Start implementing unsupervised train loop, add sfmlearner train and utils files for reference

unsupervised/third-party/train.py

@@ -0,0 +1,166 @@
+"""
+Trainer to learn depth information on unlabeled data (raw images/videos)
+
+Allows pluggable depth networks for differing performance (including fast-depth)
+"""
+
+import tensorflow as tf
+
+
+def compute_smooth_loss(self, pred_disp):
+    def gradient(pred):
+        D_dy = pred[:, 1:, :, :] - pred[:, :-1, :, :]
+        D_dx = pred[:, :, 1:, :] - pred[:, :, :-1, :]
+        return D_dx, D_dy
+    dx, dy = gradient(pred_disp)
+    dx2, dxdy = gradient(dx)
+    dydx, dy2 = gradient(dy)
+    return tf.reduce_mean(tf.abs(dx2)) + \
+        tf.reduce_mean(tf.abs(dxdy)) + \
+        tf.reduce_mean(tf.abs(dydx)) + \
+        tf.reduce_mean(tf.abs(dy2))
+
+
+def get_reference_explain_mask(self, downscaling):
+    opt = self.opt
+    tmp = np.array([0, 1])
+    ref_exp_mask = np.tile(tmp,
+                           (opt.batch_size,
+                            int(opt.img_height/(2**downscaling)),
+                            int(opt.img_width/(2**downscaling)),
+                            1))
+    ref_exp_mask = tf.constant(ref_exp_mask, dtype=tf.float32)
+    return ref_exp_mask
+
+
+def get_sfm_loss_fn(opt):
+    def sfm_loss_fn(y, y_pred):
+        # TODO: Correctly format a batch that is required for this loss function
+        pixel_loss = 0
+        exp_loss = 0
+        smooth_loss = 0
+        tgt_image_all = []
+        src_image_stack_all = []
+        proj_image_stack_all = []
+        proj_error_stack_all = []
+        exp_mask_stack_all = []
+        for s in range(opt.num_scales):
+            if opt.explain_reg_weight > 0:
+                # Construct a reference explainability mask (i.e. all
+                # pixels are explainable)
+                ref_exp_mask = get_reference_explain_mask(s)
+            # Scale the source and target images for computing loss at the
+            # according scale.
+            curr_tgt_image = tf.image.resize_area(tgt_image,
+                                                  [int(opt.img_height/(2**s)), int(opt.img_width/(2**s))])
+            curr_src_image_stack = tf.image.resize_area(src_image_stack,
+                                                        [int(opt.img_height/(2**s)), int(opt.img_width/(2**s))])
+
+            if opt.smooth_weight > 0:
+                smooth_loss += opt.smooth_weight/(2**s) * \
+                    compute_smooth_loss(pred_disp[s])
+
+            for i in range(opt.num_source):
+                # Inverse warp the source image to the target image frame
+                curr_proj_image = projective_inverse_warp(
+                    curr_src_image_stack[:, :, :, 3*i:3*(i+1)],
+                    tf.squeeze(pred_depth[s], axis=3),
+                    pred_poses[:, i, :],
+                    intrinsics[:, s, :, :])
+                curr_proj_error = tf.abs(curr_proj_image - curr_tgt_image)
+                # Cross-entropy loss as regularization for the
+                # explainability prediction
+                if opt.explain_reg_weight > 0:
+                    curr_exp_logits = tf.slice(pred_exp_logits[s],
+                                               [0, 0, 0, i*2],
+                                               [-1, -1, -1, 2])
+                    exp_loss += opt.explain_reg_weight * \
+                        self.compute_exp_reg_loss(curr_exp_logits,
+                                                  ref_exp_mask)
+                    curr_exp = tf.nn.softmax(curr_exp_logits)
+                # Photo-consistency loss weighted by explainability
+                if opt.explain_reg_weight > 0:
+                    pixel_loss += tf.reduce_mean(curr_proj_error *
+                                                 tf.expand_dims(curr_exp[:, :, :, 1], -1))
+                else:
+                    pixel_loss += tf.reduce_mean(curr_proj_error)
+                # Prepare images for tensorboard summaries
+                if i == 0:
+                    proj_image_stack = curr_proj_image
+                    proj_error_stack = curr_proj_error
+                    if opt.explain_reg_weight > 0:
+                        exp_mask_stack = tf.expand_dims(
+                            curr_exp[:, :, :, 1], -1)
+                else:
+                    proj_image_stack = tf.concat([proj_image_stack,
+                                                  curr_proj_image], axis=3)
+                    proj_error_stack = tf.concat([proj_error_stack,
+                                                  curr_proj_error], axis=3)
+                    if opt.explain_reg_weight > 0:
+                        exp_mask_stack = tf.concat([exp_mask_stack,
+                                                    tf.expand_dims(curr_exp[:, :, :, 1], -1)], axis=3)
+            tgt_image_all.append(curr_tgt_image)
+            src_image_stack_all.append(curr_src_image_stack)
+            proj_image_stack_all.append(proj_image_stack)
+            proj_error_stack_all.append(proj_error_stack)
+            if opt.explain_reg_weight > 0:
+                exp_mask_stack_all.append(exp_mask_stack)
+        total_loss = pixel_loss + smooth_loss + exp_loss
+        return total_loss
+        return sfm_loss_fn
+
+
+def photometric_reconstruction_loss(tgt_img, ref_imgs, intrinsics,
+                                    depth, explainability_mask, pose,
+                                    rotation_mode='euler', padding_mode='zeros'):
+    def one_scale(d, mask):
+        assert(mask is None or d.size()
+               [2:] == mask.size()[2:])
+        assert(pose.size(1) == len(ref_imgs))
+
+        reconstruction_loss = 0
+        b, _, h, w = d.size()
+        downscale = tgt_img.size(2)/h
+
+        tgt_img_scaled = F.interpolate(tgt_img, (h, w), mode='area')
+        ref_imgs_scaled = [F.interpolate(
+            ref_img, (h, w), mode='area') for ref_img in ref_imgs]
+        intrinsics_scaled = tf.concat(
+            (intrinsics[:, 0:2]/downscale, intrinsics[:, 2:]), dim=1)
+
+        warped_imgs = []
+        diff_maps = []
+
+        for i, ref_img in enumerate(ref_imgs_scaled):
+            current_pose = pose[:, i]
+
+            ref_img_warped, valid_points = inverse_warp(ref_img, depth[:, 0], current_pose,
+                                                        intrinsics_scaled,
+                                                        rotation_mode, padding_mode)
+            diff = (tgt_img_scaled - ref_img_warped) * \
+                valid_points.unsqueeze(1).float()
+
+            if explainability_mask is not None:
+                diff = diff * explainability_mask[:, i:i+1].expand_as(diff)
+
+            reconstruction_loss += diff.abs().mean()
+            assert((reconstruction_loss == reconstruction_loss).item() == 1)
+
+            warped_imgs.append(ref_img_warped[0])
+            diff_maps.append(diff[0])
+
+        return reconstruction_loss, warped_imgs, diff_maps
+
+    warped_results, diff_results = [], []
+    if type(explainability_mask) not in [tuple, list]:
+        explainability_mask = [explainability_mask]
+    if type(depth) not in [list, tuple]:
+        depth = [depth]
+
+    total_loss = 0
+    for d, mask in zip(depth, explainability_mask):
+        loss, warped, diff = one_scale(d, mask)
+        total_loss += loss
+        warped_results.append(warped)
+        diff_results.append(diff)
+    return total_loss, warped_results, diff_results