Merge pull request #31 from heavengate/fix_compile_prune

extract input variable from feed

model.py

@@ -360,10 +360,27 @@ class StaticGraphAdapter(object):
             metric_list, metric_splits = flatten_list(endpoints['metric'])
             fetch_list = endpoints['loss'] + metric_list
             num_loss = len(endpoints['loss'])
+
+        # if fetch Variable is same as input Variable, do not fetch
+        # from program, get it from input directly
+        pruned_fetch_list = []
+        pruned_fetch_idx_name_map = [""] * len(fetch_list)
+        for i, fetch_var in enumerate(fetch_list):
+            if fetch_var.name in feed.keys():
+                pruned_fetch_idx_name_map[i] = fetch_var.name
+            else:
+                pruned_fetch_list.append(fetch_var)
+
         rets = self._executor.run(compiled_prog,
                                   feed=feed,
-                                  fetch_list=fetch_list,
+                                  fetch_list=pruned_fetch_list,
                                   return_numpy=False)
+
+        # restore pruned fetch_list Variable from feeds
+        for i, name in enumerate(pruned_fetch_idx_name_map):
+            if len(name) > 0:
+                rets.insert(i, feed[name])
+
         # LoDTensor cannot be fetch as numpy directly
         rets = [np.array(v) for v in rets]
         if self.mode == 'test':

models/yolov3.py

@@ -138,7 +138,7 @@ class YOLOv3(Model):
                                 act='leaky_relu'))
                 self.route_blocks.append(route)
 
-    def forward(self, img_info, inputs):
+    def forward(self, img_id, img_shape, inputs):
         outputs = []
         boxes = []
         scores = []
@@ -163,8 +163,6 @@ class YOLOv3(Model):
                 for m in anchor_mask:
                     mask_anchors.append(self.anchors[2 * m])
                     mask_anchors.append(self.anchors[2 * m + 1])
-                img_shape = fluid.layers.slice(img_info, axes=[1], starts=[1], ends=[3])
-                img_id = fluid.layers.slice(img_info, axes=[1], starts=[0], ends=[1])
                 b, s = fluid.layers.yolo_box(
                     x=block_out,
                     img_size=img_shape,
@@ -181,7 +179,7 @@ class YOLOv3(Model):
         if self.model_mode == 'train':
             return outputs
 
-        preds = [img_id[0, :],
+        preds = [img_id,
                  fluid.layers.multiclass_nms(
                     bboxes=fluid.layers.concat(boxes, axis=1),
                     scores=fluid.layers.concat(scores, axis=2),

yolov3/coco.py

@@ -186,30 +186,31 @@ class COCODataset(Dataset):
             data = np.frombuffer(f.read(), dtype='uint8')
             im = cv2.imdecode(data, 1)
             im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
-        im_info = np.array([roidb['im_id'][0], roidb['h'], roidb['w']], dtype='int32')
+        im_id = roidb['im_id']
+        im_shape = np.array([roidb['h'], roidb['w']], dtype='int32')
         gt_bbox = roidb['gt_bbox']
         gt_class = roidb['gt_class']
         gt_score = roidb['gt_score']
-        return im_info, im, gt_bbox, gt_class, gt_score
+        return im_id, im_shape, im, gt_bbox, gt_class, gt_score
 
     def __getitem__(self, idx):
-        im_info, im, gt_bbox, gt_class, gt_score = self._getitem_by_index(idx)
+        im_id, im_shape, im, gt_bbox, gt_class, gt_score = self._getitem_by_index(idx)
 
         if self._mixup:
             mixup_idx = idx + np.random.randint(1, self.__len__())
             mixup_idx %= self.__len__()
-            _, mixup_im, mixup_bbox, mixup_class, _ = \
+            _, _, mixup_im, mixup_bbox, mixup_class, _ = \
                             self._getitem_by_index(mixup_idx)
             
