MS Build casting error

references/detection/eval.py

@@ -0,0 +1,139 @@
+import numpy as np
+import torchvision
+import skimage.io
+import colorsys
+import matplotlib
+import random
+import matplotlib.patches as patches
+from matplotlib import pyplot as plt
+from skimage.measure import find_contours
+from PIL import Image
+from matplotlib.patches import Polygon
+
+# COCO Class names
+# Index of the class in the list is its ID. For example, to get ID of
+# the teddy bear class, use: class_names.index('teddy bear')
+class_names = ['BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
+               'bus', 'train', 'truck', 'boat', 'traffic light',
+               'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
+               'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',
+               'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
+               'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
+               'kite', 'baseball bat', 'baseball glove', 'skateboard',
+               'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
+               'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+               'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
+               'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
+               'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
+               'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',
+               'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
+               'teddy bear', 'hair drier', 'toothbrush']
+
+def apply_mask(image, mask, color, alpha=0.5):
+    """Apply the given mask to the image.
+    """
+    for c in range(3):
+        image[:, :, c] = np.where(mask >= 0.5,
+                                  image[:, :, c] *
+                                  (1 - alpha) + alpha * color[c] * 255,
+                                  image[:, :, c])
+    return image
+
+def random_colors(N, bright=True):
+    """
+    Generate random colors.
+    To get visually distinct colors, generate them in HSV space then
+    convert to RGB.
+    """
+    brightness = 1.0 if bright else 0.7
+    hsv = [(i / N, 1, brightness) for i in range(N)]
+    colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))
+    random.shuffle(colors)
+    return colors
+
+def display_instances(image, boxes, masks, class_ids, class_names,
+                      scores=None, title="",
+                      figsize=(16, 16), ax=None):
+    """
+    boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
+    masks: [height, width, num_instances]
+    class_ids: [num_instances]
+    class_names: list of class names of the dataset
+    scores: (optional) confidence scores for each box
+    figsize: (optional) the size of the image.
+    """
+    # Number of instances
+    N = boxes.shape[0]
+    if not N:
+        print("\n*** No instances to display *** \n")
+
+    if not ax:
+        _, ax = plt.subplots(1, figsize=figsize)
+
+    # Generate random colors
+    colors = random_colors(N)
+
+    # Show area outside image boundaries.
+    height, width = image.shape[:2]
+    ax.set_ylim(height + 10, -10)
+    ax.set_xlim(-10, width + 10)
+    ax.axis('off')
+    ax.set_title(title)
+
+    masked_image = image.astype(np.uint32).copy()
+    for i in range(N):
+        color = colors[i]
+
+        score = scores[i] if scores is not None else None
+        if score < 0.4:
+            continue
+
+        x1, y1, x2, y2,  = boxes[i]
+        p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
+                              alpha=0.7, linestyle="dashed",
+                              edgecolor=color, facecolor='none')
+        ax.add_patch(p)
+
+        # Label
+        class_id = class_ids[i]
+        label = class_names[class_id]
+        caption = "{} {:.3f}".format(label, score) if score else label
+        ax.text(x1, y1 + 8, caption,
+                color='w', size=11, backgroundcolor="none")
+
+        # Mask
+        mask = masks[i, :, :].detach().squeeze(-1)
+        masked_image = apply_mask(masked_image, mask, color)
+
+        # Mask Polygon
+        # Pad to ensure proper polygons for masks that touch image edges.
+        padded_mask = np.zeros(
+            (mask.shape[1] + 2, mask.shape[2] + 2), dtype=np.uint8)
+        padded_mask[1:-1, 1:-1] = mask
+        contours = find_contours(padded_mask, 0.5)
+        for verts in contours:
+            # Subtract the padding and flip (y, x) to (x, y)
+            verts = np.fliplr(verts) - 1
+            p = Polygon(verts, facecolor="none", edgecolor=color)
+            ax.add_patch(p)
+    ax.imshow(masked_image.astype(np.uint8))
+    plt.show()
+
+model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)
+# set it to evaluation mode, as the model behaves differently
+# during training and during evaluation
+model.eval()
+
+image = Image.open('test.jpg')
+image_tensor = torchvision.transforms.functional.to_tensor(image)
+
+output = model([image_tensor])
+
+img = skimage.io.imread('test.jpg')
+
+# Visualize results
+r = output[0]
+display_instances(img, r['boxes'], r['masks'], r['labels'],
+                            class_names, r['scores'])
+plt.show()
+

torchvision/csrc/cuda/ROIAlign_cuda.cu

@@ -326,7 +326,7 @@ at::Tensor ROIAlign_forward_cuda(
   auto output_size = num_rois * pooled_height * pooled_width * channels;
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  dim3 grid(std::min(at::cuda::ATenCeilDiv(output_size, 512L), 4096L));
+  dim3 grid(std::min(at::cuda::ATenCeilDiv(static_cast<int64_t>(output_size), static_cast<int64_t>(512)), static_cast<int64_t>(4096)));
   dim3 block(512);
 
   if (output.numel() == 0) {
@@ -379,7 +379,7 @@ at::Tensor ROIAlign_backward_cuda(
 
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  dim3 grid(std::min(at::cuda::ATenCeilDiv(grad.numel(), 512L), 4096L));
+  dim3 grid(std::min(at::cuda::ATenCeilDiv(static_cast<int64_t>(grad.numel()), static_cast<int64_t>(512)), static_cast<int64_t>(4096)));
   dim3 block(512);
 
   // handle possibly empty gradients

torchvision/csrc/cuda/ROIPool_cuda.cu

@@ -146,7 +146,7 @@ std::tuple<at::Tensor, at::Tensor> ROIPool_forward_cuda(
   auto output_size = num_rois * pooled_height * pooled_width * channels;
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  dim3 grid(std::min(at::cuda::ATenCeilDiv(output_size, 512L), 4096L));
+  dim3 grid(std::min(at::cuda::ATenCeilDiv(static_cast<int64_t>(output_size), static_cast<int64_t>(512)), static_cast<int64_t>(4096)));
   dim3 block(512);
 
   if (output.numel() == 0) {
@@ -204,7 +204,7 @@ at::Tensor ROIPool_backward_cuda(
 
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  dim3 grid(std::min(at::cuda::ATenCeilDiv(grad.numel(), 512L), 4096L));
+  dim3 grid(std::min(at::cuda::ATenCeilDiv(static_cast<int64_t>(grad.numel()), static_cast<int64_t>(512)), static_cast<int64_t>(4096)));
   dim3 block(512);
 
   // handle possibly empty gradients