised)
if mask is not None:
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(img)
if callback: callback(i)
if img_callback: img_callback(img, i)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
verbose=True, timesteps=None, quantize_denoised=False,
mask=None, x0=None, shape=None,**kwargs):
if shape is None:
shape = (batch_size, self.channels, self.image_size, self.image_size)
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
return self.p_sample_loop(cond,
shape,
return_intermediates=return_intermediates, x_T=x_T,
verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
mask=mask, x0=x0)
@torch.no_grad()
def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
if ddim:
| """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
ddim_steps=300
):
super().__init__()
assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
self.validation_count = 0
self.ddim_steps = ddim_steps
self.return_cond = False
self.output_path = None
self.main_config = None
self.best_val = 0.0
self.run_full_validation_threshold = 0.0
self.eval_avg = True
def re_init_ema(self):
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
1. - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer('posterior_variance', to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
self.register_buffer('posterior_mean_coef1', to_torch(
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
self.register_buffer('posterior_mean_coef2', to_torch(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
if self.parameterization == "eps":
lvlb_weights = self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
elif self.parameterization == "x0":
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop((batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates)
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
def get_loss(self, pred, target, mean=True):
if self.loss_type == 'l1':
loss = (target - pred).abs()
if mean:
loss = loss.mean()
elif self.loss_type == 'l2':
if mean:
loss = torch.nn.functional.mse_loss(target, pred)
else:
loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
return loss
def p_losses(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_out = self.model(x_noisy, t)
loss_dict = {}
if self.parameterization == "eps":
target = noise
elif self.parameterization == "x0":
target = x_start
else:
raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
log_prefix = 'train' if self.training else 'val'
loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
loss_simple = loss.mean() * self.l_simple_weight
loss_vlb = (self.lvlb_weights[t] * loss).mean()
loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
loss = loss_simple + self.original_elbo_weight * loss_vlb
loss_dict.update({f'{log_prefix}/loss': loss})
return loss, loss_dict
def forward(self, x, *args, **kwargs):
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
return self.p_losses(x, t, *args, **kwargs)
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = rearrange(x, 'b h w c -> b c h w')
x = x.to(memory_format=torch.contiguous_format).float()
return x
def shared_step(self, batch):
x = self.get_input(batch, self.first_stage_key)
loss, loss_dict = self(x)
return loss, loss_dict
def training_step(self, batch, batch_idx):
self.train()
self.cond_stage_model.train() ###到底是在哪里训练的
loss, loss_dict = self.shared_step(batch)
self.log_dict(loss_dict, prog_bar=True,
logger=True, on_step=False, on_epoch=True)
if self.use_scheduler:
lr = self.optimizers().param_groups[0]['lr']
self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=False, on_epoch=True)
return loss
@torch.no_grad()
def generate(self, data, num_samples, ddim_steps=300, HW=None, limit=None, state=None):
# fmri_embedding: n, seq_len, embed_dim
all_samples = []
if HW is None:
shape = (self.p_channels,
self.p_image_size, self.p_image_size)
else:
num_resolutions = len(self.ch_mult)
shape = (self.p_channels,
HW[0] // 2**(num_resolutions-1), HW[1] // 2**(num_resolutions-1))
model = self
sampler = PLMSSampler(model)
# sampler = DDIMSampler(model)
model.eval()
if torch.cuda.is_available():
state = torch.cuda.get_rng_state() if state is None else state
torch.cuda.set_rng_state(state)
else:
state = torch.get_rng_state() if state is None else state
torch.set_rng_state(state)
# rng = torch.Generator(device=self.device).manual_seed(2022).set_state(state)
# state = torch.cuda.get_rng_state()
with model.ema_scope():
for count, item in enumerate(zip(data['eeg'], data['image'])):
if limit is not None:
if count >= limit:
break
latent = item[0] # fmri embedding
gt_image = rearrange(item[1], 'h w c -> 1 c h w') # h w c
print(f"rendering {num_samples} examples in {ddim_steps} steps.")
# c = model.get_learned_conditioning(repeat(latent, 'h w -> c h w', c=num_samples).to(self.device))
c, re_latent = model.get_learned_conditioning(repeat(latent, 'h w -> c h w', c=num_samples).to(self.device))
samples_ddim, _ = sampler.sample(S=ddim_steps,
conditioning=c,
batch_size=num_samples,
shape=shape,
verbose=False,
generator=None)
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0,min=0.0, max=1.0)
gt_image = torch.clamp((gt_image+1.0)/2.0,min=0.0, max=1.0)
all_samples.append(torch.cat([gt_image.detach().cpu(), x_samples_ddim.detach().cpu()], dim=0)) # put groundtruth at first
# display as grid
grid = torch.stack(all_samples, 0)
grid = rearrange(grid, 'n b c h w -> (n b) c h w')
grid = make_grid(grid, nrow=num_samples+1)
# to image
grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
return grid, (255. * torch.stack(all_samples, 0).cpu().numpy()).astype(np.uint8), state
def save_images(self, all_samples, suffix=0):
# print('output_path')
# print(self.output_path)
if self.output_path is not None:
os.makedirs(os.path.join(self.output_path, 'val', f'{self.validation_count}_{suffix}'), exist_ok=True)
for sp_idx, imgs in enumerate(all_samples):
# for copy_idx, img in enumerate(imgs[1:]):
for copy_idx, img in enumerate(imgs):
img = rearrange(img, 'c h w -> h w c')
Image.fromarray(img).save(os.path.join(self.output_path, 'val',
f'{self.validation_count}_{suffix}', f'test{sp_idx}-{copy_idx}.png'))
def full_validation(self, batch, state=None):
print('###### run full validation! ######\n')
grid, all_samples, state = self.generate(batch, ddim_steps=self.ddim_steps, num_samples=5, limit=None, state=state)
metric, metric_list = self.get_eval_metric(all_samples)
self.save_images(all_samples, suffix='%.4f'%metric[-1])
metric_dict = {f'val/{k}_full':v for k, v in zip(metric_list, metric)}
# self.logger.log_metrics(metric_dict)
grid_imgs = Image.fromarray(grid.astype(np.uint8))
# self.logger.log_image(key=f'samples_test_full', images=[grid_imgs])
if metric[-1] > self.best_val:
self.best_val = metric[-1]
torch.save(
{
'model_state_dict': self.state_dict(),
'config': self.main_config,
'state': state
},
os.path.join(self.output_path, 'checkpoint_best.pth')
)
@torch.no_grad()
def validation_step(self, batch, batch_idx):
if batch_idx != 0:
return
if self.validation_count % 5 == 0 and self.trainer.current_epoch != 0:
self.full_validation(batch)
else:
# pass
grid, all_samples, state = self.generate(batch, ddim_steps=self.ddim_steps, num_samples=3, limit=5)
metric, metric_list = self.get_eval_metric(all_samples, avg=self.eval_avg)
grid_imgs = Image.fromarray(grid.astype(np.uint8))
# self.logger.log_image(key=f'samples_test', images=[grid_imgs])
metric_dict = {f'val/{k}':v for k, v in zip(metric_list, metric)}
# self.logger.log_metrics(metric_dict)
if metric[-1] > self.run_full_validation_threshold:
self.full_validation(batch, state=state)
self.validation_count += 1
def get_eval_metric(self, samples, avg=True):
metric_list = ['mse', 'pcc', 'ssim', 'psm']
res_list = []
gt_images = [img[0] for img in samples]
gt_images = rearrange(np.stack(gt_images), 'n c h w -> n h w c')
samples_to_run = np.arange(1, len(samples[0])) if avg else [1]
for m in metric_list:
res_part = []
for s in samples_to_run:
pred_images = [img[s] for img in samples]
pred_images = rearrange(np.stack(pred_images), 'n c h w -> n h w c')
res = get_similarity_metric(pred_images, gt_images, method='pair-wise', metric_name=m)
res_part.append(np.mean(res))
res_list.append(np.mean(res_part))
res_part = []
for s in samples_to_run:
pred_images = [img[s] for img in samples]
pred_images = rearrange(np.stack(pred_images), 'n c h w -> n h w c')
res = get_similarity_metric(pred_images, gt_images, 'class', None,
n_way=50, num_trials=50, top_k=1, device='cuda')
res_part.append(np.mean(res))
res_list.append(np.mean(res_part))
res_list.append(np.max(res_part))
metric_list.append('top-1-class')
metric_list.append('top-1-class (max)')
return res_list, metric_list
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self.model)
def _get_rows_from_list(self, samples):
n_imgs_per_row = len(samples)
denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
@torch.no_grad()
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
log = dict()
x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
x = x.to(self.device)[:N]
log["inputs"] = x
# get diffusion row
diffusion_row = list()
x_start = x[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(x_start)
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
diffusion_row.append(x_noisy)
log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
log["samples"] = samples
log["denoise_row"] = self._get_rows_from_list(denoise_row)
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.model.parameters())
if self.learn_logvar:
params = params + [self.logvar]
opt = torch.optim.AdamW(params, lr=lr)
return opt
class LatentDiffusion(DDPM):
"""main class"""
def __init__(self,
first_stage_config,
cond_stage_config,
num_timesteps_cond=None,
cond_stage_key="image",
cond_stage_trainable=True,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
*args, **kwargs):
self.num_timesteps_cond = default(num_timesteps_cond, 1)
self.scale_by_std = scale_by_std
assert self.num_timesteps_cond <= kwargs['timesteps']
# for backwards compatibility after implementation of DiffusionWrapper
if conditioning_key is None:
conditioning_key = 'concat' if concat_mode else 'crossattn'
if cond_stage_config == '__is_unconditional__':
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.cond_stage_key = cond_stage_key
try:
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
except:
self.num_downs = 0
if not scale_by_std:
self.scale_factor = scale_factor
else:
self.register_buffer('scale_factor', torch.tensor(scale_factor))
self.instantiate_first_stage(first_stage_config)
self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward
self.clip_denoised = False
self.bbox_tokenizer = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys)
self.restarted_from_ckpt = True
self.train_cond_stage_only = False
self.clip_tune = True
if self.clip_tune:
self.image_embedder = FrozenImageEmbedder()
self.cls_tune = False
def make_cond_schedule(self, ):
self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
self.cond_ids[:self.num_timesteps_cond] = ids
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
# only for very first batch
if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
# set rescale weight to 1./std of encodings
print("### USING STD-RESCALING ###")
x = super().get_input(batch, self.first_stage_key)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
del self.scale_factor
self.register_buffer('scale_factor', 1. / z.flatten().std())
print(f"setting self.scale_factor to {self.scale_factor}")
print("### USING STD-RESCALING ###")
def register_schedule(self,
given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
self.make_cond_schedule()
def instantiate_first_stage(self, config):
model = instantiate_from_config(config)
self.first_stage_model = model.eval()
def freeze_diffusion_model(self):
for param in self.model.parameters():
param.requires_grad = False
def unfreeze_diffusion_model(self):
for param in self.model.parameters():
param.requires_grad = True
def freeze_cond_stage(self):
for param in self.cond_stage_model.parameters():
param.requires_grad = False
def unfreeze_cond_stage(self):
for param in self.cond_stage_model.parameters():
param.requires_grad = True
def freeze_first_stage(self):
self.first_stage_model.trainable = False
for param in self.first_stage_model.parameters():
param.requires_grad = False
def unfreeze_first_stage(self):
self.first_stage_model.trainable = True
for param in self.first_stage_model.parameters():
param.requires_grad = True
def freeze_whole_model(self):
self.first_stage_model.trainable = False
for param in self.parameters():
param.requires_grad = False
def unfreeze_whole_model(self):
self.first_stage_model.trainable = True
for param in self.parameters():
param.requires_grad = True
def instantiate_cond_stage(self, config):
if not self.cond_stage_trainable:
if config == "__is_first_stage__":
print("Using first stage also as cond stage.")
self.cond_stage_model = self.first_stage_model
elif config == "__is_unconditional__":
print(f"Training {self.__class__.__name__} as an unconditional model.")