-            im, gt_bbox, gt_class, gt_score = \
+            im_shape, im, gt_bbox, gt_class, gt_score = \
                     self._mixup_image(im, gt_bbox, gt_class, mixup_im,
                                       mixup_bbox, mixup_class)
 
         if self._transform:
-            im_info, im, gt_bbox, gt_class, gt_score = \
-                    self._transform(im_info, im, gt_bbox, gt_class, gt_score)
+            im_id, im_shape, im, gt_bbox, gt_class, gt_score = \
+                self._transform(im_id, im_shape, im, gt_bbox, gt_class, gt_score)
 
-        return [im_info, im, gt_bbox, gt_class, gt_score]
+        return [im_id, im_shape, im, gt_bbox, gt_class, gt_score]
 
     def _mixup_image(self, img1, bbox1, class1, img2, bbox2, class2):
         factor = np.random.beta(self._alpha, self._beta)
@@ -234,7 +235,9 @@ class COCODataset(Dataset):
         score2 = np.ones_like(class2, dtype="float32") * (1.0 - factor)
         gt_score = np.concatenate((score1, score2), axis=0)
 
-        return img, gt_bbox, gt_class, gt_score
+        im_shape = np.array([h, w], dtype='int32')
+
+        return im_shape, img, gt_bbox, gt_class, gt_score
     
     @property
     def mixup(self):

yolov3/main.py

@@ -63,7 +63,8 @@ def main():
     device = set_device(FLAGS.device)
     fluid.enable_dygraph(device) if FLAGS.dynamic else None
     
-    inputs = [Input([None, 3], 'int32', name='img_info'),
+    inputs = [Input([None, 1], 'int64', name='img_id'),
+              Input([None, 2], 'int32', name='img_shape'),
               Input([None, 3, None, None], 'float32', name='image')]
     labels = [Input([None, NUM_MAX_BOXES, 4], 'float32', name='gt_bbox'),
 	      Input([None, NUM_MAX_BOXES], 'int32', name='gt_label'),

yolov3/transforms.py

@@ -145,7 +145,7 @@ class ColorDistort(object):
         img += delta
         return img
 
-    def __call__(self, im_info, im, gt_bbox, gt_class, gt_score):
+    def __call__(self, im_id, im_shape, im, gt_bbox, gt_class, gt_score):
         if self.random_apply:
             distortions = np.random.permutation([
                 self.apply_brightness, self.apply_contrast,
@@ -153,7 +153,7 @@ class ColorDistort(object):
             ])
             for func in distortions:
                 im = func(im)
-            return [im_info, im, gt_bbox, gt_class, gt_score]
+            return [im_id, im_shape, im, gt_bbox, gt_class, gt_score]
 
         im = self.apply_brightness(im)
 
@@ -165,7 +165,7 @@ class ColorDistort(object):
             im = self.apply_saturation(im)
             im = self.apply_hue(im)
             im = self.apply_contrast(im)
-        return [im_info, im, gt_bbox, gt_class, gt_score]
+        return [im_id, im_shape, im, gt_bbox, gt_class, gt_score]
 
 
 class RandomExpand(object):
@@ -183,16 +183,16 @@ class RandomExpand(object):
         self.prob = prob
         self.fill_value = fill_value
 
-    def __call__(self, im_info, im, gt_bbox, gt_class, gt_score):
+    def __call__(self, im_id, im_shape, im, gt_bbox, gt_class, gt_score):
         if np.random.uniform(0., 1.) < self.prob:
-            return [im_info, im, gt_bbox, gt_class, gt_score]
+            return [im_id, im_shape, im, gt_bbox, gt_class, gt_score]
 
         height, width, _ = im.shape
         expand_ratio = np.random.uniform(1., self.ratio)
         h = int(height * expand_ratio)
         w = int(width * expand_ratio)
         if not h > height or not w > width:
-            return [im_info, im, gt_bbox, gt_class, gt_score]
+            return [im_id, im_shape, im, gt_bbox, gt_class, gt_score]
         y = np.random.randint(0, h - height)
         x = np.random.randint(0, w - width)
         canvas = np.ones((h, w, 3), dtype=np.uint8)
@@ -201,7 +201,7 @@ class RandomExpand(object):
 
         gt_bbox += np.array([x, y, x, y], dtype=np.float32)
 
