fix dali crop size bug (#4011)

PaddleCV/image_classification/dali.py

@@ -26,19 +26,32 @@ from paddle import fluid
 
 
 class HybridTrainPipe(Pipeline):
-    def __init__(self, file_root, file_list, batch_size, resize_shorter,
-                 crop, min_area, lower, upper, interp, mean, std,
-                 device_id, shard_id=0, num_shards=1, random_shuffle=True,
-                 num_threads=4, seed=42):
-        super(HybridTrainPipe, self).__init__(batch_size,
-                                              num_threads,
-                                              device_id,
-                                              seed=seed)
-        self.input = ops.FileReader(file_root=file_root,
-                                    file_list=file_list,
-                                    shard_id=shard_id,
-                                    num_shards=num_shards,
-                                    random_shuffle=random_shuffle)
+    def __init__(self,
+                 file_root,
+                 file_list,
+                 batch_size,
+                 resize_shorter,
+                 crop,
+                 min_area,
+                 lower,
+                 upper,
+                 interp,
+                 mean,
+                 std,
+                 device_id,
+                 shard_id=0,
+                 num_shards=1,
+                 random_shuffle=True,
+                 num_threads=4,
+                 seed=42):
+        super(HybridTrainPipe, self).__init__(
+            batch_size, num_threads, device_id, seed=seed)
+        self.input = ops.FileReader(
+            file_root=file_root,
+            file_list=file_list,
+            shard_id=shard_id,
+            num_shards=num_shards,
+            random_shuffle=random_shuffle)
         # set internal nvJPEG buffers size to handle full-sized ImageNet images
         # without additional reallocations
         device_memory_padding = 211025920
@@ -51,10 +64,8 @@ class HybridTrainPipe(Pipeline):
             random_aspect_ratio=[lower, upper],
             random_area=[min_area, 1.0],
             num_attempts=100)
-        self.res = ops.Resize(device='gpu',
-                              resize_x=crop,
-                              resize_y=crop,
-                              interp_type=interp)
+        self.res = ops.Resize(
+            device='gpu', resize_x=crop, resize_y=crop, interp_type=interp)
         self.cmnp = ops.CropMirrorNormalize(
             device="gpu",
             output_dtype=types.FLOAT,
@@ -79,23 +90,32 @@ class HybridTrainPipe(Pipeline):
 
 
 class HybridValPipe(Pipeline):
-    def __init__(self, file_root, file_list, batch_size,
-                 resize_shorter, crop, interp, mean, std,
-                 device_id, shard_id=0, num_shards=1, random_shuffle=False,
-                 num_threads=4, seed=42):
-        super(HybridValPipe, self).__init__(batch_size,
-                                            num_threads,
-                                            device_id,
-                                            seed=seed)
-        self.input = ops.FileReader(file_root=file_root,
-                                    file_list=file_list,
-                                    shard_id=shard_id,
-                                    num_shards=num_shards,
-                                    random_shuffle=random_shuffle)
+    def __init__(self,
+                 file_root,
+                 file_list,
+                 batch_size,
+                 resize_shorter,
+                 crop,
+                 interp,
+                 mean,
+                 std,
+                 device_id,
+                 shard_id=0,
+                 num_shards=1,
+                 random_shuffle=False,
+                 num_threads=4,
+                 seed=42):
+        super(HybridValPipe, self).__init__(
+            batch_size, num_threads, device_id, seed=seed)
+        self.input = ops.FileReader(
+            file_root=file_root,
+            file_list=file_list,
+            shard_id=shard_id,
+            num_shards=num_shards,
+            random_shuffle=random_shuffle)
         self.decode = ops.ImageDecoder(device="mixed", output_type=types.RGB)
-        self.res = ops.Resize(device="gpu",
-                              resize_shorter=resize_shorter,
-                              interp_type=interp)
+        self.res = ops.Resize(
+            device="gpu", resize_shorter=resize_shorter, interp_type=interp)
         self.cmnp = ops.CropMirrorNormalize(
             device="gpu",
             output_dtype=types.FLOAT,
@@ -134,7 +154,7 @@ def build(settings, mode='train'):
 