self.cond_stage_model = None
# self.be_unconditional = True
else:
model = instantiate_from_config(config)
self.cond_stage_model = model.eval()
# self.cond_stage_model.train = disabled_train
for param in self.cond_stage_model.parameters():
param.requires_grad = False
else:
assert config != '__is_first_stage__'
assert config != '__is_unconditional__'
model = instantiate_from_config(config)
self.cond_stage_model = model
def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
denoise_row = []
for zd in tqdm(samples, desc=desc):
denoise_row.append(self.decode_first_stage(zd.to(self.device),
force_not_quantize=force_no_decoder_quantization))
n_imgs_per_row = len(denoise_row)
denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
def get_first_stage_encoding(self, encoder_posterior):
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
z = encoder_posterior.sample()
elif isinstance(encoder_posterior, torch.Tensor):
z = encoder_posterior
else:
raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
return self.scale_factor * z
def get_learned_conditioning(self, c):
# self.cond_stage_model.eval()
if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
c, re_latent = self.cond_stage_model.encode(c)
# c = self.cond_stage_model.encode(c)
else:
c, re_latent = self.cond_stage_model(c)
# c = self.cond_stage_model(c)
# return c
return c, re_latent
def meshgrid(self, h, w):
y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
arr = torch.cat([y, x], dim=-1)
return arr
def delta_border(self, h, w):
"""
:param h: height
:param w: width
:return: normalized distance to image border,
wtith min distance = 0 at border and max dist = 0.5 at image center
"""
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
arr = self.meshgrid(h, w) / lower_right_corner
dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
return edge_dist
def get_weighting(self, h, w, Ly, Lx, device):
weighting = self.delta_border(h, w)
weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
self.split_input_params["clip_max_weight"], )
weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
if self.split_input_params["tie_braker"]:
L_weighting = self.delta_border(Ly, Lx)
L_weighting = torch.clip(L_weighting,
self.split_input_params["clip_min_tie_weight"],
self.split_input_params["clip_max_tie_weight"])
L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
weighting = weighting * L_weighting
return weighting
def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
"""
:param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
"""
bs, nc, h, w = x.shape
# number of crops in image
Ly = (h - kernel_size[0]) // stride[0] + 1
Lx = (w - kernel_size[1]) // stride[1] + 1
if uf == 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
elif uf > 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
dilation=1, padding=0,
stride=(stride[0] * uf, stride[1] * uf))
fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
elif df > 1 and uf == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
dilation=1, padding=0,
stride=(stride[0] // df, stride[1] // df))
fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
else:
raise NotImplementedError
return fold, unfold, normalization, weighting
@torch.no_grad()
def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
cond_key=None, return_original_cond=False, bs=None):
x = super().get_input(batch, k)
if bs is not None:
x = x[:bs]
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
# print('encoder_posterior.shape')
# print(encoder_posterior.shape)
z = self.get_first_stage_encoding(encoder_posterior).detach()
# print('z.shape')
# print(z.shape)
# print(cond_key)
# print(self.cond_stage_key)
# print(cond_key)
if self.model.conditioning_key is not None:
if cond_key is None:
cond_key = self.cond_stage_key
if cond_key != self.first_stage_key:
if cond_key in ['caption', 'coordinates_bbox','fmri', 'eeg']:
xc = batch[cond_key]
elif cond_key == 'class_label':
xc = batch
else:
xc = super().get_input(batch, cond_key).to(self.device)
else:
xc = x
# print('get input')
# print(not self.cond_stage_trainable)
# print(force_c_encode)
if not self.cond_stage_trainable or force_c_encode :
# print('get learned condition')
if isinstance(xc, dict) or isinstance(xc, list):
# import pudb; pudb.set_trace()
c, re_latent = self.get_learned_conditioning(xc)
# c = self.get_learned_conditioning(xc)
else:
c, re_latent = self.get_learned_conditioning(xc.to(self.device))
# c = self.get_learned_conditioning(xc.to(self.device))
else:
c = xc
if bs is not None:
c = c[:bs]
if self.use_positional_encodings:
pos_x, pos_y = self.compute_latent_shifts(batch)
ckey = __conditioning_keys__[self.model.conditioning_key]
c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
else:
c = None
xc = None
if self.use_positional_encodings:
pos_x, pos_y = self.compute_latent_shifts(batch)
c = {'pos_x': pos_x, 'pos_y': pos_y}
out = [z, c , batch['label'], batch['image_raw']]
if return_first_stage_outputs:
xrec = self.decode_first_stage(z)
out.extend([x, xrec])
if return_original_cond:
out.append(xc)
return out
@torch.no_grad()
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
# same as above but without decorator
def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
@torch.no_grad()
def encode_first_stage(self, x):
return self.first_stage_model.encode(x)
def shared_step(self, batch, **kwargs):
self.freeze_first_stage()
# print('share step\'s get input')
x, c, label, image_raw = self.get_input(batch, self.first_stage_key)
# print('get input shape')
# print('x.shape')
# print(x.shape)
# print('c.shape')
# print(c.shape)
if self.return_cond:
loss, cc = self(x, c, label, image_raw)
return loss, cc
else:
loss = self(x, c, label, image_raw)
return loss
def forward(self, x, c, label, image_raw, *args, **kwargs):
# print(self.num_timesteps)
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
# print('t.shape')
# print(t.shape)
if self.model.conditioning_key is not None:
assert c is not None
imgs = c
if self.cond_stage_trainable:
# c = self.get_learned_conditioning(c)
c, re_latent = self.get_learned_conditioning(c)
# print('c.shape')
# print(c.shape)
prefix = 'train' if self.training else 'val'
loss, loss_dict = self.p_losses(x, c, t, *args, **kwargs)
# pre_cls = self.cond_stage_model.get_cls(re_latent)
# rencon = self.cond_stage_model.recon(re_latent)
if self.clip_tune:
image_embeds = self.image_embedder(image_raw)
loss_clip = self.cond_stage_model.get_clip_loss(re_latent, image_embeds)
# loss_recon = self.recon_loss(imgs, rencon)
# loss_cls = self.cls_loss(label, pre_cls)
loss += loss_clip
# loss += loss_cls # loss_recon + #(self.original_elbo_weight * loss_vlb)
# loss_dict.update({f'{prefix}/loss_recon': loss_recon})
# loss_dict.update({f'{prefix}/loss_cls': loss_cls})
loss_dict.update({f'{prefix}/loss_clip': loss_clip})
if self.cls_tune:
pre_cls = self.cond_stage_model.get_cls(re_latent)
loss_cls = self.cls_loss(label, pre_cls)
# image_embeds = self.image_embedder(image_raw)
# loss_clip = self.cond_stage_model.get_clip_loss(re_latent, image_embeds)
# loss_recon = self.recon_loss(imgs, rencon)
# loss_cls = self.cls_loss(label, pre_cls)
loss += loss_cls
# loss += loss_cls # loss_recon + #(self.original_elbo_weight * loss_vlb)
# loss_dict.update({f'{prefix}/loss_recon': loss_recon})
# loss_dict.update({f'{prefix}/loss_cls': loss_cls})
loss_dict.update({f'{prefix}/loss_cls': loss_cls})
# if self.return_cond:
# return self.p_losses(x, c, t, *args, **kwargs), c
# return self.p_losses(x, c, t, *args, **kwargs)
if self.return_cond:
return loss, loss_dict, c
return loss, loss_dict
# def recon_loss(self, )
def recon_loss(self, imgs, pred):
"""
imgs: [N, 1, num_voxels]
pred: [N, L, p]
mask: [N, L], 0 is keep, 1 is remove,
"""
# target = self.patchify(imgs)
loss = (pred - imgs) ** 2
loss = loss.mean()
# loss = loss.mean(dim=-1) # [N, L], mean loss per patch
# loss = (loss * mask).sum() / mask.sum() if mask.sum() != 0 else (loss * mask).sum() # mean loss on removed patches
return loss
def cls_loss(self, label, pred):
return torch.nn.CrossEntropyLoss()(pred, label)
def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
def rescale_bbox(bbox):
x0 = torch.clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
y0 = torch.clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
w = min(bbox[2] / crop_coordinates[2], 1 - x0)
h = min(bbox[3] / crop_coordinates[3], 1 - y0)
return x0, y0, w, h
return [rescale_bbox(b) for b in bboxes]
def apply_model(self, x_noisy, t, cond, return_ids=False):
if isinstance(cond, dict):
# hybrid case, cond is exptected to be a dict
pass
else:
if not isinstance(cond, list):
cond = [cond]
key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
cond = {key: cond}
x_recon = self.model(x_noisy, t, **cond)
# print('x_recon')
# if isinstance(x_recon, tuple):
# print('is tuple')
# # print(len(x_recon))
# # print(x_recon[0].shape)
# else:
# print(x_recon.shape)
if isinstance(x_recon, tuple) and not return_ids:
return x_recon[0]
else:
return x_recon
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
def _prior_bpd(self, x_start):
"""
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
"""
batch_size = x_start.shape[0]
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
return mean_flat(kl_prior) / np.log(2.0)
def p_losses(self, x_start, cond, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
# print('p_losses')
# print('noise.shape')
# print(noise.shape)
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
# print('x_noisy[0].shape')
# print(x_noisy[0].shape)
model_output = self.apply_model(x_noisy, t, cond)
loss_dict = {}
prefix = 'train' if self.training else 'val'
if self.parameterization == "x0":
target = x_start
elif self.parameterization == "eps":
target = noise
else:
raise NotImplementedError()
loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
logvar_t = self.logvar[t].to(self.device)
loss = loss_simple / torch.exp(logvar_t) + logvar_t
# loss = loss_simple / torch.exp(self.logvar) + self.logvar
if self.learn_logvar:
loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
loss_dict.update({'logvar': self.logvar.data.mean()})
loss = self.l_simple_weight * loss.mean()
loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
loss += (self.original_elbo_weight * loss_vlb)
loss_dict.update({f'{prefix}/loss': loss})
return loss, loss_dict
def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
return_x0=False, score_corrector=None, corrector_kwargs=None):
t_in = t
model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
if score_corrector is not None:
assert self.parameterization == "eps"
model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
if return_codebook_ids:
model_out, logits = model_out
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
else:
raise NotImplementedError()
if clip_denoised:
x_recon.clamp_(-1., 1.)
if quantize_denoised:
x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
if return_codebook_ids:
return model_mean, posterior_variance, posterior_log_variance, logits
elif return_x0:
return model_mean, posterior_variance, posterior_log_variance, x_recon
else:
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
return_codebook_ids=False, quantize_denoised=False, return_x0=False,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
b, *_, device = *x.shape, x.device
outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
return_codebook_ids=return_codebook_ids,
quantize_denoised=quantize_denoised,
return_x0=return_x0,
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
if return_x0:
model_mean, _, model_log_variance, x0 = outputs
else:
model_mean, _, model_log_variance = outputs
noise = noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
if return_x0:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
else:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
timesteps = self.num_timesteps
if batch_size is not None:
b = batch_size if batch_size is not None else shape[0]
shape = [batch_size] + list(shape)
else:
b = batch_size = shape[0]
if x_T is None:
img = torch.randn(shape, device=self.device)
else:
img = x_T
intermediates = []
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
total=timesteps) if verbose else reversed(
range(0, timesteps))
if type(temperature) == float:
temperature = [temperature] * timesteps
for i in iterator:
ts = torch.full((b,), i, device=self.device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img, x0_partial = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised, return_x0=True,
temperature=temperature[i], noise_dropout=noise_dropout,
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
if mask is not None:
assert x0 is not None
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(x0_partial)
if callback: callback(i)
if img_callback: img_callback(img, i)
return img, intermediates
@torch.no_grad()
def p_sample_loop(self, cond, shape, return_intermediates=False,
x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
device = self.betas.device
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
intermediates = [img]
if timesteps is None:
timesteps = self.num_timesteps
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
range(0, timesteps))
if mask is not None:
assert x0 is not None
assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
for i in iterator:
ts = torch.full((b,), i, device=device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised)
if mask is not None:
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(img)
if callback: callback(i)
if img_callback: img_callback(img, i)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
verbose=True, timesteps=None, quantize_denoised=False,
mask=None, x0=None, shape=None,**kwargs):
if shape is None:
shape = (batch_size, self.channels, self.image_size, self.image_size)
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
return self.p_sample_loop(cond,
shape,
return_intermediates=return_intermediates, x_T=x_T,
verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
mask=mask, x0=x0)
@torch.no_grad()
def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
if ddim: | ddim_sampler = DDIMSampler(self) | 17 | 2023-12-16 12:52:14+00:00 | 16k |
tonnetonne814/PL-Bert-VITS2 | train_ms.py | [
{
"identifier": "DistributedBucketSampler",
"path": "data_utils.py",
"snippet": "class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):\n \"\"\"\n Maintain similar input lengths in a batch.\n Length groups are specified by boundaries.\n Ex) boundaries = [b1, b2, b3]... | import argparse
import itertools
import json
import math
import os
import logging
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import tqdm
import commons
import models
import utils
from torch import nn, optim
from torch.cuda.amp import GradScaler, autocast
from torch.nn import functional as F
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from data_utils import (DistributedBucketSampler, TextAudioSpeakerCollate,
TextAudioSpeakerLoader)
from losses import discriminator_loss, feature_loss, generator_loss, kl_loss
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from models import (AVAILABLE_DURATION_DISCRIMINATOR_TYPES,
AVAILABLE_FLOW_TYPES,
DurationDiscriminatorV1, DurationDiscriminatorV2,
MultiPeriodDiscriminator, SynthesizerTrn)
from PL_BERT_ja.text.symbols import symbols | 11,091 | train_sampler = DistributedBucketSampler(
train_dataset,
hps.train.batch_size,
[32, 300, 500, 700, 900, 1100, 1300, 1500, 3000],
num_replicas=n_gpus,
rank=rank,
shuffle=True,
)
collate_fn = TextAudioSpeakerCollate()
train_loader = DataLoader(
train_dataset,
num_workers=8,
shuffle=False,
pin_memory=True,
collate_fn=collate_fn,
batch_sampler=train_sampler,
)
if rank == 0:
eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
eval_loader = DataLoader(
eval_dataset,
num_workers=8,
shuffle=False,
batch_size=hps.train.batch_size,
pin_memory=True,
drop_last=False,
collate_fn=collate_fn,
)
# some of these flags are not being used in the code and directly set in hps json file.
# they are kept here for reference and prototyping.
if (
"use_transformer_flows" in hps.model.keys()
and hps.model.use_transformer_flows == True
):
use_transformer_flows = True
transformer_flow_type = hps.model.transformer_flow_type
print(f"Using transformer flows {transformer_flow_type} for VITS2")
assert (
transformer_flow_type in AVAILABLE_FLOW_TYPES
), f"transformer_flow_type must be one of {AVAILABLE_FLOW_TYPES}"
else:
print("Using normal flows for VITS1")
use_transformer_flows = False
if (
"use_spk_conditioned_encoder" in hps.model.keys()
and hps.model.use_spk_conditioned_encoder == True
):
if hps.data.n_speakers == 0:
raise ValueError(
"n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model"
)
use_spk_conditioned_encoder = True
else:
print("Using normal encoder for VITS1")
use_spk_conditioned_encoder = False
if (
"use_noise_scaled_mas" in hps.model.keys()
and hps.model.use_noise_scaled_mas == True
):
print("Using noise scaled MAS for VITS2")
use_noise_scaled_mas = True
mas_noise_scale_initial = 0.01
noise_scale_delta = 2e-6
else:
print("Using normal MAS for VITS1")
use_noise_scaled_mas = False
mas_noise_scale_initial = 0.0
noise_scale_delta = 0.0
if (
"use_duration_discriminator" in hps.model.keys()
and hps.model.use_duration_discriminator == True
):
# print("Using duration discriminator for VITS2")
use_duration_discriminator = True
# comment - choihkk
# add duration discriminator type here
# I think it would be a good idea to come up with a method to input this part accurately, like a hydra
duration_discriminator_type = getattr(
hps.model, "duration_discriminator_type", "dur_disc_1"
)
print(f"Using duration_discriminator {duration_discriminator_type} for VITS2")
assert (
duration_discriminator_type in AVAILABLE_DURATION_DISCRIMINATOR_TYPES
), f"duration_discriminator_type must be one of {AVAILABLE_DURATION_DISCRIMINATOR_TYPES}"
# duration_discriminator_type = AVAILABLE_DURATION_DISCRIMINATOR_TYPES # ここ修正
if duration_discriminator_type == "dur_disc_1":
net_dur_disc = DurationDiscriminatorV1(
hps.model.hidden_channels,
hps.model.hidden_channels,
3,
0.1,
gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
).cuda(rank)
elif duration_discriminator_type == "dur_disc_2":
net_dur_disc = DurationDiscriminatorV2(
hps.model.hidden_channels,
hps.model.hidden_channels,
3,
0.1,
gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
).cuda(rank)
else:
print("NOT using any duration discriminator like VITS1")
net_dur_disc = None
use_duration_discriminator = False
net_g = SynthesizerTrn(
len(symbols)+1,
posterior_channels,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
mas_noise_scale_initial=mas_noise_scale_initial,
noise_scale_delta=noise_scale_delta,
**hps.model,
).cuda(rank)
|
numba_logger = logging.getLogger('numba')
numba_logger.setLevel(logging.WARNING)
# from tensorboardX import SummaryWriter
torch.backends.cudnn.benchmark = True
global_step = 0
def main():
"""Assume Single Node Multi GPUs Training Only"""
assert torch.cuda.is_available(), "CPU training is not allowed."