-        return [im_info, canvas, gt_bbox, gt_class, gt_score]
+        return [im_id, im_shape, canvas, gt_bbox, gt_class, gt_score]
 
 
 class RandomCrop():
@@ -232,9 +232,9 @@ class RandomCrop():
         self.allow_no_crop = allow_no_crop
         self.cover_all_box = cover_all_box
 
-    def __call__(self, im_info, im, gt_bbox, gt_class, gt_score):
+    def __call__(self, im_id, im_shape, im, gt_bbox, gt_class, gt_score):
         if len(gt_bbox) == 0:
-            return [im_info, im, gt_bbox, gt_class, gt_score]
+            return [im_id, im_shape, im, gt_bbox, gt_class, gt_score]
 
         # NOTE Original method attempts to generate one candidate for each
         # threshold then randomly sample one from the resulting list.
@@ -251,7 +251,7 @@ class RandomCrop():
 
         for thresh in thresholds:
             if thresh == 'no_crop':
-                return [im_info, im, gt_bbox, gt_class, gt_score]
+                return [im_id, im_shape, im, gt_bbox, gt_class, gt_score]
 
             h, w, _ = im.shape
             found = False
@@ -286,9 +286,9 @@ class RandomCrop():
                 gt_bbox = np.take(cropped_box, valid_ids, axis=0)
                 gt_class = np.take(gt_class, valid_ids, axis=0)
                 gt_score = np.take(gt_score, valid_ids, axis=0)
-                return [im_info, im, gt_bbox, gt_class, gt_score]
+                return [im_id, im_shape, im, gt_bbox, gt_class, gt_score]
 
-        return [im_info, im, gt_bbox, gt_class, gt_score]
+        return [im_id, im_shape, im, gt_bbox, gt_class, gt_score]
 
     def _iou_matrix(self, a, b):
         tl_i = np.maximum(a[:, np.newaxis, :2], b[:, :2])
@@ -334,7 +334,7 @@ class RandomFlip():
                 isinstance(self.is_normalized, bool)):
             raise TypeError("{}: input type is invalid.".format(self))
 
-    def __call__(self, im_info, im, gt_bbox, gt_class, gt_score):
+    def __call__(self, im_id, im_shape, im, gt_bbox, gt_class, gt_score):
         """Filp the image and bounding box.
         Operators:
             1. Flip the image numpy.
@@ -363,20 +363,20 @@ class RandomFlip():
                     m = "{}: invalid box, x2 should be greater than x1".format(
                         self)
                     raise ValueError(m)
-        return [im_info, im, gt_bbox, gt_class, gt_score]
+        return [im_id, im_shape, im, gt_bbox, gt_class, gt_score]
 
 
 class NormalizeBox(object):
     """Transform the bounding box's coornidates to [0,1]."""
 
-    def __call__(self, im_info, im, gt_bbox, gt_class, gt_score):
+    def __call__(self, im_id, im_shape, im, gt_bbox, gt_class, gt_score):
         height, width, _ = im.shape
         for i in range(gt_bbox.shape[0]):
             gt_bbox[i][0] = gt_bbox[i][0] / width
             gt_bbox[i][1] = gt_bbox[i][1] / height
             gt_bbox[i][2] = gt_bbox[i][2] / width
             gt_bbox[i][3] = gt_bbox[i][3] / height
-        return [im_info, im, gt_bbox, gt_class, gt_score]
+        return [im_id, im_shape, im, gt_bbox, gt_class, gt_score]
 
 
 class PadBox(object):
@@ -388,7 +388,7 @@ class PadBox(object):
         """
         self.num_max_boxes = num_max_boxes
 
