Added a check to check the install of MS Deform Atten

app.py

@@ -49,6 +49,85 @@ class AppSteps(Enum):
 
 CONF_THRESH = 0.23
 
+@spaces.GPU
+def check_ms_deform_install():
+    from __future__ import absolute_import
+    from __future__ import print_function
+    from __future__ import division
+    
+    import time
+    import torch
+    import torch.nn as nn
+    from torch.autograd import gradcheck
+    
+    from functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch
+    
+    
+    N, M, D = 1, 2, 2
+    Lq, L, P = 2, 2, 2
+    shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()
+    level_start_index = torch.cat((shapes.new_zeros((1, )), shapes.prod(1).cumsum(0)[:-1]))
+    S = sum([(H*W).item() for H, W in shapes])
+    
+    
+    torch.manual_seed(3)
+
+
+    @torch.no_grad()
+    def check_forward_equal_with_pytorch_double():
+        value = torch.rand(N, S, M, D).cuda() * 0.01
+        sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
+        attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
+        attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
+        im2col_step = 2
+        output_pytorch = ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double()).detach().cpu()
+        output_cuda = MSDeformAttnFunction.apply(value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step).detach().cpu()
+        fwdok = torch.allclose(output_cuda, output_pytorch)
+        max_abs_err = (output_cuda - output_pytorch).abs().max()
+        max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()
+    
+        print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
+    
+    
+    @torch.no_grad()
+    def check_forward_equal_with_pytorch_float():
+        value = torch.rand(N, S, M, D).cuda() * 0.01
+        sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
+        attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
+        attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
+        im2col_step = 2
+        output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu()
+        output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu()
+        fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)
+        max_abs_err = (output_cuda - output_pytorch).abs().max()
+        max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()
+    
+        print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
+
+
+    def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True):
+    
+        value = torch.rand(N, S, M, channels).cuda() * 0.01
+        sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
+        attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
+        attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
+        im2col_step = 2
+        func = MSDeformAttnFunction.apply
+    
+        value.requires_grad = grad_value
+        sampling_locations.requires_grad = grad_sampling_loc
+        attention_weights.requires_grad = grad_attn_weight
+    
+        gradok = gradcheck(func, (value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step))
+    
+        print(f'* {gradok} check_gradient_numerical(D={channels})')
+    
+    check_forward_equal_with_pytorch_double()
+    check_forward_equal_with_pytorch_float()
+
+    for channels in [30, 32, 64, 71]:
+        check_gradient_numerical(channels, True, True, True)
+
 # MODEL:
 def get_args_parser():
     """
@@ -164,7 +243,7 @@ def build_model_and_transforms(args):
 
 parser = argparse.ArgumentParser("Counting Application", parents=[get_args_parser()])
 args = parser.parse_args()
-
+check_ms_deform_install()
 device = get_device()
 model, transform = build_model_and_transforms(args)
 model = model.to(device)