n_gpus = torch.cuda.device_count()
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "6060"
hps = utils.get_hparams()
mp.spawn(
run,
nprocs=n_gpus,
args=(
n_gpus,
hps,
),
)
def run(rank, n_gpus, hps):
net_dur_disc = None
global global_step
if rank == 0:
logger = utils.get_logger(hps.model_dir)
logger.info(hps)
utils.check_git_hash(hps.model_dir)
writer = SummaryWriter(log_dir=hps.model_dir)
writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
dist.init_process_group(
backend="nccl", init_method="env://", world_size=n_gpus, rank=rank
)
torch.manual_seed(hps.train.seed)
torch.cuda.set_device(rank)
if (
"use_mel_posterior_encoder" in hps.model.keys()
and hps.model.use_mel_posterior_encoder == True
):
print("Using mel posterior encoder for VITS2")
posterior_channels = 128 # vits2
hps.data.use_mel_posterior_encoder = True
else:
print("Using lin posterior encoder for VITS1")
posterior_channels = hps.data.filter_length // 2 + 1
hps.data.use_mel_posterior_encoder = False
train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
train_sampler = DistributedBucketSampler(
train_dataset,
hps.train.batch_size,
[32, 300, 500, 700, 900, 1100, 1300, 1500, 3000],
num_replicas=n_gpus,
rank=rank,
shuffle=True,
)
collate_fn = TextAudioSpeakerCollate()
train_loader = DataLoader(
train_dataset,
num_workers=8,
shuffle=False,
pin_memory=True,
collate_fn=collate_fn,
batch_sampler=train_sampler,
)
if rank == 0:
eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
eval_loader = DataLoader(
eval_dataset,
num_workers=8,
shuffle=False,
batch_size=hps.train.batch_size,
pin_memory=True,
drop_last=False,
collate_fn=collate_fn,
)
# some of these flags are not being used in the code and directly set in hps json file.
# they are kept here for reference and prototyping.
if (
"use_transformer_flows" in hps.model.keys()
and hps.model.use_transformer_flows == True
):
use_transformer_flows = True
transformer_flow_type = hps.model.transformer_flow_type
print(f"Using transformer flows {transformer_flow_type} for VITS2")
assert (
transformer_flow_type in AVAILABLE_FLOW_TYPES
), f"transformer_flow_type must be one of {AVAILABLE_FLOW_TYPES}"
else:
print("Using normal flows for VITS1")
use_transformer_flows = False
if (
"use_spk_conditioned_encoder" in hps.model.keys()
and hps.model.use_spk_conditioned_encoder == True
):
if hps.data.n_speakers == 0:
raise ValueError(
"n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model"
)
use_spk_conditioned_encoder = True
else:
print("Using normal encoder for VITS1")
use_spk_conditioned_encoder = False
if (
"use_noise_scaled_mas" in hps.model.keys()
and hps.model.use_noise_scaled_mas == True
):
print("Using noise scaled MAS for VITS2")
use_noise_scaled_mas = True
mas_noise_scale_initial = 0.01
noise_scale_delta = 2e-6
else:
print("Using normal MAS for VITS1")
use_noise_scaled_mas = False
mas_noise_scale_initial = 0.0
noise_scale_delta = 0.0
if (
"use_duration_discriminator" in hps.model.keys()
and hps.model.use_duration_discriminator == True
):
# print("Using duration discriminator for VITS2")
use_duration_discriminator = True
# comment - choihkk
# add duration discriminator type here
# I think it would be a good idea to come up with a method to input this part accurately, like a hydra
duration_discriminator_type = getattr(
hps.model, "duration_discriminator_type", "dur_disc_1"
)
print(f"Using duration_discriminator {duration_discriminator_type} for VITS2")
assert (
duration_discriminator_type in AVAILABLE_DURATION_DISCRIMINATOR_TYPES
), f"duration_discriminator_type must be one of {AVAILABLE_DURATION_DISCRIMINATOR_TYPES}"
# duration_discriminator_type = AVAILABLE_DURATION_DISCRIMINATOR_TYPES # ここ修正
if duration_discriminator_type == "dur_disc_1":
net_dur_disc = DurationDiscriminatorV1(
hps.model.hidden_channels,
hps.model.hidden_channels,
3,
0.1,
gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
).cuda(rank)
elif duration_discriminator_type == "dur_disc_2":
net_dur_disc = DurationDiscriminatorV2(
hps.model.hidden_channels,
hps.model.hidden_channels,
3,
0.1,
gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
).cuda(rank)
else:
print("NOT using any duration discriminator like VITS1")
net_dur_disc = None
use_duration_discriminator = False
net_g = SynthesizerTrn(
len(symbols)+1,
posterior_channels,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
mas_noise_scale_initial=mas_noise_scale_initial,
noise_scale_delta=noise_scale_delta,
**hps.model,
).cuda(rank) | net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) | 13 | 2023-12-16 05:34:02+00:00 | 16k |
Ruiyuan-Zhang/CCS | multi_part_assembly/utils/wx_transformer_utilities/multihead_attention.py | [
{
"identifier": "FairseqDropout",
"path": "multi_part_assembly/utils/wx_transformer_utilities/fairseq_dropout.py",
"snippet": "class FairseqDropout(nn.Module):\n\n def __init__(self, p, module_name=None):\n super().__init__()\n self.p = p\n self.module_name = module_name\n ... | import math
import time
import numpy as np
import torch
import torch.nn.functional as F
import multi_part_assembly.utils.wx_transformer_utilities.fairseq_utils as utils
from typing import Dict, Optional, Tuple
from torch import Tensor, nn
from torch.nn import Parameter
from .fairseq_dropout import FairseqDropout
from .attention_rim import MultiHeadAttention as MHAMemory
from .quant_noise import quant_noise
from .group_linear_layer import GroupLinearLayer
from .relational_memory_volatile import RelationalMemory
from .relational_memory_regressive import RelationalMemory as RelationalMemoryRegressive | 14,080 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#import models.fairseq_util
#from fairseq.incremental_decoding_utils import with_incremental_state
#from .relational_memory_lstm import RelationalMemory # 为什么作者没有从这两个类别中引入relmem？
#from fairseq.modules.shared_group_linear_layer import SharedGroupLinearLayer as GroupLinearLayer
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(
self,
embed_dim,
num_heads,
kdim=None,
vdim=None,
dropout=0.0,
bias=True,
add_bias_kv=False,
add_zero_attn=False,
self_attention=False,
encoder_decoder_attention=False,
q_noise=0.0,
qn_block_size=8,
nblocks=1,
top_k_ratio=None,
use_value_competition=True,
shared_memory_attention = False,
use_topk = False,
topk = 3,
num_steps = 5,
mem_slots = 4,
null_attention = False,
regressive = False
):
super().__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
self.num_heads = num_heads
self.dropout_module = FairseqDropout(
dropout, module_name=self.__class__.__name__
)
self.head_dim = embed_dim // num_heads
self.shared_memory_attention = shared_memory_attention
print('total heads', self.num_heads)
print('head dim', self.head_dim)
self.use_topk = use_topk
self.topk = topk
print('use topk?' + str(self.use_topk))
print('topk:'+str(self.topk))
assert (
self.head_dim * num_heads == self.embed_dim
), "embed_dim must be divisible by num_heads"
self.scaling = self.head_dim ** -0.5
self.self_attention = self_attention
self.encoder_decoder_attention = encoder_decoder_attention
assert not self.self_attention or self.qkv_same_dim, (
"Self-attention requires query, key and " "value to be of the same size"
)
if not self.shared_memory_attention: # 这里的共享memory_attention是什么内容呢？表示的是不在不同的layer之间共享memory吗？
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#import models.fairseq_util
#from fairseq.incremental_decoding_utils import with_incremental_state
#from .relational_memory_lstm import RelationalMemory # 为什么作者没有从这两个类别中引入relmem？
#from fairseq.modules.shared_group_linear_layer import SharedGroupLinearLayer as GroupLinearLayer
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(
self,
embed_dim,
num_heads,
kdim=None,
vdim=None,
dropout=0.0,
bias=True,
add_bias_kv=False,
add_zero_attn=False,
self_attention=False,
encoder_decoder_attention=False,
q_noise=0.0,
qn_block_size=8,
nblocks=1,
top_k_ratio=None,
use_value_competition=True,
shared_memory_attention = False,
use_topk = False,
topk = 3,
num_steps = 5,
mem_slots = 4,
null_attention = False,
regressive = False
):
super().__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
self.num_heads = num_heads
self.dropout_module = FairseqDropout(
dropout, module_name=self.__class__.__name__
)
self.head_dim = embed_dim // num_heads
self.shared_memory_attention = shared_memory_attention
print('total heads', self.num_heads)
print('head dim', self.head_dim)
self.use_topk = use_topk
self.topk = topk
print('use topk?' + str(self.use_topk))
print('topk:'+str(self.topk))
assert (
self.head_dim * num_heads == self.embed_dim
), "embed_dim must be divisible by num_heads"
self.scaling = self.head_dim ** -0.5
self.self_attention = self_attention
self.encoder_decoder_attention = encoder_decoder_attention
assert not self.self_attention or self.qkv_same_dim, (
"Self-attention requires query, key and " "value to be of the same size"
)
if not self.shared_memory_attention: # 这里的共享memory_attention是什么内容呢？表示的是不在不同的layer之间共享memory吗？ | self.k_proj = quant_noise(GroupLinearLayer(self.kdim//nblocks, embed_dim//nblocks, nblocks, bias=bias), q_noise, qn_block_size) | 3 | 2023-12-15 13:13:01+00:00 | 16k |
camenduru/FreeInit-hf | app.py | [
{
"identifier": "UNet3DConditionModel",
"path": "animatediff/models/unet.py",
"snippet": "class UNet3DConditionModel(ModelMixin, ConfigMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,\n in... | import os
import torch
import random
import gradio as gr
from glob import glob
from omegaconf import OmegaConf
from safetensors import safe_open
from diffusers import AutoencoderKL
from diffusers import EulerDiscreteScheduler, DDIMScheduler
from diffusers.utils.import_utils import is_xformers_available
from transformers import CLIPTextModel, CLIPTokenizer
from animatediff.models.unet import UNet3DConditionModel
from animatediff.pipelines.pipeline_animation import AnimationFreeInitPipeline
from animatediff.utils.util import save_videos_grid
from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint
from diffusers.training_utils import set_seed
from animatediff.utils.freeinit_utils import get_freq_filter
from collections import namedtuple | 13,894 | ["use_fp16"]
],
# 1-ToonYou
[
"toonyou_beta3.safetensors",
"mm_sd_v14.ckpt",
"(best quality, masterpiece), 1girl, looking at viewer, blurry background, upper body, contemporary, dress",
"(worst quality, low quality)",
512, 512, "478028150728261",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# 2-Lyriel
[
"lyriel_v16.safetensors",
"mm_sd_v14.ckpt",
"hypercars cyberpunk moving, muted colors, swirling color smokes, legend, cityscape, space",
"3d, cartoon, anime, sketches, worst quality, low quality, nsfw, logo",
512, 512, "1566149281915957",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# 3-RCNZ
[
"rcnzCartoon3d_v10.safetensors",
"mm_sd_v14.ckpt",
"A cute raccoon playing guitar in a boat on the ocean",
"worst quality, low quality, nsfw, logo",
512, 512, "1566149281915957",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# 4-MajicMix
[
"majicmixRealistic_v5Preview.safetensors",
"mm_sd_v14.ckpt",
"1girl, reading book",
"(ng_deepnegative_v1_75t:1.2), (badhandv4:1), (worst quality:2), (low quality:2), (normal quality:2), lowres, bad anatomy, bad hands, watermark, moles",
512, 512, "2005563494988190",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# # 5-RealisticVision
# [
# "realisticVisionV51_v20Novae.safetensors",
# "mm_sd_v14.ckpt",
# "A panda standing on a surfboard in the ocean in sunset.",
# "worst quality, low quality, nsfw, logo",
# 512, 512, "2005563494988190",
# "butterworth", 0.25, 0.25, 3,
# ["use_fp16"]
# ]
]
# clean unrelated ckpts
# ckpts = [
# "realisticVisionV40_v20Novae.safetensors",
# "majicmixRealistic_v5Preview.safetensors",
# "rcnzCartoon3d_v10.safetensors",
# "lyriel_v16.safetensors",
# "toonyou_beta3.safetensors"
# ]
# for path in glob(os.path.join("models", "DreamBooth_LoRA", "*.safetensors")):
# for ckpt in ckpts:
# if path.endswith(ckpt): break
# else:
# print(f"### Cleaning {path} ...")
# os.system(f"rm -rf {path}")
# os.system(f"rm -rf {os.path.join('models', 'DreamBooth_LoRA', '*.safetensors')}")
# os.system(f"bash download_bashscripts/1-ToonYou.sh")
# os.system(f"bash download_bashscripts/2-Lyriel.sh")
# os.system(f"bash download_bashscripts/3-RcnzCartoon.sh")
# os.system(f"bash download_bashscripts/4-MajicMix.sh")
# os.system(f"bash download_bashscripts/5-RealisticVision.sh")
# # clean Gradio cache
# print(f"### Cleaning cached examples ...")
# os.system(f"rm -rf gradio_cached_examples/")
class AnimateController:
def __init__(self):
# config dirs
self.basedir = os.getcwd()
self.stable_diffusion_dir = os.path.join(self.basedir, "models", "StableDiffusion")
self.motion_module_dir = os.path.join(self.basedir, "models", "Motion_Module")
self.personalized_model_dir = os.path.join(self.basedir, "models", "DreamBooth_LoRA")
self.savedir = os.path.join(self.basedir, "samples")
os.makedirs(self.savedir, exist_ok=True)
self.base_model_list = []
self.motion_module_list = []
self.filter_type_list = [
"butterworth",
"gaussian",
"box",
"ideal"
]
self.selected_base_model = None
self.selected_motion_module = None
self.selected_filter_type = None
self.set_width = None
self.set_height = None
self.set_d_s = None
self.set_d_t = None
self.refresh_motion_module()
self.refresh_personalized_model()
# config models
self.inference_config = OmegaConf.load(inference_config_path)
self.tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
self.text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder").cuda()
self.vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae").cuda()
|
pretrained_model_path = "models/StableDiffusion/stable-diffusion-v1-5"
inference_config_path = "configs/inference/inference-v1.yaml"
css = """
.toolbutton {
margin-buttom: 0em 0em 0em 0em;
max-width: 2.5em;
min-width: 2.5em !important;
height: 2.5em;
}
"""
examples = [
# 0-RealisticVision
[
"realisticVisionV51_v20Novae.safetensors",
"mm_sd_v14.ckpt",
"A panda standing on a surfboard in the ocean under moonlight.",
"worst quality, low quality, nsfw, logo",
512, 512, "2005563494988190",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# 1-ToonYou
[
"toonyou_beta3.safetensors",
"mm_sd_v14.ckpt",
"(best quality, masterpiece), 1girl, looking at viewer, blurry background, upper body, contemporary, dress",
"(worst quality, low quality)",
512, 512, "478028150728261",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# 2-Lyriel
[
"lyriel_v16.safetensors",
"mm_sd_v14.ckpt",
"hypercars cyberpunk moving, muted colors, swirling color smokes, legend, cityscape, space",
"3d, cartoon, anime, sketches, worst quality, low quality, nsfw, logo",
512, 512, "1566149281915957",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# 3-RCNZ
[
"rcnzCartoon3d_v10.safetensors",
"mm_sd_v14.ckpt",
"A cute raccoon playing guitar in a boat on the ocean",
"worst quality, low quality, nsfw, logo",
512, 512, "1566149281915957",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# 4-MajicMix
[
"majicmixRealistic_v5Preview.safetensors",
"mm_sd_v14.ckpt",
"1girl, reading book",
"(ng_deepnegative_v1_75t:1.2), (badhandv4:1), (worst quality:2), (low quality:2), (normal quality:2), lowres, bad anatomy, bad hands, watermark, moles",
512, 512, "2005563494988190",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# # 5-RealisticVision
# [
# "realisticVisionV51_v20Novae.safetensors",
# "mm_sd_v14.ckpt",
# "A panda standing on a surfboard in the ocean in sunset.",
# "worst quality, low quality, nsfw, logo",
# 512, 512, "2005563494988190",
# "butterworth", 0.25, 0.25, 3,
# ["use_fp16"]
# ]
]
