From abe876183052e9da9c3d633e41386c5c1f4fc1e6 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期五, 08 九月 2017 05:51:42 +0800
Subject: [PATCH] Before adding refinement layer

---
 code/utils.py |   63 +++++++++++++++++--------------
 1 files changed, 34 insertions(+), 29 deletions(-)

diff --git a/code/utils.py b/code/utils.py
index 1e37045..5a18d7c 100644
--- a/code/utils.py
+++ b/code/utils.py
@@ -7,6 +7,40 @@
 import math
 from math import cos, sin
 
+def softmax_temperature(tensor, temperature):
+    result = torch.exp(tensor / temperature)
+    result = torch.div(result, torch.sum(result, 1).unsqueeze(1).expand_as(result))
+    return result
+
+def get_pose_params_from_mat(mat_path):
+    # This functions gets the pose parameters from the .mat
+    # Annotations that come with the Pose_300W_LP dataset.
+    mat = sio.loadmat(mat_path)
+    # [pitch yaw roll tdx tdy tdz scale_factor]
+    pre_pose_params = mat['Pose_Para'][0]
+    # Get [pitch, yaw, roll, tdx, tdy]
+    pose_params = pre_pose_params[:5]
+    return pose_params
+
+def get_ypr_from_mat(mat_path):
+    # Get yaw, pitch, roll from .mat annotation.
+    # They are in radians
+    mat = sio.loadmat(mat_path)
+    # [pitch yaw roll tdx tdy tdz scale_factor]
+    pre_pose_params = mat['Pose_Para'][0]
+    # Get [pitch, yaw, roll]
+    pose_params = pre_pose_params[:3]
+    return pose_params
+
+def get_pt2d_from_mat(mat_path):
+    # Get 2D landmarks
+    mat = sio.loadmat(mat_path)
+    pt2d = mat['pt2d']
+    return pt2d
+
+def mse_loss(input, target):
+    return torch.sum(torch.abs(input.data - target.data) ** 2)
+
 def plot_pose_cube(img, yaw, pitch, roll, tdx=None, tdy=None, size=150.):
     # Input is a cv2 image
     # pose_params: (pitch, yaw, roll, tdx, tdy)
@@ -49,32 +83,3 @@
     cv2.line(img, (int(x3), int(y3)), (int(x3+x2-face_x),int(y3+y2-face_y)),(0,255,0),2)
 
     return img
-
-def get_pose_params_from_mat(mat_path):
-    # This functions gets the pose parameters from the .mat
-    # Annotations that come with the 300W_LP dataset.
-    mat = sio.loadmat(mat_path)
-    # [pitch yaw roll tdx tdy tdz scale_factor]
-    pre_pose_params = mat['Pose_Para'][0]
-    # Get [pitch, yaw, roll, tdx, tdy]
-    pose_params = pre_pose_params[:5]
-    return pose_params
-
-def get_ypr_from_mat(mat_path):
-    # Get yaw, pitch, roll from .mat annotation.
-    # They are in radians
-    mat = sio.loadmat(mat_path)
-    # [pitch yaw roll tdx tdy tdz scale_factor]
-    pre_pose_params = mat['Pose_Para'][0]
-    # Get [pitch, yaw, roll]
-    pose_params = pre_pose_params[:3]
-    return pose_params
-
-def get_pt2d_from_mat(mat_path):
-    # Get 2D landmarks
-    mat = sio.loadmat(mat_path)
-    pt2d = mat['pt2d']
-    return pt2d
-
-def mse_loss(input, target):
-    return torch.sum(torch.abs(input.data - target.data) ** 2)

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