     mean = [v * 255 for v in settings.image_mean]
     std = [v * 255 for v in settings.image_std]
-    crop = settings.crop_size
+    crop = settings.image_shape[1]
     resize_shorter = settings.resize_short_size
     min_area = settings.lower_scale
     lower = settings.lower_ratio
@@ -142,9 +162,9 @@ def build(settings, mode='train'):
 
     interp = settings.interpolation or 1  # default to linear
     interp_map = {
-        0: types.INTERP_NN,        # cv2.INTER_NEAREST
-        1: types.INTERP_LINEAR,    # cv2.INTER_LINEAR
-        2: types.INTERP_CUBIC,     # cv2.INTER_CUBIC
+        0: types.INTERP_NN,  # cv2.INTER_NEAREST
+        1: types.INTERP_LINEAR,  # cv2.INTER_LINEAR
+        2: types.INTERP_CUBIC,  # cv2.INTER_CUBIC
         4: types.INTERP_LANCZOS3,  # XXX use LANCZOS3 for cv2.INTER_LANCZOS4
     }
     assert interp in interp_map, "interpolation method not supported by DALI"
@@ -159,14 +179,23 @@ def build(settings, mode='train'):
         if not os.path.exists(file_list):
             file_list = None
             file_root = os.path.join(file_root, 'val')
-        pipe = HybridValPipe(file_root, file_list, batch_size,
-                             resize_shorter, crop, interp, mean, std,
-                             device_id=device_id)
+        pipe = HybridValPipe(
+            file_root,
+            file_list,
+            batch_size,
+            resize_shorter,
+            crop,
+            interp,
+            mean,
+            std,
+            device_id=device_id)
         pipe.build()
-        return DALIGenericIterator(pipe, ['feed_image', 'feed_label'],
-                                   size=len(pipe), dynamic_shape=True,
-                                   fill_last_batch=False,
-                                   last_batch_padded=True)
+        return DALIGenericIterator(
+            pipe, ['feed_image', 'feed_label'],
+            size=len(pipe),
+            dynamic_shape=True,
+            fill_last_batch=False,
+            last_batch_padded=True)
 
     file_list = os.path.join(file_root, 'train_list.txt')
     if not os.path.exists(file_list):
@@ -177,11 +206,22 @@ def build(settings, mode='train'):
         shard_id = int(env['PADDLE_TRAINER_ID'])
         num_shards = int(env['PADDLE_TRAINERS_NUM'])
         device_id = int(env['FLAGS_selected_gpus'])
-        pipe = HybridTrainPipe(file_root, file_list, batch_size,
-                               resize_shorter, crop, min_area,
-                               lower, upper, interp, mean, std,
-                               device_id, shard_id, num_shards,
-                               seed=42 + shard_id)
+        pipe = HybridTrainPipe(
+            file_root,
+            file_list,
+            batch_size,
+            resize_shorter,
+            crop,
+            min_area,
+            lower,
+            upper,
+            interp,
+            mean,
+            std,
+            device_id,
+            shard_id,
+            num_shards,
+            seed=42 + shard_id)
         pipe.build()
         pipelines = [pipe]
         sample_per_shard = len(pipe) // num_shards
@@ -194,10 +234,21 @@ def build(settings, mode='train'):
             place.set_place(p)
             device_id = place.gpu_device_id()
             pipe = HybridTrainPipe(
-                file_root, file_list, batch_size,
-                resize_shorter, crop, min_area,
-                lower, upper, interp, mean, std,
-                device_id, idx, num_shards, seed=42 + idx)
+                file_root,
+                file_list,
+                batch_size,
+                resize_shorter,
+                crop,
+                min_area,
+                lower,
+                upper,
+                interp,
+                mean,
+                std,
+                device_id,
+                idx,
+                num_shards,
+                seed=42 + idx)
             pipe.build()
             pipelines.append(pipe)
         sample_per_shard = len(pipelines[0])