# clean unrelated ckpts
# ckpts = [
# "realisticVisionV40_v20Novae.safetensors",
# "majicmixRealistic_v5Preview.safetensors",
# "rcnzCartoon3d_v10.safetensors",
# "lyriel_v16.safetensors",
# "toonyou_beta3.safetensors"
# ]
# for path in glob(os.path.join("models", "DreamBooth_LoRA", "*.safetensors")):
# for ckpt in ckpts:
# if path.endswith(ckpt): break
# else:
# print(f"### Cleaning {path} ...")
# os.system(f"rm -rf {path}")
# os.system(f"rm -rf {os.path.join('models', 'DreamBooth_LoRA', '*.safetensors')}")
# os.system(f"bash download_bashscripts/1-ToonYou.sh")
# os.system(f"bash download_bashscripts/2-Lyriel.sh")
# os.system(f"bash download_bashscripts/3-RcnzCartoon.sh")
# os.system(f"bash download_bashscripts/4-MajicMix.sh")
# os.system(f"bash download_bashscripts/5-RealisticVision.sh")
# # clean Gradio cache
# print(f"### Cleaning cached examples ...")
# os.system(f"rm -rf gradio_cached_examples/")
class AnimateController:
def __init__(self):
# config dirs
self.basedir = os.getcwd()
self.stable_diffusion_dir = os.path.join(self.basedir, "models", "StableDiffusion")
self.motion_module_dir = os.path.join(self.basedir, "models", "Motion_Module")
self.personalized_model_dir = os.path.join(self.basedir, "models", "DreamBooth_LoRA")
self.savedir = os.path.join(self.basedir, "samples")
os.makedirs(self.savedir, exist_ok=True)
self.base_model_list = []
self.motion_module_list = []
self.filter_type_list = [
"butterworth",
"gaussian",
"box",
"ideal"
]
self.selected_base_model = None
self.selected_motion_module = None
self.selected_filter_type = None
self.set_width = None
self.set_height = None
self.set_d_s = None
self.set_d_t = None
self.refresh_motion_module()
self.refresh_personalized_model()
# config models
self.inference_config = OmegaConf.load(inference_config_path)
self.tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
self.text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder").cuda()
self.vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae").cuda() | self.unet = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda() | 0 | 2023-12-19 21:06:32+00:00 | 16k |
exislow/tidal-dl-ng | tidal_dl_ng/gui.py | [
{
"identifier": "get_format_template",
"path": "tidal_dl_ng/helper/path.py",
"snippet": "def get_format_template(\n media: Track | Album | Playlist | UserPlaylist | Video | Mix | MediaType, settings\n) -> str | bool:\n result = False\n\n if isinstance(media, Track) or media == MediaType.TRACK:\... | import math
import sys
import qdarktheme
import coloredlogs.converter
from collections.abc import Callable
from tidal_dl_ng.helper.path import get_format_template
from PySide6 import QtCore, QtGui, QtWidgets
from rich.progress import Progress
from tidalapi import Album, Mix, Playlist, Quality, Track, UserPlaylist, Video
from tidalapi.session import SearchTypes
from tidal_dl_ng.config import Settings, Tidal
from tidal_dl_ng.constants import QualityVideo, TidalLists
from tidal_dl_ng.download import Download
from tidal_dl_ng.logger import XStream, logger_gui
from tidal_dl_ng.model.gui_data import ProgressBars, ResultSearch
from tidal_dl_ng.ui.main import Ui_MainWindow
from tidal_dl_ng.ui.spinner import QtWaitingSpinner
from tidal_dl_ng.worker import Worker | 14,197 | if isinstance(l_media, list):
result = result + self.search_result_to_model(l_media)
return result
def search_result_to_model(self, items: [*SearchTypes]) -> [ResultSearch]:
result = []
for idx, item in enumerate(items):
if isinstance(item, Track):
result_item: ResultSearch = ResultSearch(
position=idx,
artist=", ".join(artist.name for artist in item.artists),
title=item.name,
album=item.album.name,
duration_sec=item.duration,
obj=item,
)
result.append(result_item)
elif isinstance(item, Video):
result_item: ResultSearch = ResultSearch(
position=idx,
artist=", ".join(artist.name for artist in item.artists),
title=item.name,
album=item.album.name if item.album else "",
duration_sec=item.duration,
obj=item,
)
result.append(result_item)
elif isinstance(item, Playlist):
result_item: ResultSearch = ResultSearch(
position=idx,
artist=", ".join(artist.name for artist in item.promoted_artists) if item.promoted_artists else "",
title=item.name,
album="",
duration_sec=item.duration,
obj=item,
)
result.append(result_item)
elif isinstance(item, Album):
result_item: ResultSearch = ResultSearch(
position=idx,
artist=", ".join(artist.name for artist in item.artists),
title="",
album=item.name,
duration_sec=item.duration,
obj=item,
)
result.append(result_item)
return result
def _init_signals(self):
self.b_download.clicked.connect(lambda: self.thread_it(self.on_download_results))
self.l_search.returnPressed.connect(
lambda: self.search_populate_results(self.l_search.text(), self.cb_search_type.currentData())
)
self.b_search.clicked.connect(
lambda: self.search_populate_results(self.l_search.text(), self.cb_search_type.currentData())
)
self.cb_quality_audio.currentIndexChanged.connect(self.quality_set_audio)
self.cb_quality_video.currentIndexChanged.connect(self.quality_set_video)
self.tr_lists_user.itemClicked.connect(self.on_list_items_show)
self.spinner_start[QtWidgets.QWidget].connect(self.on_spinner_start)
self.spinner_stop.connect(self.on_spinner_stop)
self.s_item_advance.connect(self.progress_item)
self.s_item_name.connect(self.progress_item_name)
self.s_list_advance.connect(self.progress_list)
self.s_pb_reset.connect(self.progress_reset)
self.s_populate_tree_lists.connect(self.on_populate_tree_lists)
def progress_list(self, value: float):
self.pb_list.setValue(int(math.ceil(value)))
def progress_item(self, value: float):
self.pb_item.setValue(int(math.ceil(value)))
def progress_item_name(self, value: str):
self.pb_item.setFormat(f"%p% {value}")
def progress_list_name(self, value: str):
self.pb_list.setFormat(f"%p% {value}")
def quality_set_audio(self, index):
self.settings.data.quality_audio = Quality(self.cb_quality_audio.itemData(index).value)
self.settings.save()
if self.tidal:
self.tidal.settings_apply()
def quality_set_video(self, index):
self.settings.data.quality_video = QualityVideo(self.cb_quality_video.itemData(index).value)
self.settings.save()
if self.tidal:
self.tidal.settings_apply()
def on_list_items_show(self, item: QtWidgets.QTreeWidgetItem):
media_list: Album | Playlist = item.data(3, QtCore.Qt.ItemDataRole.UserRole)
# Only if clicked item is not a top level item.
if media_list:
self.list_items_show(media_list)
def list_items_show(self, media_list: Album | Playlist | None = None, point: QtCore.QPoint | None = None):
if point:
item = self.tr_lists_user.itemAt(point)
media_list = item.data(3, QtCore.Qt.ItemDataRole.UserRole)
media_items = media_list.items()
result = self.search_result_to_model(media_items)
self.populate_tree_results(result)
def thread_it(self, fn: Callable, *args, **kwargs):
# Any other args, kwargs are passed to the run function
|
try:
except ImportError as e:
print(e)
print("Qt dependencies missing. Cannot start GUI. Please execute: 'pip install pyside6 pyqtdarktheme'")
sys.exit(1)
# TODO: Make more use of Exceptions
# TODO: Add File -> Version
class MainWindow(QtWidgets.QMainWindow, Ui_MainWindow):
settings: Settings = None
tidal: Tidal = None
dl: Download = None
threadpool: QtCore.QThreadPool = None
spinner: QtWaitingSpinner = None
spinner_start: QtCore.Signal = QtCore.Signal(QtWidgets.QWidget)
spinner_stop: QtCore.Signal = QtCore.Signal()
pb_item: QtWidgets.QProgressBar = None
s_item_advance: QtCore.Signal = QtCore.Signal(float)
s_item_name: QtCore.Signal = QtCore.Signal(str)
pb_list: QtWidgets.QProgressBar = None
s_list_advance: QtCore.Signal = QtCore.Signal(float)
s_pb_reset: QtCore.Signal = QtCore.Signal()
s_populate_tree_lists: QtCore.Signal = QtCore.Signal(list)
def __init__(self, tidal: Tidal | None = None):
super().__init__()
self.setupUi(self)
# self.setGeometry(50, 50, 500, 300)
self.setWindowTitle("TIDAL Downloader Next Gen!")
# TODO: Fix icons (make them visible).
# my_pixmap = QtGui.QPixmap("tidal_dl_ng/ui/icon.png")
my_icon = QtGui.QIcon("tidal_dl_ng/ui/icon.png")
self.setWindowIcon(my_icon)
tray = QtWidgets.QSystemTrayIcon()
tray.setIcon(my_icon)
tray.setVisible(True)
# Logging redirect.
XStream.stdout().messageWritten.connect(self._log_output)
# XStream.stderr().messageWritten.connect(self._log_output)
self.settings = Settings()
self.threadpool = QtCore.QThreadPool()
# TODO: Show GUI, create a progress bar showing the TIDAL querying progress.
self._init_tree_results(self.tr_results)
self._init_tree_lists(self.tr_lists_user)
self._init_progressbar()
self._populate_quality(self.cb_quality_audio, Quality)
self._populate_quality(self.cb_quality_video, QualityVideo)
self._populate_search_types(self.cb_search_type, SearchTypes)
self.apply_settings(self.settings)
self._init_signals()
self.init_tidal(tidal)
logger_gui.debug("Multithreading with maximum %d threads" % self.threadpool.maxThreadCount())
logger_gui.debug("All setup.")
def init_tidal(self, tidal: Tidal = None):
result: bool = False
if tidal:
self.tidal = tidal
result = True
else:
self.tidal = Tidal(self.settings)
while True:
result = self.tidal.login(logger_gui.info)
if result:
break
if result:
self.dl = Download(self.tidal.session, self.tidal.settings.data.skip_existing)
self.thread_it(self.tidal_user_lists)
def _init_progressbar(self):
self.pb_list = QtWidgets.QProgressBar()
self.pb_item = QtWidgets.QProgressBar()
pbs = [self.pb_list, self.pb_item]
for pb in pbs:
pb.setRange(0, 100)
# self.pb_progress.setVisible()
self.statusbar.addPermanentWidget(pb)
def progress_reset(self):
self.pb_list.setValue(0)
self.pb_item.setValue(0)
def _log_output(self, text):
display_msg = coloredlogs.converter.convert(text)
cursor: QtGui.QTextCursor = self.te_debug.textCursor()
cursor.movePosition(QtGui.QTextCursor.End)
cursor.insertHtml(display_msg)
self.te_debug.setTextCursor(cursor)
self.te_debug.ensureCursorVisible()
def _populate_quality(self, ui_target: QtWidgets.QComboBox, options: type[Quality | QualityVideo]):
for item in options:
ui_target.addItem(item.name, item)
def _populate_search_types(self, ui_target: QtWidgets.QComboBox, options: SearchTypes):
for item in options:
if item and item.__name__ != "Artist":
ui_target.addItem(item.__name__, item)
self.cb_search_type.setCurrentIndex(1)
def _init_tree_results(self, tree: QtWidgets.QTableWidget):
tree.setColumnHidden(5, True)
tree.sortByColumn(0, QtCore.Qt.SortOrder.AscendingOrder)
# TODO: Refactor to own TIDAL file or so.
def tidal_user_lists(self):
# Start loading spinner
self.spinner_start.emit(self.tr_lists_user)
user_playlists: [Playlist | UserPlaylist] = self.tidal.session.user.playlist_and_favorite_playlists()
user_mixes: [Mix] = self.tidal.session.mixes().categories[0].items
user_all: [Playlist | UserPlaylist | Mix] = user_playlists + user_mixes
self.s_populate_tree_lists.emit(user_all)
def on_populate_tree_lists(self, user_lists: [Playlist | UserPlaylist | Mix]):
self.tr_results.clear()
twi_playlists: QtWidgets.QTreeWidgetItem = self.tr_lists_user.findItems(
TidalLists.PLAYLISTS.value, QtCore.Qt.MatchExactly, 0
)[0]
twi_mixes: QtWidgets.QTreeWidgetItem = self.tr_lists_user.findItems(
TidalLists.FAVORITES.value, QtCore.Qt.MatchExactly, 0
)[0]
twi_favorites: QtWidgets.QTreeWidgetItem = self.tr_lists_user.findItems(
TidalLists.MIXES.value, QtCore.Qt.MatchExactly, 0
)[0]
for item in user_lists:
if isinstance(item, UserPlaylist):
twi_child = QtWidgets.QTreeWidgetItem(twi_playlists)
name: str = item.name
info: str = f"({item.num_tracks + item.num_videos} Tracks)"
elif isinstance(item, Playlist):
twi_child = QtWidgets.QTreeWidgetItem(twi_mixes)
name: str = item.name
info: str = f"({item.num_tracks + item.num_videos} Tracks) {item.description}"
elif isinstance(item, Mix):
twi_child = QtWidgets.QTreeWidgetItem(twi_favorites)
name: str = item.title
info: str = item.sub_title
twi_child.setText(0, name)
twi_child.setText(1, info)
twi_child.setData(3, QtCore.Qt.ItemDataRole.UserRole, item)
# Stop load spinner
self.spinner_stop.emit()
def _init_tree_lists(self, tree: QtWidgets.QTreeWidget):
# Adjust Tree.
tree.setColumnWidth(0, 200)
tree.setColumnWidth(1, 300)
tree.setColumnHidden(2, True)
tree.expandAll()
# Connect the contextmenu
tree.setContextMenuPolicy(QtCore.Qt.CustomContextMenu)
tree.customContextMenuRequested.connect(self.menu_context_tree_lists)
def apply_settings(self, settings: Settings):
l_cb = [
{"element": self.cb_quality_audio, "setting": settings.data.quality_audio.name, "default_id": 1},
{"element": self.cb_quality_video, "setting": settings.data.quality_video.name, "default_id": 0},
]
for item in l_cb:
idx = item["element"].findText(item["setting"])
if idx > -1:
item["element"].setCurrentIndex(idx)
else:
item["element"].setCurrentIndex(item["default_id"])
def on_spinner_start(self, parent: QtWidgets.QWidget):
self.spinner = QtWaitingSpinner(parent, True, True)
self.spinner.setColor(QtGui.QColor(255, 255, 255))
self.spinner.start()
def on_spinner_stop(self):
self.spinner.stop()
self.spinner = None
def menu_context_tree_lists(self, point):
# Infos about the node selected.
index = self.tr_lists_user.indexAt(point)
# Do not open menu if something went wrong or a parent node is clicked.
if not index.isValid() or not index.parent().data():
return
# We build the menu.
menu = QtWidgets.QMenu()
menu.addAction("Download Playlist", lambda: self.thread_download_list_media(point))
menu.exec(self.tr_lists_user.mapToGlobal(point))
def thread_download_list_media(self, point):
self.thread_it(self.on_download_list_media, point)
self.thread_it(self.list_items_show, point=point)
def on_download_list_media(self, point: QtCore.QPoint):
item = self.tr_lists_user.itemAt(point)
media = item.data(3, QtCore.Qt.ItemDataRole.UserRole)
# TODO: Implement disable download button etc.
self.download(media, self.dl)
def search_populate_results(self, query: str, type_media: SearchTypes):
self.tr_results.clear()
results: [ResultSearch] = self.search(query, [type_media])
self.populate_tree_results(results)
def populate_tree_results(self, results: [ResultSearch]):
self.tr_results.clear()
for item in results:
# Format seconds to mm:ss.
m, s = divmod(item.duration_sec, 60)
duration: str = f"{m:02d}:{s:02d}"
# Since sorting happens only by string, we need to pad the index and add 1 (to avoid start at 0)
index: str = f"{item.position + 1:03}"
child = QtWidgets.QTreeWidgetItem()
child.setText(0, index)
child.setText(1, item.artist)
child.setText(2, item.title)
child.setText(3, item.album)
child.setText(4, duration)
child.setData(5, QtCore.Qt.ItemDataRole.UserRole, item.obj)
self.tr_results.addTopLevelItem(child)
def search(self, query: str, types_media: SearchTypes) -> [ResultSearch]:
result_search: [dict[str, SearchTypes]] = self.tidal.session.search(query, models=types_media, limit=999)
result: [ResultSearch] = []
for _media_type, l_media in result_search.items():
if isinstance(l_media, list):
result = result + self.search_result_to_model(l_media)
return result
def search_result_to_model(self, items: [*SearchTypes]) -> [ResultSearch]:
result = []
for idx, item in enumerate(items):
if isinstance(item, Track):
result_item: ResultSearch = ResultSearch(
position=idx,
artist=", ".join(artist.name for artist in item.artists),
title=item.name,
album=item.album.name,
duration_sec=item.duration,
obj=item,
)
result.append(result_item)
elif isinstance(item, Video):
result_item: ResultSearch = ResultSearch(
position=idx,
artist=", ".join(artist.name for artist in item.artists),
title=item.name,
album=item.album.name if item.album else "",
duration_sec=item.duration,
obj=item,
)
result.append(result_item)
elif isinstance(item, Playlist):
result_item: ResultSearch = ResultSearch(
position=idx,
artist=", ".join(artist.name for artist in item.promoted_artists) if item.promoted_artists else "",
title=item.name,
album="",
duration_sec=item.duration,
obj=item,
)
result.append(result_item)
elif isinstance(item, Album):
result_item: ResultSearch = ResultSearch(
position=idx,
artist=", ".join(artist.name for artist in item.artists),
title="",
album=item.name,
duration_sec=item.duration,
obj=item,
)
result.append(result_item)
return result
def _init_signals(self):
self.b_download.clicked.connect(lambda: self.thread_it(self.on_download_results))
self.l_search.returnPressed.connect(
lambda: self.search_populate_results(self.l_search.text(), self.cb_search_type.currentData())
)
self.b_search.clicked.connect(
lambda: self.search_populate_results(self.l_search.text(), self.cb_search_type.currentData())
)
self.cb_quality_audio.currentIndexChanged.connect(self.quality_set_audio)
self.cb_quality_video.currentIndexChanged.connect(self.quality_set_video)
self.tr_lists_user.itemClicked.connect(self.on_list_items_show)
self.spinner_start[QtWidgets.QWidget].connect(self.on_spinner_start)
self.spinner_stop.connect(self.on_spinner_stop)
self.s_item_advance.connect(self.progress_item)
self.s_item_name.connect(self.progress_item_name)
self.s_list_advance.connect(self.progress_list)
self.s_pb_reset.connect(self.progress_reset)
self.s_populate_tree_lists.connect(self.on_populate_tree_lists)
def progress_list(self, value: float):
self.pb_list.setValue(int(math.ceil(value)))
def progress_item(self, value: float):
self.pb_item.setValue(int(math.ceil(value)))
def progress_item_name(self, value: str):
self.pb_item.setFormat(f"%p% {value}")
def progress_list_name(self, value: str):
self.pb_list.setFormat(f"%p% {value}")
def quality_set_audio(self, index):
self.settings.data.quality_audio = Quality(self.cb_quality_audio.itemData(index).value)
self.settings.save()
if self.tidal:
self.tidal.settings_apply()
def quality_set_video(self, index):
self.settings.data.quality_video = QualityVideo(self.cb_quality_video.itemData(index).value)
self.settings.save()
if self.tidal:
self.tidal.settings_apply()
def on_list_items_show(self, item: QtWidgets.QTreeWidgetItem):
media_list: Album | Playlist = item.data(3, QtCore.Qt.ItemDataRole.UserRole)
# Only if clicked item is not a top level item.
if media_list:
self.list_items_show(media_list)
def list_items_show(self, media_list: Album | Playlist | None = None, point: QtCore.QPoint | None = None):
if point:
item = self.tr_lists_user.itemAt(point)
media_list = item.data(3, QtCore.Qt.ItemDataRole.UserRole)
media_items = media_list.items()
result = self.search_result_to_model(media_items)
self.populate_tree_results(result)
def thread_it(self, fn: Callable, *args, **kwargs):
# Any other args, kwargs are passed to the run function | worker = Worker(fn, *args, **kwargs) | 11 | 2023-12-19 23:05:47+00:00 | 16k |
zyrant/SPGroup3D | tests/test_data/test_datasets/test_scannet_dataset.py | [
{
"identifier": "ScanNetDataset",
"path": "mmdet3d/datasets/scannet_dataset.py",
"snippet": "class ScanNetDataset(Custom3DDataset):\n r\"\"\"ScanNet Dataset for Detection Task.\n\n This class serves as the API for experiments on the ScanNet Dataset.\n\n Please refer to the `github repo <https:/... | import copy
import numpy as np
import pytest
import torch
import tempfile
import tempfile
import mmcv
import tempfile
import tempfile
import mmcv
import mmcv
from mmdet3d.datasets import (ScanNetDataset, ScanNetInstanceSegDataset,
ScanNetSegDataset, ScanNetInstanceSegV2Dataset)
from mmdet3d.core.bbox.structures import DepthInstance3DBoxes
from os import path as osp
from mmdet3d.core.bbox import DepthInstance3DBoxes
from os import path as osp
from os import path as osp | 11,054 | 3, 19, 12, 8, 0, 11, 0, 0, 1, 2, 13, 17, 1, 1, 1, 6, 2, 13, 19, 4,
17, 0, 14, 1, 7, 2, 1, 7, 2, 0, 5, 17, 5, 0, 0, 3, 6, 5, 11, 1, 13,
13, 2, 3, 1, 0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
]).long())
results = [result]
scannet_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
# test show with pipeline
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'otherfurniture')
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24,
28, 33, 34, 36, 39),
max_cat_id=40),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
]
scannet_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
def test_seg_format_results():
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetSegDataset(
data_root=root_path, ann_file=ann_file, test_mode=True)
results = []
pred_sem_mask = dict(
semantic_mask=torch.tensor([
13, 5, 1, 2, 6, 2, 13, 1, 14, 2, 0, 0, 5, 5, 3, 0, 1, 14, 0, 0, 0,
18, 6, 15, 13, 0, 2, 4, 0, 3, 16, 6, 13, 5, 13, 0, 0, 0, 0, 1, 7,
3, 19, 12, 8, 0, 11, 0, 0, 1, 2, 13, 17, 1, 1, 1, 6, 2, 13, 19, 4,
17, 0, 14, 1, 7, 2, 1, 7, 2, 0, 5, 17, 5, 0, 0, 3, 6, 5, 11, 1, 13,
13, 2, 3, 1, 0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
]).long())
results.append(pred_sem_mask)
result_files, tmp_dir = scannet_dataset.format_results(results)
expected_label = np.array([
16, 6, 2, 3, 7, 3, 16, 2, 24, 3, 1, 1, 6, 6, 4, 1, 2, 24, 1, 1, 1, 36,
7, 28, 16, 1, 3, 5, 1, 4, 33, 7, 16, 6, 16, 1, 1, 1, 1, 2, 8, 4, 39,
14, 9, 1, 12, 1, 1, 2, 3, 16, 34, 2, 2, 2, 7, 3, 16, 39, 5, 34, 1, 24,
2, 8, 3, 2, 8, 3, 1, 6, 34, 6, 1, 1, 4, 7, 6, 12, 2, 16, 16, 3, 4, 2,
1, 16, 39, 2, 24, 6, 4, 2, 16, 2, 3, 4, 3, 2
])
expected_txt_path = osp.join(tmp_dir.name, 'results', 'scene0000_00.txt')
assert np.all(result_files[0]['seg_mask'] == expected_label)
mmcv.check_file_exist(expected_txt_path)
def test_instance_seg_getitem():
np.random.seed(0)
root_path = './tests/data/scannet/'
ann_file = './tests/data/scannet/scannet_infos.pkl'
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=True,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33,
34, 36, 39),
max_cat_id=40),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
keys=['points', 'pts_semantic_mask', 'pts_instance_mask'])
]
| # Copyright (c) OpenMMLab. All rights reserved.
def test_getitem():
np.random.seed(0)
root_path = './tests/data/scannet/'
ann_file = './tests/data/scannet/scannet_infos.pkl'
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')
pipelines = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
with_mask_3d=True,
with_seg_3d=True),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='PointSegClassMapping',
valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33,
34, 36, 39)),
dict(type='PointSample', num_points=5),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=1.0,
flip_ratio_bev_vertical=1.0),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.087266, 0.087266],
scale_ratio_range=[1.0, 1.0],
shift_height=True),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
keys=[
'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
'pts_instance_mask'
],
meta_keys=['file_name', 'sample_idx', 'pcd_rotation']),
]
scannet_dataset = ScanNetDataset(root_path, ann_file, pipelines)
data = scannet_dataset[0]
points = data['points']._data
gt_bboxes_3d = data['gt_bboxes_3d']._data
gt_labels = data['gt_labels_3d']._data
pts_semantic_mask = data['pts_semantic_mask']._data
pts_instance_mask = data['pts_instance_mask']._data
file_name = data['img_metas']._data['file_name']
pcd_rotation = data['img_metas']._data['pcd_rotation']
sample_idx = data['img_metas']._data['sample_idx']
expected_rotation = np.array([[0.99654, 0.08311407, 0.],
[-0.08311407, 0.99654, 0.], [0., 0., 1.]])
assert file_name == './tests/data/scannet/points/scene0000_00.bin'
assert np.allclose(pcd_rotation, expected_rotation, 1e-3)
assert sample_idx == 'scene0000_00'
expected_points = torch.tensor(
[[1.8339e+00, 2.1093e+00, 2.2900e+00, 2.3895e+00],
[3.6079e+00, 1.4592e-01, 2.0687e+00, 2.1682e+00],
[4.1886e+00, 5.0614e+00, -1.0841e-01, -8.8736e-03],
[6.8790e+00, 1.5086e+00, -9.3154e-02, 6.3816e-03],
[4.8253e+00, 2.6668e-01, 1.4917e+00, 1.5912e+00]])
expected_gt_bboxes_3d = torch.tensor(
[[-1.1835, -3.6317, 1.5704, 1.7577, 0.3761, 0.5724, 0.0000],
[-3.1832, 3.2269, 1.1911, 0.6727, 0.2251, 0.6715, 0.0000],
[-0.9598, -2.2864, 0.0093, 0.7506, 2.5709, 1.2145, 0.0000],
[-2.6988, -2.7354, 0.8288, 0.7680, 1.8877, 0.2870, 0.0000],
[3.2989, 0.2885, -0.0090, 0.7600, 3.8814, 2.1603, 0.0000]])
expected_gt_labels = np.array([
6, 6, 4, 9, 11, 11, 10, 0, 15, 17, 17, 17, 3, 12, 4, 4, 14, 1, 0, 0, 0,
0, 0, 0, 5, 5, 5
])
expected_pts_semantic_mask = np.array([0, 18, 18, 18, 18])
expected_pts_instance_mask = np.array([44, 22, 10, 10, 57])
original_classes = scannet_dataset.CLASSES
assert scannet_dataset.CLASSES == class_names
assert torch.allclose(points, expected_points, 1e-2)
assert gt_bboxes_3d.tensor[:5].shape == (5, 7)
assert torch.allclose(gt_bboxes_3d.tensor[:5], expected_gt_bboxes_3d, 1e-2)
assert np.all(gt_labels.numpy() == expected_gt_labels)
assert np.all(pts_semantic_mask.numpy() == expected_pts_semantic_mask)
assert np.all(pts_instance_mask.numpy() == expected_pts_instance_mask)
assert original_classes == class_names
scannet_dataset = ScanNetDataset(
root_path, ann_file, pipeline=None, classes=['cabinet', 'bed'])
assert scannet_dataset.CLASSES != original_classes
assert scannet_dataset.CLASSES == ['cabinet', 'bed']
scannet_dataset = ScanNetDataset(
root_path, ann_file, pipeline=None, classes=('cabinet', 'bed'))
assert scannet_dataset.CLASSES != original_classes
assert scannet_dataset.CLASSES == ('cabinet', 'bed')
# Test load classes from file
with tempfile.TemporaryDirectory() as tmpdir:
path = tmpdir + 'classes.txt'
with open(path, 'w') as f:
f.write('cabinet\nbed\n')
scannet_dataset = ScanNetDataset(
root_path, ann_file, pipeline=None, classes=path)
assert scannet_dataset.CLASSES != original_classes
assert scannet_dataset.CLASSES == ['cabinet', 'bed']
def test_evaluate():
if not torch.cuda.is_available():
pytest.skip()
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetDataset(root_path, ann_file)
results = []
pred_boxes = dict()
pred_boxes['boxes_3d'] = DepthInstance3DBoxes(
torch.tensor([[
1.4813e+00, 3.5207e+00, 1.5704e+00, 1.7445e+00, 2.3196e-01,
5.7235e-01, 0.0000e+00
],
[
2.9040e+00, -3.4803e+00, 1.1911e+00, 6.6078e-01,
1.7072e-01, 6.7154e-01, 0.0000e+00
],
[
1.1466e+00, 2.1987e+00, 9.2576e-03, 5.4184e-01,
2.5346e+00, 1.2145e+00, 0.0000e+00
],
[
2.9168e+00, 2.5016e+00, 8.2875e-01, 6.1697e-01,
1.8428e+00, 2.8697e-01, 0.0000e+00
],
[
-3.3114e+00, -1.3351e-02, -8.9524e-03, 4.4082e-01,
3.8582e+00, 2.1603e+00, 0.0000e+00
],
[
-2.0135e+00, -3.4857e+00, 9.3848e-01, 1.9911e+00,
2.1603e-01, 1.2767e+00, 0.0000e+00
],
[
-2.1945e+00, -3.1402e+00, -3.8165e-02, 1.4801e+00,
6.8676e-01, 1.0586e+00, 0.0000e+00
],
[
-2.7553e+00, 2.4055e+00, -2.9972e-02, 1.4764e+00,
1.4927e+00, 2.3380e+00, 0.0000e+00
]]))
pred_boxes['labels_3d'] = torch.tensor([6, 6, 4, 9, 11, 11])
pred_boxes['scores_3d'] = torch.tensor([0.5, 1.0, 1.0, 1.0, 1.0, 0.5])
results.append(pred_boxes)
metric = [0.25, 0.5]
ret_dict = scannet_dataset.evaluate(results, metric)
assert abs(ret_dict['table_AP_0.25'] - 0.3333) < 0.01
assert abs(ret_dict['window_AP_0.25'] - 1.0) < 0.01
assert abs(ret_dict['counter_AP_0.25'] - 1.0) < 0.01
assert abs(ret_dict['curtain_AP_0.25'] - 1.0) < 0.01
# test evaluate with pipeline
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2]),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
ret_dict = scannet_dataset.evaluate(
results, metric, pipeline=eval_pipeline)
assert abs(ret_dict['table_AP_0.25'] - 0.3333) < 0.01
assert abs(ret_dict['window_AP_0.25'] - 1.0) < 0.01
assert abs(ret_dict['counter_AP_0.25'] - 1.0) < 0.01
assert abs(ret_dict['curtain_AP_0.25'] - 1.0) < 0.01
def test_show():
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetDataset(root_path, ann_file)
boxes_3d = DepthInstance3DBoxes(
torch.tensor([[
-2.4053e+00, 9.2295e-01, 8.0661e-02, 2.4054e+00, 2.1468e+00,
8.5990e-01, 0.0000e+00
],
[
-1.9341e+00, -2.0741e+00, 3.0698e-03, 3.2206e-01,
2.5322e-01, 3.5144e-01, 0.0000e+00
],
[
-3.6908e+00, 8.0684e-03, 2.6201e-01, 4.1515e-01,
7.6489e-01, 5.3585e-01, 0.0000e+00
