Added the inference exported model

.gitattributes

@@ -1,2 +1,3 @@
 *.pth filter=lfs diff=lfs merge=lfs -text
 example.jpg filter=lfs diff=lfs merge=lfs -text
+panda-model-1.pkl filter=lfs diff=lfs merge=lfs -text

panda-model-1.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:901163c9777e38a23c9d52409b74156bb9f741d5e33655ae8917b0c6e24b3e6b
+size 47089387

tools.py

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+from fastai.vision.all import *
+import tifffile
+
+imgdir = Path('/scratch/train_images')
+
+def get_crops(x):
+    tile_size = 250
+    if type(x) == PILImage:
+        img = np.array(x)
+    else:
+        tiff_file = imgdir/f'{x["image_id"]}.tiff'
+        img = tifffile.imread(tiff_file, key=1)
+    crop = np.array(img.shape) // tile_size * tile_size; crop
+    imgc = img[:crop[0],:crop[1]]
+    imgc = imgc.reshape(imgc.shape[0] // tile_size, tile_size, imgc.shape[1] // tile_size, tile_size, 3)
+    xs, ys = (imgc.mean(axis=1).mean(axis=2).mean(axis=-1) < 252).nonzero()
+    if len(xs) == 0:
+        xs, ys = (imgc.mean(axis=1).mean(axis=2).mean(axis=-1)).nonzero()
+#     if len(xs) < 2: print("no data in image:", x)
+    pidxs = random.choices(list(range(len(xs))), k=36)
+    return PILImage.create(imgc[xs[pidxs],:,ys[pidxs],:].reshape(6,6,tile_size,tile_size,3).transpose(0,2,1,3,4).reshape(6*tile_size,6*tile_size,3))
+#     return imgc.mean(axis=1).mean(axis=2).mean(axis=-1)
+
+def get_labels(x):
+    return np.arange(5) <= x['isup_grade']