-    def __call__(self, im_info, im, gt_bbox, gt_class, gt_score):
+    def __call__(self, im_id, im_shape, im, gt_bbox, gt_class, gt_score):
         gt_num = min(self.num_max_boxes, len(gt_bbox))
         num_max = self.num_max_boxes
 
@@ -406,7 +406,7 @@ class PadBox(object):
         if gt_num > 0:
             pad_score[:gt_num] = gt_score[:gt_num, 0]
         gt_score = pad_score
-        return [im_info, im, gt_bbox, gt_class, gt_score]
+        return [im_id, im_shape, im, gt_bbox, gt_class, gt_score]
 
 
 class BboxXYXY2XYWH(object):
@@ -414,10 +414,10 @@ class BboxXYXY2XYWH(object):
     Convert bbox XYXY format to XYWH format.
     """
 
-    def __call__(self, im_info, im, gt_bbox, gt_class, gt_score):
+    def __call__(self, im_id, im_shape, im, gt_bbox, gt_class, gt_score):
         gt_bbox[:, 2:4] = gt_bbox[:, 2:4] - gt_bbox[:, :2]
         gt_bbox[:, :2] = gt_bbox[:, :2] + gt_bbox[:, 2:4] / 2.
-        return [im_info, im, gt_bbox, gt_class, gt_score]
+        return [im_id, im_shape, im, gt_bbox, gt_class, gt_score]
 
 
 class RandomShape(object):
@@ -450,13 +450,13 @@ class RandomShape(object):
         method = np.random.choice(self.interps) if self.random_inter \
             else cv2.INTER_NEAREST
         for i in range(len(samples)):
-            im = samples[i][1]
+            im = samples[i][2]
             h, w = im.shape[:2]
             scale_x = float(shape) / w
             scale_y = float(shape) / h
             im = cv2.resize(
                 im, None, None, fx=scale_x, fy=scale_y, interpolation=method)
-            samples[i][1] = im
+            samples[i][2] = im
         return samples
 
 
@@ -492,7 +492,7 @@ class NormalizeImage(object):
             3. (optional) permute channel
         """
         for i in range(len(samples)):
-            im = samples[i][1]
+            im = samples[i][2]
             im = im.astype(np.float32, copy=False)
             mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
             std = np.array(self.std)[np.newaxis, np.newaxis, :]
@@ -502,7 +502,7 @@ class NormalizeImage(object):
             im /= std
             if self.channel_first:
                 im = im.transpose((2, 0, 1))
-            samples[i][1] = im
+            samples[i][2] = im
         return samples
 
 
@@ -595,16 +595,15 @@ class ResizeImage(object):
                 format(type(target_size)))
         self.target_size = target_size
 
-    def __call__(self, im_info, im, gt_bbox, gt_class, gt_score):
+    def __call__(self, im_id, im_shape, im, gt_bbox, gt_class, gt_score):
         """ Resize the image numpy.
         """
         if not isinstance(im, np.ndarray):
             raise TypeError("{}: image type is not numpy.".format(self))
         if len(im.shape) != 3:
             raise ImageError('{}: image is not 3-dimensional.'.format(self))
-        im_shape = im.shape
-        im_scale_x = float(self.target_size) / float(im_shape[1])
-        im_scale_y = float(self.target_size) / float(im_shape[0])
+        im_scale_x = float(self.target_size) / float(im.shape[1])
+        im_scale_y = float(self.target_size) / float(im.shape[0])
         resize_w = self.target_size 
         resize_h = self.target_size 
 
@@ -616,5 +615,5 @@ class ResizeImage(object):
             fy=im_scale_y,
             interpolation=self.interp)
 
-        return [im_info, im, gt_bbox, gt_class, gt_score]
+        return [im_id, im_shape, im, gt_bbox, gt_class, gt_score]