],
[
2.6332e+00, 8.5143e-01, -4.9964e-03, 3.0367e-01,
1.3448e+00, 1.8329e+00, 0.0000e+00
],
[
2.0221e-02, 2.6153e+00, 1.5109e-02, 7.3335e-01,
1.0429e+00, 1.0251e+00, 0.0000e+00
]]))
scores_3d = torch.tensor(
[1.2058e-04, 2.3012e-03, 6.2324e-06, 6.6139e-06, 6.7965e-05])
labels_3d = torch.tensor([0, 0, 0, 0, 0])
result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d)
results = [result]
scannet_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
# show function with pipeline
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2]),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
scannet_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
def test_seg_getitem():
np.random.seed(0)
root_path = './tests/data/scannet/'
ann_file = './tests/data/scannet/scannet_infos.pkl'
class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'otherfurniture')
palette = [
[174, 199, 232],
[152, 223, 138],
[31, 119, 180],
[255, 187, 120],
[188, 189, 34],
[140, 86, 75],
[255, 152, 150],
[214, 39, 40],
[197, 176, 213],
[148, 103, 189],
[196, 156, 148],
[23, 190, 207],
[247, 182, 210],
[219, 219, 141],
[255, 127, 14],
[158, 218, 229],
[44, 160, 44],
[112, 128, 144],
[227, 119, 194],
[82, 84, 163],
]
scene_idxs = [0 for _ in range(20)]
# test network inputs are (xyz, rgb, normalized_xyz)
pipelines = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24,
28, 33, 34, 36, 39),
max_cat_id=40),
dict(
type='IndoorPatchPointSample',
num_points=5,
block_size=1.5,
ignore_index=len(class_names),
use_normalized_coord=True,
enlarge_size=0.2,
min_unique_num=None),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
keys=['points', 'pts_semantic_mask'],
meta_keys=['file_name', 'sample_idx'])
]
scannet_dataset = ScanNetSegDataset(
data_root=root_path,
ann_file=ann_file,
pipeline=pipelines,
classes=None,
palette=None,
modality=None,
test_mode=False,
ignore_index=None,
scene_idxs=scene_idxs)
data = scannet_dataset[0]
points = data['points']._data
pts_semantic_mask = data['pts_semantic_mask']._data
file_name = data['img_metas']._data['file_name']
sample_idx = data['img_metas']._data['sample_idx']
assert file_name == './tests/data/scannet/points/scene0000_00.bin'
assert sample_idx == 'scene0000_00'
expected_points = torch.tensor([[
0.0000, 0.0000, 1.2427, 0.6118, 0.5529, 0.4471, -0.6462, -1.0046,
0.4280
],
[
0.1553, -0.0074, 1.6077, 0.5882,
0.6157, 0.5569, -0.6001, -1.0068,
0.5537
],
[
0.1518, 0.6016, 0.6548, 0.1490, 0.1059,
0.0431, -0.6012, -0.8309, 0.2255
],
[
-0.7494, 0.1033, 0.6756, 0.5216,
0.4353, 0.3333, -0.8687, -0.9748,
0.2327
],
[
-0.6836, -0.0203, 0.5884, 0.5765,
0.5020, 0.4510, -0.8491, -1.0105,
0.2027
]])
expected_pts_semantic_mask = np.array([13, 13, 12, 2, 0])
original_classes = scannet_dataset.CLASSES
original_palette = scannet_dataset.PALETTE
assert scannet_dataset.CLASSES == class_names
assert scannet_dataset.ignore_index == 20
assert torch.allclose(points, expected_points, 1e-2)
assert np.all(pts_semantic_mask.numpy() == expected_pts_semantic_mask)
assert original_classes == class_names
assert original_palette == palette
assert scannet_dataset.scene_idxs.dtype == np.int32
assert np.all(scannet_dataset.scene_idxs == np.array(scene_idxs))
# test network inputs are (xyz, rgb)
np.random.seed(0)
new_pipelines = copy.deepcopy(pipelines)
new_pipelines[3] = dict(
type='IndoorPatchPointSample',
num_points=5,
block_size=1.5,
ignore_index=len(class_names),
use_normalized_coord=False,
enlarge_size=0.2,
min_unique_num=None)
scannet_dataset = ScanNetSegDataset(
data_root=root_path,
ann_file=ann_file,
pipeline=new_pipelines,
scene_idxs=scene_idxs)
data = scannet_dataset[0]
points = data['points']._data
assert torch.allclose(points, expected_points[:, :6], 1e-2)
# test network inputs are (xyz, normalized_xyz)
np.random.seed(0)
new_pipelines = copy.deepcopy(pipelines)
new_pipelines[0] = dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=False,
load_dim=6,
use_dim=[0, 1, 2])
new_pipelines.remove(new_pipelines[4])
scannet_dataset = ScanNetSegDataset(
data_root=root_path,
ann_file=ann_file,
pipeline=new_pipelines,
scene_idxs=scene_idxs)
data = scannet_dataset[0]
points = data['points']._data
assert torch.allclose(points, expected_points[:, [0, 1, 2, 6, 7, 8]], 1e-2)
# test network inputs are (xyz,)
np.random.seed(0)
new_pipelines = copy.deepcopy(pipelines)
new_pipelines[0] = dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=False,
load_dim=6,
use_dim=[0, 1, 2])
new_pipelines[3] = dict(
type='IndoorPatchPointSample',
num_points=5,
block_size=1.5,
ignore_index=len(class_names),
use_normalized_coord=False,
enlarge_size=0.2,
min_unique_num=None)
new_pipelines.remove(new_pipelines[4])
scannet_dataset = ScanNetSegDataset(
data_root=root_path,
ann_file=ann_file,
pipeline=new_pipelines,
scene_idxs=scene_idxs)
data = scannet_dataset[0]
points = data['points']._data
assert torch.allclose(points, expected_points[:, :3], 1e-2)
# test dataset with selected classes
scannet_dataset = ScanNetSegDataset(
data_root=root_path,
ann_file=ann_file,
pipeline=None,
classes=['cabinet', 'chair'],
scene_idxs=scene_idxs)
label_map = {i: 20 for i in range(41)}
label_map.update({3: 0, 5: 1})
assert scannet_dataset.CLASSES != original_classes
assert scannet_dataset.CLASSES == ['cabinet', 'chair']
assert scannet_dataset.PALETTE == [palette[2], palette[4]]
assert scannet_dataset.VALID_CLASS_IDS == [3, 5]
assert scannet_dataset.label_map == label_map
assert scannet_dataset.label2cat == {0: 'cabinet', 1: 'chair'}
# test load classes from file
with tempfile.TemporaryDirectory() as tmpdir:
path = tmpdir + 'classes.txt'
with open(path, 'w') as f:
f.write('cabinet\nchair\n')
scannet_dataset = ScanNetSegDataset(
data_root=root_path,
ann_file=ann_file,
pipeline=None,
classes=path,
scene_idxs=scene_idxs)
assert scannet_dataset.CLASSES != original_classes
assert scannet_dataset.CLASSES == ['cabinet', 'chair']
assert scannet_dataset.PALETTE == [palette[2], palette[4]]
assert scannet_dataset.VALID_CLASS_IDS == [3, 5]
assert scannet_dataset.label_map == label_map
assert scannet_dataset.label2cat == {0: 'cabinet', 1: 'chair'}
# test scene_idxs in dataset
# we should input scene_idxs in train mode
with pytest.raises(NotImplementedError):
scannet_dataset = ScanNetSegDataset(
data_root=root_path,
ann_file=ann_file,
pipeline=None,
scene_idxs=None)
# test mode
scannet_dataset = ScanNetSegDataset(
data_root=root_path,
ann_file=ann_file,
pipeline=None,
test_mode=True,
scene_idxs=scene_idxs)
assert np.all(scannet_dataset.scene_idxs == np.array([0]))
def test_seg_evaluate():
if not torch.cuda.is_available():
pytest.skip()
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetSegDataset(
data_root=root_path, ann_file=ann_file, test_mode=True)
results = []
pred_sem_mask = dict(
semantic_mask=torch.tensor([
13, 5, 1, 2, 6, 2, 13, 1, 14, 2, 0, 0, 5, 5, 3, 0, 1, 14, 0, 0, 0,
18, 6, 15, 13, 0, 2, 4, 0, 3, 16, 6, 13, 5, 13, 0, 0, 0, 0, 1, 7,
3, 19, 12, 8, 0, 11, 0, 0, 1, 2, 13, 17, 1, 1, 1, 6, 2, 13, 19, 4,
17, 0, 14, 1, 7, 2, 1, 7, 2, 0, 5, 17, 5, 0, 0, 3, 6, 5, 11, 1, 13,
13, 2, 3, 1, 0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
]).long())
results.append(pred_sem_mask)
class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'otherfurniture')
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24,
28, 33, 34, 36, 39),
max_cat_id=40),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
]
ret_dict = scannet_dataset.evaluate(results, pipeline=eval_pipeline)
assert abs(ret_dict['miou'] - 0.5308) < 0.01
assert abs(ret_dict['acc'] - 0.8219) < 0.01
assert abs(ret_dict['acc_cls'] - 0.7649) < 0.01
def test_seg_show():
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetSegDataset(
data_root=root_path, ann_file=ann_file, scene_idxs=[0])
result = dict(
semantic_mask=torch.tensor([
13, 5, 1, 2, 6, 2, 13, 1, 14, 2, 0, 0, 5, 5, 3, 0, 1, 14, 0, 0, 0,
18, 6, 15, 13, 0, 2, 4, 0, 3, 16, 6, 13, 5, 13, 0, 0, 0, 0, 1, 7,
3, 19, 12, 8, 0, 11, 0, 0, 1, 2, 13, 17, 1, 1, 1, 6, 2, 13, 19, 4,
17, 0, 14, 1, 7, 2, 1, 7, 2, 0, 5, 17, 5, 0, 0, 3, 6, 5, 11, 1, 13,
13, 2, 3, 1, 0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
]).long())
results = [result]
scannet_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
# test show with pipeline
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'otherfurniture')
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24,
28, 33, 34, 36, 39),
max_cat_id=40),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
]
scannet_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
def test_seg_format_results():
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetSegDataset(
data_root=root_path, ann_file=ann_file, test_mode=True)
results = []
pred_sem_mask = dict(
semantic_mask=torch.tensor([
13, 5, 1, 2, 6, 2, 13, 1, 14, 2, 0, 0, 5, 5, 3, 0, 1, 14, 0, 0, 0,
18, 6, 15, 13, 0, 2, 4, 0, 3, 16, 6, 13, 5, 13, 0, 0, 0, 0, 1, 7,
3, 19, 12, 8, 0, 11, 0, 0, 1, 2, 13, 17, 1, 1, 1, 6, 2, 13, 19, 4,
17, 0, 14, 1, 7, 2, 1, 7, 2, 0, 5, 17, 5, 0, 0, 3, 6, 5, 11, 1, 13,
13, 2, 3, 1, 0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
]).long())
results.append(pred_sem_mask)
result_files, tmp_dir = scannet_dataset.format_results(results)
expected_label = np.array([
16, 6, 2, 3, 7, 3, 16, 2, 24, 3, 1, 1, 6, 6, 4, 1, 2, 24, 1, 1, 1, 36,
7, 28, 16, 1, 3, 5, 1, 4, 33, 7, 16, 6, 16, 1, 1, 1, 1, 2, 8, 4, 39,
14, 9, 1, 12, 1, 1, 2, 3, 16, 34, 2, 2, 2, 7, 3, 16, 39, 5, 34, 1, 24,
2, 8, 3, 2, 8, 3, 1, 6, 34, 6, 1, 1, 4, 7, 6, 12, 2, 16, 16, 3, 4, 2,
1, 16, 39, 2, 24, 6, 4, 2, 16, 2, 3, 4, 3, 2
])
expected_txt_path = osp.join(tmp_dir.name, 'results', 'scene0000_00.txt')
assert np.all(result_files[0]['seg_mask'] == expected_label)
mmcv.check_file_exist(expected_txt_path)
def test_instance_seg_getitem():
np.random.seed(0)
root_path = './tests/data/scannet/'
ann_file = './tests/data/scannet/scannet_infos.pkl'
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=True,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33,
34, 36, 39),
max_cat_id=40),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
keys=['points', 'pts_semantic_mask', 'pts_instance_mask'])
] | scannet_dataset = ScanNetInstanceSegDataset( | 1 | 2023-12-21 12:50:35+00:00 | 16k |
v3ucn/Bert-vits2-V2.2 | train_ms.py | [
{
"identifier": "config",
"path": "config.py",
"snippet": "class Resample_config:\nclass Preprocess_text_config:\nclass Bert_gen_config:\nclass Emo_gen_config:\nclass Train_ms_config:\nclass Webui_config:\nclass Server_config:\nclass Translate_config:\nclass Config:\n def __init__(self, in_dir: str, ... | import platform
import os
import torch
import torch.distributed as dist
import logging
import argparse
import datetime
import gc
import commons
import utils
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, GradScaler
from tqdm import tqdm
from config import config
from data_utils import (
TextAudioSpeakerLoader,
TextAudioSpeakerCollate,
DistributedBucketSampler,
)
from models import (
SynthesizerTrn,
MultiPeriodDiscriminator,
DurationDiscriminator,
)
from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from text.symbols import symbols | 11,642 |
def train_and_evaluate(
rank,
local_rank,
epoch,
hps,
nets,
optims,
schedulers,
scaler,
loaders,
logger,
writers,
):
net_g, net_d, net_dur_disc = nets
optim_g, optim_d, optim_dur_disc = optims
scheduler_g, scheduler_d, scheduler_dur_disc = schedulers
train_loader, eval_loader = loaders
if writers is not None:
writer, writer_eval = writers
train_loader.batch_sampler.set_epoch(epoch)
global global_step
net_g.train()
net_d.train()
if net_dur_disc is not None:
net_dur_disc.train()
for batch_idx, (
x,
x_lengths,
spec,
spec_lengths,
y,
y_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
emo,
) in enumerate(tqdm(train_loader)):
if net_g.module.use_noise_scaled_mas:
current_mas_noise_scale = (
net_g.module.mas_noise_scale_initial
- net_g.module.noise_scale_delta * global_step
)
net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
x, x_lengths = x.cuda(local_rank, non_blocking=True), x_lengths.cuda(
local_rank, non_blocking=True
)
spec, spec_lengths = spec.cuda(
local_rank, non_blocking=True
), spec_lengths.cuda(local_rank, non_blocking=True)
y, y_lengths = y.cuda(local_rank, non_blocking=True), y_lengths.cuda(
local_rank, non_blocking=True
)
speakers = speakers.cuda(local_rank, non_blocking=True)
tone = tone.cuda(local_rank, non_blocking=True)
language = language.cuda(local_rank, non_blocking=True)
bert = bert.cuda(local_rank, non_blocking=True)
ja_bert = ja_bert.cuda(local_rank, non_blocking=True)
en_bert = en_bert.cuda(local_rank, non_blocking=True)
emo = emo.cuda(local_rank, non_blocking=True)
with autocast(enabled=hps.train.fp16_run):
(
y_hat,
l_length,
attn,
ids_slice,
x_mask,
z_mask,
(z, z_p, m_p, logs_p, m_q, logs_q),
(hidden_x, logw, logw_),
g,
loss_commit,
) = net_g(
x,
x_lengths,
spec,
spec_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
emo,
)
mel = spec_to_mel_torch(
spec,
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
y_mel = commons.slice_segments(
mel, ids_slice, hps.train.segment_size // hps.data.hop_length
)
y_hat_mel = mel_spectrogram_torch(
y_hat.squeeze(1),
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
y = commons.slice_segments(
y, ids_slice * hps.data.hop_length, hps.train.segment_size
) # slice
# Discriminator
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
with autocast(enabled=False):
| # flake8: noqa: E402
logging.getLogger("numba").setLevel(logging.WARNING)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = (
True # If encontered training problem,please try to disable TF32.
)
torch.set_float32_matmul_precision("medium")
torch.backends.cuda.sdp_kernel("flash")
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_mem_efficient_sdp(
True
) # Not available if torch version is lower than 2.0
torch.backends.cuda.enable_math_sdp(True)
global_step = 0
def run():
# 环境变量解析
envs = config.train_ms_config.env
for env_name, env_value in envs.items():
if env_name not in os.environ.keys():
print("加载config中的配置{}".format(str(env_value)))
os.environ[env_name] = str(env_value)
print(
"加载环境变量 \nMASTER_ADDR: {},\nMASTER_PORT: {},\nWORLD_SIZE: {},\nRANK: {},\nLOCAL_RANK: {}".format(
os.environ["MASTER_ADDR"],
os.environ["MASTER_PORT"],
os.environ["WORLD_SIZE"],
os.environ["RANK"],
os.environ["LOCAL_RANK"],
)
)
backend = "nccl"
if platform.system() == "Windows":
backend = "gloo" # If Windows,switch to gloo backend.
dist.init_process_group(
backend=backend,
init_method="env://",
timeout=datetime.timedelta(seconds=300),
) # Use torchrun instead of mp.spawn
rank = dist.get_rank()
local_rank = int(os.environ["LOCAL_RANK"])
n_gpus = dist.get_world_size()
# 命令行/config.yml配置解析
# hps = utils.get_hparams()
parser = argparse.ArgumentParser()
# 非必要不建议使用命令行配置，请使用config.yml文件
parser.add_argument(
"-c",
"--config",
type=str,
default=config.train_ms_config.config_path,
help="JSON file for configuration",
)
parser.add_argument(
"-m",
"--model",
type=str,
help="数据集文件夹路径，请注意，数据不再默认放在/logs文件夹下。如果需要用命令行配置，请声明相对于根目录的路径",
default=config.dataset_path,
)
args = parser.parse_args()
model_dir = os.path.join(args.model, config.train_ms_config.model)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
hps = utils.get_hparams_from_file(args.config)
hps.model_dir = model_dir
# 比较路径是否相同
if os.path.realpath(args.config) != os.path.realpath(
config.train_ms_config.config_path
):
with open(args.config, "r", encoding="utf-8") as f:
data = f.read()
with open(config.train_ms_config.config_path, "w", encoding="utf-8") as f:
f.write(data)
torch.manual_seed(hps.train.seed)
torch.cuda.set_device(local_rank)
global global_step
if rank == 0:
logger = utils.get_logger(hps.model_dir)
logger.info(hps)
utils.check_git_hash(hps.model_dir)
writer = SummaryWriter(log_dir=hps.model_dir)
writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
train_sampler = DistributedBucketSampler(
train_dataset,
hps.train.batch_size,
[32, 300, 400, 500, 600, 700, 800, 900, 1000],
num_replicas=n_gpus,
rank=rank,
shuffle=True,
)
collate_fn = TextAudioSpeakerCollate()
train_loader = DataLoader(
train_dataset,
num_workers=min(config.train_ms_config.num_workers, os.cpu_count() - 1),
shuffle=False,
pin_memory=True,
collate_fn=collate_fn,
batch_sampler=train_sampler,
persistent_workers=True,
prefetch_factor=4,
) # DataLoader config could be adjusted.
if rank == 0:
eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
eval_loader = DataLoader(
eval_dataset,
num_workers=0,
shuffle=False,
batch_size=1,
pin_memory=True,
drop_last=False,
collate_fn=collate_fn,
)
if (
"use_noise_scaled_mas" in hps.model.keys()
and hps.model.use_noise_scaled_mas is True
):
print("Using noise scaled MAS for VITS2")
mas_noise_scale_initial = 0.01
noise_scale_delta = 2e-6
else:
print("Using normal MAS for VITS1")
mas_noise_scale_initial = 0.0
noise_scale_delta = 0.0
if (
"use_duration_discriminator" in hps.model.keys()
and hps.model.use_duration_discriminator is True
):
print("Using duration discriminator for VITS2")
net_dur_disc = DurationDiscriminator(
hps.model.hidden_channels,
hps.model.hidden_channels,
3,
0.1,
gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
).cuda(local_rank)
if (
"use_spk_conditioned_encoder" in hps.model.keys()
and hps.model.use_spk_conditioned_encoder is True
):
if hps.data.n_speakers == 0:
raise ValueError(
"n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model"
)
else:
print("Using normal encoder for VITS1")
net_g = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
mas_noise_scale_initial=mas_noise_scale_initial,
noise_scale_delta=noise_scale_delta,
**hps.model,
).cuda(local_rank)
if getattr(hps.train, "freeze_ZH_bert", False):
print("Freezing ZH bert encoder !!!")
for param in net_g.enc_p.bert_proj.parameters():
param.requires_grad = False
if getattr(hps.train, "freeze_EN_bert", False):
print("Freezing EN bert encoder !!!")
for param in net_g.enc_p.en_bert_proj.parameters():
param.requires_grad = False
if getattr(hps.train, "freeze_JP_bert", False):
print("Freezing JP bert encoder !!!")
for param in net_g.enc_p.ja_bert_proj.parameters():
param.requires_grad = False
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(local_rank)
optim_g = torch.optim.AdamW(
filter(lambda p: p.requires_grad, net_g.parameters()),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps,
)
optim_d = torch.optim.AdamW(
net_d.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps,
)
if net_dur_disc is not None:
optim_dur_disc = torch.optim.AdamW(
net_dur_disc.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps,
)
else:
optim_dur_disc = None
net_g = DDP(net_g, device_ids=[local_rank], bucket_cap_mb=512)
net_d = DDP(net_d, device_ids=[local_rank], bucket_cap_mb=512)
dur_resume_lr = None
if net_dur_disc is not None:
net_dur_disc = DDP(
net_dur_disc,
device_ids=[local_rank],
find_unused_parameters=True,
bucket_cap_mb=512,
)
# 下载底模
if config.train_ms_config.base["use_base_model"]:
utils.download_checkpoint(
hps.model_dir,
config.train_ms_config.base,
token=config.openi_token,
mirror=config.mirror,
)
try:
if net_dur_disc is not None:
_, _, dur_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"),
net_dur_disc,
optim_dur_disc,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
_, optim_g, g_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"),
net_g,
optim_g,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
_, optim_d, d_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"),
net_d,
optim_d,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
if not optim_g.param_groups[0].get("initial_lr"):
optim_g.param_groups[0]["initial_lr"] = g_resume_lr
if not optim_d.param_groups[0].get("initial_lr"):
optim_d.param_groups[0]["initial_lr"] = d_resume_lr
if not optim_dur_disc.param_groups[0].get("initial_lr"):
optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
epoch_str = max(epoch_str, 1)
# global_step = (epoch_str - 1) * len(train_loader)
global_step = int(
utils.get_steps(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"))
)
print(
f"******************检测到模型存在，epoch为 {epoch_str}，gloabl step为 {global_step}*********************"
)
except Exception as e:
print(e)
epoch_str = 1
global_step = 0
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
)
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
)
if net_dur_disc is not None:
if not optim_dur_disc.param_groups[0].get("initial_lr"):
optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(
optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
)
else:
scheduler_dur_disc = None
scaler = GradScaler(enabled=hps.train.fp16_run)
for epoch in range(epoch_str, hps.train.epochs + 1):
if rank == 0:
train_and_evaluate(
rank,
local_rank,
epoch,
hps,
[net_g, net_d, net_dur_disc],
[optim_g, optim_d, optim_dur_disc],
[scheduler_g, scheduler_d, scheduler_dur_disc],
scaler,
[train_loader, eval_loader],
logger,
[writer, writer_eval],
)
else:
train_and_evaluate(
rank,
local_rank,
epoch,
hps,
[net_g, net_d, net_dur_disc],
[optim_g, optim_d, optim_dur_disc],
[scheduler_g, scheduler_d, scheduler_dur_disc],
scaler,
[train_loader, None],
None,
None,
)
scheduler_g.step()
scheduler_d.step()
if net_dur_disc is not None:
scheduler_dur_disc.step()
def train_and_evaluate(
rank,
local_rank,
epoch,
hps,
nets,
optims,
schedulers,
scaler,
loaders,
logger,
writers,
):
net_g, net_d, net_dur_disc = nets
optim_g, optim_d, optim_dur_disc = optims
scheduler_g, scheduler_d, scheduler_dur_disc = schedulers
train_loader, eval_loader = loaders
if writers is not None:
writer, writer_eval = writers
train_loader.batch_sampler.set_epoch(epoch)
global global_step
net_g.train()
net_d.train()
if net_dur_disc is not None:
net_dur_disc.train()
for batch_idx, (
x,
x_lengths,
spec,
spec_lengths,
y,
y_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
emo,
) in enumerate(tqdm(train_loader)):
if net_g.module.use_noise_scaled_mas:
current_mas_noise_scale = (
net_g.module.mas_noise_scale_initial
- net_g.module.noise_scale_delta * global_step
)
net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
x, x_lengths = x.cuda(local_rank, non_blocking=True), x_lengths.cuda(
local_rank, non_blocking=True
)
spec, spec_lengths = spec.cuda(
local_rank, non_blocking=True
), spec_lengths.cuda(local_rank, non_blocking=True)
y, y_lengths = y.cuda(local_rank, non_blocking=True), y_lengths.cuda(
local_rank, non_blocking=True
)
speakers = speakers.cuda(local_rank, non_blocking=True)
tone = tone.cuda(local_rank, non_blocking=True)
language = language.cuda(local_rank, non_blocking=True)
bert = bert.cuda(local_rank, non_blocking=True)
ja_bert = ja_bert.cuda(local_rank, non_blocking=True)
en_bert = en_bert.cuda(local_rank, non_blocking=True)
emo = emo.cuda(local_rank, non_blocking=True)
with autocast(enabled=hps.train.fp16_run):
(
y_hat,
l_length,
attn,
ids_slice,
x_mask,
z_mask,
(z, z_p, m_p, logs_p, m_q, logs_q),
(hidden_x, logw, logw_),
g,
loss_commit,
) = net_g(
x,
x_lengths,
spec,
spec_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
emo,
)
mel = spec_to_mel_torch(
spec,
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
y_mel = commons.slice_segments(
mel, ids_slice, hps.train.segment_size // hps.data.hop_length
)
y_hat_mel = mel_spectrogram_torch(
y_hat.squeeze(1),
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
y = commons.slice_segments(
y, ids_slice * hps.data.hop_length, hps.train.segment_size
) # slice
# Discriminator
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
with autocast(enabled=False): | loss_disc, losses_disc_r, losses_disc_g = discriminator_loss( | 8 | 2023-12-18 04:54:46+00:00 | 16k |
m-abr/FCPCodebase | world/Robot.py | [
{
"identifier": "Math_Ops",
"path": "math_ops/Math_Ops.py",
"snippet": "class Math_Ops():\n '''\n This class provides general mathematical operations that are not directly available through numpy \n '''\n \n @staticmethod\n def deg_sph2cart(spherical_vec):\n ''' Converts SimSpark'... | from collections import deque
from math import atan, pi, sqrt, tan
from math_ops.Math_Ops import Math_Ops as M
from math_ops.Matrix_3x3 import Matrix_3x3
from math_ops.Matrix_4x4 import Matrix_4x4
from world.commons.Body_Part import Body_Part
from world.commons.Joint_Info import Joint_Info
import numpy as np
import xml.etree.ElementTree as xmlp | 12,934 |
class Robot():
STEPTIME = 0.02 # Fixed step time
VISUALSTEP = 0.04 # Fixed visual step time
SQ_STEPTIME = STEPTIME * STEPTIME
GRAVITY = np.array([0,0,-9.81])
IMU_DECAY = 0.996 #IMU's velocity decay
#------------------ constants to force symmetry in joints/effectors
MAP_PERCEPTOR_TO_INDEX = {"hj1":0, "hj2":1, "llj1":2, "rlj1":3,
"llj2":4, "rlj2":5, "llj3":6, "rlj3":7,
"llj4":8, "rlj4":9, "llj5":10,"rlj5":11,
"llj6":12,"rlj6":13,"laj1":14,"raj1":15,
"laj2":16,"raj2":17,"laj3":18,"raj3":19,
"laj4":20,"raj4":21,"llj7":22,"rlj7":23 }
# Fix symmetry issues 1a/4 (identification)
FIX_PERCEPTOR_SET = {'rlj2','rlj6','raj2','laj3','laj4'}
FIX_INDICES_LIST = [5,13,17,18,20]
# Recommended height for unofficial beam (near ground)
BEAM_HEIGHTS = [0.4, 0.43, 0.4, 0.46, 0.4]
def __init__(self, unum:int, robot_type:int) -> None:
robot_xml = "nao"+str(robot_type)+".xml" # Typical NAO file name
self.type = robot_type
self.beam_height = Robot.BEAM_HEIGHTS[robot_type]
self.no_of_joints = 24 if robot_type == 4 else 22
#Fix symmetry issues 1b/4 (identification)
self.FIX_EFFECTOR_MASK = np.ones(self.no_of_joints)
self.FIX_EFFECTOR_MASK[Robot.FIX_INDICES_LIST] = -1
self.body_parts = dict() # keys='body part names' (given by the robot's XML), values='Body_Part objects'
self.unum = unum # Robot's uniform number
self.gyro = np.zeros(3) # Angular velocity along the three axes of freedom of the robot's torso (deg/s)
self.acc = np.zeros(3) # Proper acceleration along the three axes of freedom of the robot's torso (m/s2)
self.frp = dict() # foot "lf"/"rf", toe "lf1"/"rf1" resistance perceptor (relative [p]oint of origin + [f]orce vector) e.g. {"lf":(px,py,pz,fx,fy,fz)}
self.feet_toes_last_touch = {"lf":0,"rf":0,"lf1":0,"rf1":0} # foot "lf"/"rf", toe "lf1"/"rf1" World.time_local_ms when foot/toe last touched any surface
self.feet_toes_are_touching = {"lf":False,"rf":False,"lf1":False,"rf1":False} # foot "lf"/"rf", toe "lf1"/"rf1" True if touching in last received server message
self.fwd_kinematics_list = None # List of body parts, ordered according to dependencies
self.rel_cart_CoM_position = np.zeros(3) # Center of Mass position, relative to head, in cartesian coordinates (m)
# Joint variables are optimized for performance / array operations
self.joints_position = np.zeros(self.no_of_joints) # Joints' angular position (deg)
self.joints_speed = np.zeros(self.no_of_joints) # Joints' angular speed (rad/s)
self.joints_target_speed = np.zeros(self.no_of_joints) # Joints' target speed (rad/s) (max: 6.1395 rad/s, see rcssserver3d/data/rsg/agent/nao/hingejoint.rsg)
self.joints_target_last_speed = np.zeros(self.no_of_joints) # Joints' last target speed (rad/s) (max: 6.1395 rad/s, see rcssserver3d/data/rsg/agent/nao/hingejoint.rsg)
self.joints_info = [None] * self.no_of_joints # Joints' constant information (see class Joint_Info)
self.joints_transform = [Matrix_4x4() for _ in range(self.no_of_joints)] # Joints' transformation matrix
# Localization variables relative to head
self.loc_head_to_field_transform = Matrix_4x4() # Transformation matrix from head to field
self.loc_field_to_head_transform = Matrix_4x4() # Transformation matrix from field to head
|
class Robot():
STEPTIME = 0.02 # Fixed step time
VISUALSTEP = 0.04 # Fixed visual step time
SQ_STEPTIME = STEPTIME * STEPTIME
GRAVITY = np.array([0,0,-9.81])
IMU_DECAY = 0.996 #IMU's velocity decay
#------------------ constants to force symmetry in joints/effectors
MAP_PERCEPTOR_TO_INDEX = {"hj1":0, "hj2":1, "llj1":2, "rlj1":3,
"llj2":4, "rlj2":5, "llj3":6, "rlj3":7,
"llj4":8, "rlj4":9, "llj5":10,"rlj5":11,
"llj6":12,"rlj6":13,"laj1":14,"raj1":15,
"laj2":16,"raj2":17,"laj3":18,"raj3":19,
"laj4":20,"raj4":21,"llj7":22,"rlj7":23 }
# Fix symmetry issues 1a/4 (identification)
FIX_PERCEPTOR_SET = {'rlj2','rlj6','raj2','laj3','laj4'}
FIX_INDICES_LIST = [5,13,17,18,20]
# Recommended height for unofficial beam (near ground)
BEAM_HEIGHTS = [0.4, 0.43, 0.4, 0.46, 0.4]
def __init__(self, unum:int, robot_type:int) -> None:
robot_xml = "nao"+str(robot_type)+".xml" # Typical NAO file name
self.type = robot_type
self.beam_height = Robot.BEAM_HEIGHTS[robot_type]
self.no_of_joints = 24 if robot_type == 4 else 22
#Fix symmetry issues 1b/4 (identification)
self.FIX_EFFECTOR_MASK = np.ones(self.no_of_joints)
self.FIX_EFFECTOR_MASK[Robot.FIX_INDICES_LIST] = -1
self.body_parts = dict() # keys='body part names' (given by the robot's XML), values='Body_Part objects'
self.unum = unum # Robot's uniform number
self.gyro = np.zeros(3) # Angular velocity along the three axes of freedom of the robot's torso (deg/s)
self.acc = np.zeros(3) # Proper acceleration along the three axes of freedom of the robot's torso (m/s2)
self.frp = dict() # foot "lf"/"rf", toe "lf1"/"rf1" resistance perceptor (relative [p]oint of origin + [f]orce vector) e.g. {"lf":(px,py,pz,fx,fy,fz)}
self.feet_toes_last_touch = {"lf":0,"rf":0,"lf1":0,"rf1":0} # foot "lf"/"rf", toe "lf1"/"rf1" World.time_local_ms when foot/toe last touched any surface
self.feet_toes_are_touching = {"lf":False,"rf":False,"lf1":False,"rf1":False} # foot "lf"/"rf", toe "lf1"/"rf1" True if touching in last received server message
self.fwd_kinematics_list = None # List of body parts, ordered according to dependencies
self.rel_cart_CoM_position = np.zeros(3) # Center of Mass position, relative to head, in cartesian coordinates (m)
# Joint variables are optimized for performance / array operations
self.joints_position = np.zeros(self.no_of_joints) # Joints' angular position (deg)
self.joints_speed = np.zeros(self.no_of_joints) # Joints' angular speed (rad/s)
self.joints_target_speed = np.zeros(self.no_of_joints) # Joints' target speed (rad/s) (max: 6.1395 rad/s, see rcssserver3d/data/rsg/agent/nao/hingejoint.rsg)
self.joints_target_last_speed = np.zeros(self.no_of_joints) # Joints' last target speed (rad/s) (max: 6.1395 rad/s, see rcssserver3d/data/rsg/agent/nao/hingejoint.rsg)
self.joints_info = [None] * self.no_of_joints # Joints' constant information (see class Joint_Info)
self.joints_transform = [Matrix_4x4() for _ in range(self.no_of_joints)] # Joints' transformation matrix
# Localization variables relative to head
self.loc_head_to_field_transform = Matrix_4x4() # Transformation matrix from head to field
self.loc_field_to_head_transform = Matrix_4x4() # Transformation matrix from field to head | self.loc_rotation_head_to_field = Matrix_3x3() # Rotation matrix from head to field | 1 | 2023-12-16 23:40:23+00:00 | 16k |
Sam-Izdat/tinycio | src/tinycio/util/colorutil.py | [
{
"identifier": "Float2",
"path": "src/tinycio/numerics/vector.py",
"snippet": "class Float2(np.ndarray):\n \"\"\"\n Float2 type using numpy.ndarray.\n \"\"\"\n def __new__(cls, *args):\n if len(args) == 1:\n if isinstance(args[0], list) or isinstance(args[0], tuple):\n ... | import typing
import torch
import numpy as np
from typing import Union
from ..numerics import Float2, Float3 | 11,196 |
def col_hsv_to_rgb(hsv:Union[Float3, Color]) -> Float3:
"""
Convert HSV color to RGB.
:param hsv: HSV color
:type hsv: Float3 | Color
"""
h, s, v = hsv.x, hsv.y, hsv.z
i = np.floor(h * 6)
f = h * 6 - i
p = v * (1 - s)
q = v * (1 - f * s)
t = v * (1 - (1 - f) * s)
r, g, b = [
(v, t, p),
(q, v, p),
(p, v, t),
(p, q, v),
(t, p, v),
(v, p, q),
][int(i%6)]
return Float3(r, g, b)
def col_rgb_to_hsv(rgb:Union[Float3, Color]) -> Float3:
"""
Convert RGB color to HSV.
:param rgb: RGB color
:type rgb: Float3 | Color
"""
r, g, b = rgb.r, rgb.g, rgb.b
high = np.max([r, g, b])
low = np.min([r, g, b])
h, s, v = high, high, high
d = high - low
s = 0 if high == 0 else d/high
if high == low:
h = 0.0
else:
h = {
r: (g - b) / d + (6 if g < b else 0),
g: (b - r) / d + 2,
b: (r - g) / d + 4,
}[high]
h /= 6
return Float3(h, s, v)
# My fairly lazy OKHSV/OKHSL "port" of:
# https://github.com/holbrookdev/ok-color-picker
# MIT License
# Copyright (c) 2022 Brian Holbrook
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
__ok_eps = 1e-7
def col_oklab_to_linear_srgb(lab:Union[Float3, Color]) -> Float3:
"""
Convert OKLAB color to linear sRGB.
:param lab: OKLAB color
:type lab: Float3 | Color
"""
L, a, b = lab.x, lab.y, lab.z
l_ = L + 0.3963377774 * a + 0.2158037573 * b
m_ = L - 0.1055613458 * a - 0.0638541728 * b
s_ = L - 0.0894841775 * a - 1.291485548 * b
l = l_ * l_ * l_
m = m_ * m_ * m_
s = s_ * s_ * s_
res = Float3(
+4.0767416621 * l - 3.3077115913 * m + 0.2309699292 * s,
-1.2684380046 * l + 2.6097574011 * m - 0.3413193965 * s,
-0.0041960863 * l - 0.7034186147 * m + 1.707614701 * s
)
return res
def col_linear_srgb_to_oklab(srgb:Union[Float3, Color]) -> Float3:
"""
Convert linear sRGB color to OKLAB.
:param srgb: linear sRGB color
:type srgb: Float3 | Color
"""
r, g, b = srgb.r, srgb.g, srgb.b
l = 0.4122214708 * r + 0.5363325363 * g + 0.0514459929 * b
m = 0.2119034982 * r + 0.6806995451 * g + 0.1073969566 * b
s = 0.0883024619 * r + 0.2817188376 * g + 0.6299787005 * b
l_ = np.cbrt(l)
m_ = np.cbrt(m)
s_ = np.cbrt(s)
res = Float3(
0.2104542553 * l_ + 0.793617785 * m_ - 0.0040720468 * s_,
1.9779984951 * l_ - 2.428592205 * m_ + 0.4505937099 * s_,
0.0259040371 * l_ + 0.7827717662 * m_ - 0.808675766 * s_
)
return res
| from __future__ import annotations
def srgb_luminance(im_srgb:Union[torch.Tensor, ColorImage]) -> torch.Tensor:
"""
Return relative luminance of linear sRGB image.
:param im_srgb: [C=3, H, W] color image tensor in sRGB color space
:type im_srgb: torch.Tensor | ColorImage
:return: [C=1, H, W] image tensor
"""
lum_r, lum_g, lum_b = 0.2126, 0.7152, 0.0722
return lum_r * im_srgb[0:1,...] + lum_g * im_srgb[1:2,...] + lum_b * im_srgb[2:3,...]
def apply_gamma(im:Union[torch.Tensor, ColorImage], gamma:float) -> torch.Tensor:
"""
Apply arbitrary gamma correction.
:param im: Image tensor
:type im: torch.Tensor | ColorImage
:param gamma: Gamma correction (should be in the range [0.1, 10.0])
:return: Gamma-corrected image tensor
"""
if gamma == 1.: return im
assert 0.1 <= gamma <= 10.0, "gamma value should be in range [0.1, 10.0]"
im = torch.pow(im, gamma)
def apply_hue_oklab(im_oklab:Union[torch.Tensor, ColorImage], hue_delta:float) -> torch.Tensor:
"""
Manually shift hue of an image by a -1 to +1 delta value.
:param im_oklab: Image tensor in OKLAB color space
:type im_oklab: torch.Tensor | ColorImage
:param hue_delta: Hue shift value in the range [-1., 1.]
:return: Image tensor in OKLAB color space with adjusted hue
"""
assert -1. <= hue_delta <= 1., "hue_delta value should be in range [-1., 1.]"
L, a, b = im_oklab[0:1], im_oklab[1:2], im_oklab[2:3]
hue_delta = ((hue_delta * 0.5) % 1.) * 2. * torch.pi
# Calculate angle and magnitude in the a-b plane
angle = torch.atan2(b, a)
magnitude = torch.sqrt(a**2 + b**2)
# Apply hue correction
angle += hue_delta
# Convert back to Cartesian coordinates
a_corrected = magnitude * torch.cos(angle)
b_corrected = magnitude * torch.sin(angle)
corrected = torch.cat([L, a_corrected, b_corrected], dim=0)
return corrected
return im
def col_hsv_to_rgb(hsv:Union[Float3, Color]) -> Float3:
"""
Convert HSV color to RGB.
:param hsv: HSV color
:type hsv: Float3 | Color
"""
h, s, v = hsv.x, hsv.y, hsv.z
i = np.floor(h * 6)
f = h * 6 - i
p = v * (1 - s)
q = v * (1 - f * s)
t = v * (1 - (1 - f) * s)
r, g, b = [
(v, t, p),
(q, v, p),
(p, v, t),
(p, q, v),
(t, p, v),
(v, p, q),
][int(i%6)]
return Float3(r, g, b)
def col_rgb_to_hsv(rgb:Union[Float3, Color]) -> Float3:
"""
Convert RGB color to HSV.
:param rgb: RGB color
:type rgb: Float3 | Color
"""
r, g, b = rgb.r, rgb.g, rgb.b
high = np.max([r, g, b])
low = np.min([r, g, b])
h, s, v = high, high, high
d = high - low
s = 0 if high == 0 else d/high
if high == low:
h = 0.0
else:
h = {
r: (g - b) / d + (6 if g < b else 0),
g: (b - r) / d + 2,
b: (r - g) / d + 4,
}[high]
h /= 6
return Float3(h, s, v)
# My fairly lazy OKHSV/OKHSL "port" of:
# https://github.com/holbrookdev/ok-color-picker
# MIT License
# Copyright (c) 2022 Brian Holbrook
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
__ok_eps = 1e-7
def col_oklab_to_linear_srgb(lab:Union[Float3, Color]) -> Float3:
"""
Convert OKLAB color to linear sRGB.
:param lab: OKLAB color
:type lab: Float3 | Color
"""
L, a, b = lab.x, lab.y, lab.z
l_ = L + 0.3963377774 * a + 0.2158037573 * b
m_ = L - 0.1055613458 * a - 0.0638541728 * b
s_ = L - 0.0894841775 * a - 1.291485548 * b
l = l_ * l_ * l_
m = m_ * m_ * m_
s = s_ * s_ * s_
res = Float3(
+4.0767416621 * l - 3.3077115913 * m + 0.2309699292 * s,
-1.2684380046 * l + 2.6097574011 * m - 0.3413193965 * s,
-0.0041960863 * l - 0.7034186147 * m + 1.707614701 * s
)
return res
def col_linear_srgb_to_oklab(srgb:Union[Float3, Color]) -> Float3:
"""
Convert linear sRGB color to OKLAB.
:param srgb: linear sRGB color
:type srgb: Float3 | Color
"""
r, g, b = srgb.r, srgb.g, srgb.b
l = 0.4122214708 * r + 0.5363325363 * g + 0.0514459929 * b
m = 0.2119034982 * r + 0.6806995451 * g + 0.1073969566 * b
s = 0.0883024619 * r + 0.2817188376 * g + 0.6299787005 * b
l_ = np.cbrt(l)
m_ = np.cbrt(m)
s_ = np.cbrt(s)
res = Float3(
0.2104542553 * l_ + 0.793617785 * m_ - 0.0040720468 * s_,
1.9779984951 * l_ - 2.428592205 * m_ + 0.4505937099 * s_,
0.0259040371 * l_ + 0.7827717662 * m_ - 0.808675766 * s_
)
return res
| def __ok_compute_max_saturation(ab:Float2) -> float: | 0 | 2023-12-15 15:39:08+00:00 | 16k |
quocanh34/magic-animate-modified | magicanimate/pipelines/pipeline_animation.py | [
{
"identifier": "UNet3DConditionModel",
"path": "magicanimate/models/unet_controlnet.py",
"snippet": "class UNet3DConditionModel(ModelMixin, ConfigMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,... | import inspect, math
import numpy as np
import torch
import torch.distributed as dist
from typing import Callable, List, Optional, Union
from dataclasses import dataclass
from PIL import Image
from tqdm import tqdm
from diffusers.utils import is_accelerate_available
from packaging import version
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from diffusers.utils import deprecate, logging, BaseOutput
from einops import rearrange
from magicanimate.models.unet_controlnet import UNet3DConditionModel
from magicanimate.models.multicontrolnet import ControlNetProcessor #fix
from magicanimate.models.mutual_self_attention import ReferenceAttentionControl
from magicanimate.pipelines.context import (
get_context_scheduler,
get_total_steps
)
from magicanimate.utils.util import get_tensor_interpolation_method
from accelerate import cpu_offload | 13,110 | # *************************************************************************
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
# ytedance Inc..
# *************************************************************************
# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
TODO:
1. support multi-controlnet
2. [DONE] support DDIM inversion
3. support Prompt-to-prompt
"""
# from magicanimate.models.controlnet import ControlNetModel
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class AnimationPipelineOutput(BaseOutput):
videos: Union[torch.Tensor, np.ndarray]
class AnimationPipeline(DiffusionPipeline):
_optional_components = []
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
| # *************************************************************************
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
# ytedance Inc..
# *************************************************************************
# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
TODO:
1. support multi-controlnet
2. [DONE] support DDIM inversion
3. support Prompt-to-prompt
"""
# from magicanimate.models.controlnet import ControlNetModel
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class AnimationPipelineOutput(BaseOutput):
videos: Union[torch.Tensor, np.ndarray]
class AnimationPipeline(DiffusionPipeline):
_optional_components = []
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer, | unet: UNet3DConditionModel, | 0 | 2023-12-15 01:22:37+00:00 | 16k |
daihaojun554/biliscrapy | biliscrapy/network/bilibili_danmu.py | [
{
"identifier": "bili_pb2",
"path": "biliscrapy/network/protobuf/bili_pb2.py",
"snippet": "DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\\n\\x08my.proto\\x12 bilibili.community.service.dm.v1\\\"d\\n\\x06\\x41vatar\\x12\\n\\n\\x02id\\x18\\x01 \\x01(\\t\\x12\\x0b\\n\\x03url\\x18\\x02 \\x01(... | import logging
import os
import json
import sys
import requests
from datetime import datetime
from .protobuf import bili_pb2 as Danmaku
from .bilibili_utils import bili_utils | 12,137 |
headers = {
'authority': 'message.bilibili.com',
'accept': 'application/json, text/plain, */*',
'accept-language': 'zh-CN,zh;q=0.9',
'cache-control': 'no-cache',
'origin': 'https://www.bilibili.com',
'pragma': 'no-cache',
'referer': 'https://www.bilibili.com/',
'sec-ch-ua': '"Chromium";v="118", "Google Chrome";v="118", "Not=A?Brand";v="99"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Windows"',
'sec-fetch-dest': 'empty',
'sec-fetch-mode': 'cors',
'sec-fetch-site': 'same-site',
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36',
}
# import bili_pb2 as Danmaku
class Danmu:
def __init__(self):
|
headers = {
'authority': 'message.bilibili.com',
'accept': 'application/json, text/plain, */*',
'accept-language': 'zh-CN,zh;q=0.9',
'cache-control': 'no-cache',
'origin': 'https://www.bilibili.com',
'pragma': 'no-cache',
'referer': 'https://www.bilibili.com/',
'sec-ch-ua': '"Chromium";v="118", "Google Chrome";v="118", "Not=A?Brand";v="99"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Windows"',
'sec-fetch-dest': 'empty',
'sec-fetch-mode': 'cors',
'sec-fetch-site': 'same-site',
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36',
}
# import bili_pb2 as Danmaku
class Danmu:
def __init__(self): | self.utils = bili_utils() | 1 | 2023-12-14 10:14:24+00:00 | 16k |
Subsets and Splits
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that have consistent code formatting levels across multiple scales (2k, 4k, 8k, 12k) and reveals the structured formatting patterns within these repositories.
SQL Console for tianyang/repobench_python_v1.1
Compares cross-file and in-file code structure patterns across different complexity levels, revealing how file organization strategies vary with code size and potentially informing better code architecture decisions.
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that have complete performance data across all seven code complexity levels, revealing consistent benchmarking patterns across different code sizes.
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that contain all 7 distinct quality levels (2k through 32k), revealing complete datasets that might be useful for comprehensive analysis.